A.I. as opposed to what?

One popular use of A.I. - specifically chatbots using LLMS - that I did not foresee is as a source of life advice. Sometimes, people refer to this in the context of therapy, as if the user is using ChatGPT or another A.I. as a substitute therapist. That implies a certain level of intimacy and privacy that probably applies to a minority of the many advice-seeking queries users offer every day. 

When thinking about the effects of anything, we often search for comparisons, and the comparisons we make are often shaped by metaphors we use - I think that's what is happening with how we think about the effects of using A.I. for advice-seeking. Instead of choosing a comparison that comes to mind the quickest or perhaps one that suits our existing biases for or against the phenomenon in question (e.g., how do the answers given by ChatGPT compare to those given by a professional therapist), we might choose our comparisons in a more deliberate way. Instead of starting with the connections our mind makes when learning of a few vivid exemplars, we might first define the field of inquiry - what is the behavior we're interested in, how many folks are engaging in it, and how can I observe a representative sample of it? 

The next step is to consider the behavior in the context of people's lives. Here, the choice of comparison is not what comes to mind for us, but our reasonable guess as to what people engaging in the behavior would have done otherwise. If they didn't have access to ChatGPT, who or what would they have gone to for advice (or would they have gone to anyone or anything for advice at all)? 

Therapists strike me as sources of very good advice on many topics. They're also better than self-help books because they can tailor their advice to you as an individual and they engage in a back-and-forth exchange. There's also some intangible humanity to your connection with a therapist that clearly helps. On the down side, it is hard to access therapy. There are only so many professionally trained therapists to go around. Efforts to make them more accessible by moving therapy online sacrifices some benefits of the therapeutic experience, namely the intangible human connection, in addition to leading to burnout from overbooked therapists. Even a society that is fully committed to serving the mental and emotional needs of its citizens runs up against the limits of therapeutic supply. 

Of course, people have turned to many other sources for life advice, including friends, family, clergy, authors, and artists. You may not approach a movie, TV show, or a novel with an advice-seeking intension, and yet its lessons may be your guide through any number of emotional or existential straits.

Then there are sources of advice that we encounter online: in spaces explicitly marked as such (and which derive their format and logic from newspaper advice columns) but also on YouTube, TikTok, and podcasts. Again, these life lessons may not be something we sought out, but they may still inform how we navigate challenging times in our lives. 

As I consider whether its a good idea for people to turn to ChatGPT for advice, I find that the most apt comparison - that is, the most plausible source of advice for most of the people asking ChatGPT for advice - is either googling it, searching YouTube or TikTok, or running across it on YouTube or TikTok via an algorithm. The quality of advice coming from these sources is, well, mixed. In many cases, the answers aren't as high quality as one would get from a professional, but its much more accessible, which is why its used more often. In some cases - googling something that is commonly googled and finding results from trusted sources - the quality is similar to that which you'd get from a professional. 

Are the types of life advice questions users ask ChatGPT more like the impersonal kind that people have, for the past 20 years, tended to google? Or are they more like the personal questions that people turned to friends, family, and - if they have access - therapists for? My sense is that a fair amount of young people have been getting that kind of advice from YouTube, TikTok, and other social media platforms for the past decade. In that sense, it is an appropriate point of comparison for that type of advice.

I'd imagine advice of this sort given by ChatGPT would be pretty homogeneous when compared to what you would find on social media platforms. The latter would vary greatly in values it reflected, the perspectives and experiences from which it drew. It leaves the existing variety of humanity intact, while ChatGPT probably flattens it. 

I guess I imagine cases in which people get bad life advice - "bad" in the sense that they result in some harm to themselves and/or others - being more numerous on social media platforms than on ChatGPT. I also imagine that the flat homogeneity of its answers won't necessarily spread to other aspects of culture more broadly, as some suspect it will. ChatGPT tends to qualify its advice and allow for some degree of variety and nuance in its answers. By merely asserting that there is some variety and nuance to certain life advice questions, ChatGPT would be contradicting the values of some of its users. This is less likely to happen on social media where answers are sorted by algorithm to confirm the existing biases of users. 

And so that would seem to be the trade-off: leave the echo chambers of social media advice intact - some of which generate harmful outcomes - or replace them with answers that guide most users away from harmful outcomes but homogenize...something. But is it really Culture that's being homogenized if people are encouraged to cope with crisis in particular ways? This is where my speculation reaches its limit and I feel the need for a more systematic examination of the life advice questions people ask ChatGPT and the answers they receive. 

What's a podcast?

Here's a number: more than 1 billion active monthly YouTube users view podcast content of some kind on the platform. 

Aside from being another data point indicating that more and more people are getting information and entertainment through podcasts, this fact also raises the question of whether the term "podcast" brings to mind something you listen to or something you watch. Podcasts - as audio-only content - have been around for 20 years, deriving their name from a particular Apple device for listening to music and other audio content. Podcasts, as a category, were fairly niche for most of that time - the vast majority of Americans, let alone people around the world, did not listen to any podcasts. Starting in 2013, they start to trend upwards, though not at the hyperbolic or exponential rate of, say, adoption of social media platforms like Instagram or TikTok. Gradually, podcasts became part of the average American's information diet. But like social media, video streaming platforms, and every other high-choice information medium of the 21st century, podcasts offered seemingly infinite variety in terms of topics and perspectives. A few superstars dominated the podcast charts, but the distribution tail was long and listenership fragmented - most listeners never listened to the most popular podcasts. 

During the pandemic, video streaming took off, owing its popularity to the prolonged shutdown of traditional media production. It's funny/sad to look back to the early stages of the pandemic at TV producers' attempts to approximate the experiences TV viewers were used to. The dissimilarities between television (particularly live TV) and this pale imitation were striking. This moment also democratized video production in the sense that the production tools - lights, camera, set design - used by TV talk show hosts were ones readily available to regular folks. It familiarized more audiences with low/no-budget talk show production. As long as you were charismatic, good-looking, and had access to interview other charismatic, good-looking people, who cared what your background looked like, whether you were on a broadcast network, or whether or not you had a studio audience? 

I recently asked a student whether she listens to any podcasts, and after saying that she didn't, she mentioned that she was recently on a podcast (which made me wonder: what would it mean if more people were either guests or hosts of podcasts than actually listened to podcasts? Might the same have been true for blogs during their initial heyday?). At least she thought it was a podcast - she wasn't sure, because it was on YouTube and it was something you watched, not something you listened to. This should give any podcast researcher pause - make sure you understand how your study participants define the term "podcast."

I'd noticed that some of the podcasts I'd listened to on Spotify had added a video component. Of course, traditional news sources like The New York Times had been pushing reluctant writers in front of video cameras for years, since the early days of online video. The visual aspect didn't add much - it wasn't as if they edited together b-roll and interview footage, which would have meant recreating the traditional television news format on a different medium. It was just people talking - either to each other or to us. It seems to have been the rise of TikTok, Twitch streamers, and YouTube-as-first-screen among Gen-Z folks that prompted the more widespread integration of video into formerly audio or text content. Video was just a means by which to reach the large audiences already on platforms like YouTube and TikTok, not a means of adding much in terms of aesthetics or meaning.

During the pandemic boom in video streaming and podcasting, I recall realizing the importance of pre-production and editing (not live editing, as is used in sports broadcasts and other live events, but post-production editing) in the context of these shows. Those were the key separators between live streamers and podcasters, the qualities that made the former feel like an attempt to connect and converse with the audience and the latter feel like an attempt to entertain them, to put on a show. Then there were scripted podcasts like Serial or S-Town which resembled - in their production scale and in their feel - documentaries. Scripted non-fiction video podcasts - perhaps too short and informal to be called documentaries - are common, and as with their audio-only counterparts, they require more time to create than simple streaming content. Somewhere in between, you had podcasts like Freakonomics that edited scripted segments together with interview clips, something more like long-form journalism. 

But rather than written journalism, it's radio that is the obvious antecedent to podcasts. How different is the streamer directly addressing his audience for hours on end from Rush Limbaugh? How different is the group of friends joking around about news, entertainment, and one another's private lives from Howard Stern? Television talk shows - daytime and late-night - are another template. How different is an intimate podcast interview with a celebrity from Oprah? And how different might their effects be on individuals or societies?

Though there are plenty of similarities between talk radio, TV talk shows, and podcasts - enough to make scholarship on those cultural forms a must-read for anyone interested in the effects of podcasts - I can think of two important differences. The first concerns the number of podcasts - many more than were available through local or syndicated radio, through broadcast or cable TV. A lower barrier to entry means more entrants to the marketplace, and so the market for podcasts - in its scale and dynamics - is closer to the market for music: millions of creators, a few superstars, a really long tail. There might be less audience churn than with music, as audiences develop a para-social connection to podcasters and podcasts don't seem as evanescent as musical trends. 

As with any long-tail marketplace, it's a mistake to only study the distribution head (and I feel like a broken record for saying this again, but...) most listeners are not listening to whatever is trending. They're fragmented across the long tail. It's possible (even likely, I'd argue) that the thousands of niches served by millions of podcasts resemble niches served by magazines in the 20th century. Most of the niches and, collectively, most of the podcast audience consist of benign special interest content (think model train podcasts). But there's no way to know until we analyze the content along the long tail.

The size and diffuseness of the podcast market means that no single podcaster has the kind of reach that broadcasters had in the 20th century. Joe Rogan has something like 11 million listeners to each podcast while Howard Stern and Rush Limbaugh, in their heyday, had 20 million each, and that was at a time when the U.S. population was 2/3 the size, and a decent chunk of Rogan's listeners are from outside the U.S. It's worth dwelling on this point because it applies to many podcasters, streamers, and influencers. On the one hand, they have a global reach that broadcasters in the era of nationalized media markets could only dream of. But if we're trying to discern how much attention share they have (and I'd argue that attention share is a precondition to having influence), then you should be using the global population as your denominator, not the national population. In that case, no podcaster has more than 1% of the population's attention, and nearly all of them have less than .001% of it.

The second difference is regulation, or lack thereof. Howard Stern frequently butted heads with the FCC, and Rush Limbaugh had to placate the kinds of sponsors that would have likely steered clear of some of today's most popular podcasters. In the absence of FCC content regulation, edgy talk show hosts become edgier podcasters, moving their small-but-significant audiences closer to the fringes of society. Platforms regulate their content, too - creators can still get de-platformed - but it takes a lot more to get de-platformed than it took to get fined by the FCC. Meanwhile, provocateurs expand the Overton window.

But Rogan, like Stern (but perhaps not like Limbaugh), has to maintain some variety in order to maintain his large audience. Their critics tend to focus on particularly outlandish, offensive, or dangerous moments on their shows, but a quick survey of their output suggests that its not all like that, that they attempt to cater to multiple audiences. Don't like what you're hearing? Skip that episode and tune back in later. If we care about influence, we need to take into account how content varies by episode - not an impossible task so long as researchers can analyze podcast transcripts and look for keywords. 

Just as talk shows were spaces for discussion and commentary relating to social issues, political issues, and current events, so too are podcasts. Terms like "infotainment" and "soft news" can be applied to the category, though some podcasts - in format, production, and reception - are more clearly in the tradition of documentary filmmaking or journalism, and that's the genre of podcast that excites me the most. Documentaries continue to find audiences on streaming platforms, and there is no one type of documentary. It's a vibrant cultural form. Meanwhile, journalism continues its long quest for a business model to replace the one that produced a golden era of civic awareness that is swiftly receding into the past. There are a lot of people in the podcast "space" right now - audiences, creators, investors, platforms. But the medium hasn't reached maturity; it hasn't found its Orson Welles or its Dorothea Lange. For these reasons, if I were to place a bet on a means by which to tell a non-fiction story that had real impact, I'd bet on the podcast. 

What did Americans watch on TikTok?

It's a seemingly simple question with a seemingly obvious answer. If you've read anything about TikTok, you probably assume that most Americans are watching influencers who talk about politics, fashion, weight loss, and a broad, amorphous category of behaviors called "trends." Those who write about TikTok - not just journalists but academic researchers - tend to write about what they're interested in, what has the biggest impact on society or the economy, or what is trending. What is trending on any large platform like YouTube or TikTok represents less than 5% of what users are watching, so it's not a good starting point for answering this question. 

Instead, it's better to start with a representative sample of Americans and ask them to report URLS of the last ten TikTok videos they watched, which is what we did in April of 2024. Why ten? People tend to get tired of copying and pasting URLs after that, and we believe this allows us a view of most Americans' TikTok viewing habits that other approaches do not offer. 

After collecting roughly 3,000 URLs from roughly 300 Americans, we watched each video and categorized it based on its topic. Topics ranged from music and dance to true crime to news, politics, and social issues. Recognizing that many videos were "about" multiple things (a comedy skit that was about animals and relationships), we assigned as many topic labels to these videos as was warranted. We then calculated the share of videos in our sample that belonged to each topic category. Here is what we found.

It is hard to characterize Americans' TikTok viewing in terms of topic. It may be tempting to look at the highest bars in the chart and talk about how TikTok is mostly a light-hearted platform where Americans watched comedy and dance videos, but those two categories, cumulatively, account for less than half of the videos in our sample. Another way to describe the viewing is "fragmented," which is misleading and oversimplifying in its own way. To get a better understanding of what types of content Americans watched on TikTok, it helps to have a point of comparison. 

We also asked our representative sample of Americans to provide the URLs of the last ten YouTube videos they watched. Here is a side-by-side comparison of the two platforms. 

Relative to YouTube, TikTok was used by Americans to view more comedy content and content about relationships while YouTube was used more for educational videos (e.g., tutorials) and clips from movies and TV shows. But the diversity of topics is broad on both platforms: not only can you find content on a wide variety of topics on both platforms; Americans tended to watch a variety of content. Based on this admittedly rudimentary analysis, it's best to think of cross-platform differences in Americans' short video diets (and perhaps, by extension, the identities and purposes of the platforms) as tendencies rather than in absolute terms. 

What do we mean by "Influencer?"

One of the most fruitful panels I attended at this year's International Communication Association conference concerned the definition of "influencer." The term has always rubbed me the wrong way, always sounded like self-serving marketer-speak that misleads us about how influential anyone is online. As Crystal Abidin of Curtin University pointed out during the panel, the term is deployed strategically. If calling yourself an influencer makes you money and avoids the kind of regulation to which professional media producers are subject, then that's what you'll call yourself. 

What are the alternatives? "Content producer" is certainly less aggrandizing, but it also makes no claim with regard to reach, engagement, or any other metric of success. Even if only a few people watch my videos, I'm still a content producer. The same could be said of platform-specific or media-specific monikers like YouTuber, TikTokker, podcaster, streamer, or vlogger/blogger. There needed to be some term that differentiated online content producers with a certain level of success - however one wanted to define and measure success - from those without it. 

For a time, that term was "micro-celebrity," a name as diminishing (who wants to be thought of as a "micro" anything?) as "influencer" is aggrandizing. Aside from their opposite associations, differences in name matter for scholars, journalists, or interested members of the public seeking out research on this phenomenon. Search the databases for articles, chapters, and books on "influencers" and you'll be missing some of the most important work on the topic, like Alice Marwick's work on successful YouTubers - highly applicable to the successful TikTokkers we now refer to as "influencers." So, that was one takeaway: if you're interested in influencers, start with the scholarly work done on micro-celebrity. 

Number of followers = Influencer?

The European Commission defines Influencers as "content creators who often advertise or sell products on a regular basis." While this jibes with most people's conception of Influencers, it doesn't seem inclusive enough. It rules out anyone with a large audience who makes money through Patreon or pre-roll ads (which, I would think, is the platform advertising rather than the content creator, but the creator still gets a cut of the ad revenue) instead of overt paid promotion. Such a user might be highly influential - raising awareness of a cause or an approach to investing or a political candidate - but would not meet the strict legal definition set forth by the European Commission.

Most of the research on Influencers, both qualitative and quantitative, points to some metric - usually the user's number of followers - as evidence that they are, in fact, an influencer. As best I can tell, the cut-off for Influencer status is arbitrary and/or based on round numbers chosen by marketing professionals. The bare minimum seems to be 1,000 followers, qualifying for the lowest tier of influencer - the "nano-influencer."

In addition to metrics, there seems to be an aesthetic component to the accepted definition of Influencer. Accounts referred to as Influencers tend to feature individuals or, less frequently, duos or families, and they typically appear in all or most of their videos, typically directly addressing the camera/audience. A popular (and, perhaps, highly influential) account that compiled thematically related clips would not fit this aesthetic description nor would an account run by a group of comedians. And yet sometimes, such accounts are referred to as influencers. 

Are Influencers Actually Influential?

Why does it matter whom we call influencers? What are we assuming about them when we take them as a starting point to studying or reporting on some social phenomenon?

All members of the ICA panel seemed skeptical that everyone, or even most, influencers are especially influential, and skeptical of the direct relationship - often assumed by journalists, users, and even researchers - between follower count and influence. Marketers would tell you that "reach" is an important aspect of influence, but not the only aspect. To be influential, it helps to be well liked. And sure, the act of following an account implies affinity, but how many of those who follow an account see every post, "like" every post, and have their beliefs, attitudes, or behavior influenced by every post? A subset of followers actually see any given post, and of that, a smaller subset might be persuadable, depending on their mood and the proximity between their current beliefs and attitudes and those of the influencer.

Most press articles and more than a few academic articles and books about influencers don't make the distinction between reach and influence. At best, these models of influence lack nuance but are still fundamentally sound. All things being equal, a content creator with 1,000,000 followers is probably more influential than one with 10,000 followers. This kind of relative judgment of influence is a logic that underpins the entire advertising industry; an industry, it should be noted, that gave rise to the term "influencer." How effective is a given ad at persuading people to purchase a product? Surprisingly, it's still difficult to tell. 

Part of the reason it's difficult to tell is that companies creating and selling ads don't have much incentive to prove the magnitude of their efficacy. All they have to prove to companies seeking to promote their goods and services through advertising is the relative advantage in the marketplace compared to their competition. As long as they are more visible to potential consumers than the competition, they're more likely to sell more products or services than the competition. 

But for those of us who care less about selling one particular set of products or services and care more about influence in general (who to vote for, whether or not to get vaccinated, changing one's beliefs about capitalism or gender equality, etc.), the magnitude of influences matter. Simply equating exposure with influence puts us back at the hypodermic needle model of media effects - everyone who is exposed to a media message reacts to it the same way. That theory was debunked (or at least modified) over 60 years ago by the Limited Media Effects paradigm.

Some might argue that its different this time around. Influencers are more influential than standard mediated promotion because audiences/followers feel as though they have a relationship with the influencer (i.e. a parasocial relationship), and because influencers are perceived to be more authentic than celebrities. At times, they actually interact with audience members through comments or replies. We know that one's peers can have an influence many times greater than that of impersonal mass promotion campaigns, and the relationship between influencers and their followers is thought to be like those of peers. For the relatively few followers who repeatedly comment and receive replies from influencers (i.e., interact with them repeatedly), this seems plausible. For the majority (90-95% of followers, I'd guess) who don't, it seems more like the relationship between a talk show host and their audience - more personal (and thus more influential) than the relationship between a fictional character and an audience or mass advertising and an audience, but less than an actual close friend. Might influencers be more influential than impersonal TV advertising? Sure, but that's a pretty low bar. 

It seems most likely to me that influencers are highly influential under certain conditions. They're probably good at directing attention to people, places, or things that have yet to attract much attention. They can take an unknown product, social cause, political candidate, or location and make it enormously popular (or enormously hated) overnight. It strikes me as far less likely that influencers can get people to change their minds about a person, place, or thing once their audience already know about it and have formed an opinion about it. This is nothing new: that's how persuasion works - easy when people don't have an awareness or opinion of something or someone, difficult when they already do. Any marketer, campaign manager, or media effects researcher from 60 years ago could have told you that. So, a more accurate moniker for influencers might be "attention directors."

The Real Influencers

Consider this alternative: perhaps the people who "like," share, repeatedly view, or discuss content online have the greatest amount of influence in our current mediascape. This largely anonymous, highly engaged group is far smaller than the general public, and in most cases smaller than the audience, which includes casual, less engaged people. The influence of the highly engaged group over the influencer is subtle, but worth thinking about. 

It might help to take the perspective of someone who sets out to be an influencer. They have something they want to say, some "self" they want to express. They express it...and get very little attention from audiences. Disappointed, they take a quick scan of the most popular influencers overall and in their particular domain (say, gaming, sports, or political commentary). They get a sense of the things those people say or do that make them popular - aesthetic choices like editing pace and clarity of message, but also ideological choices, embodiments of personality, tone, language, sense of humor, etc. They begin to adopt some of these, reluctant to wander too far away from their original expression of self, both because it makes them uncomfortable and because they worry about being perceived as inauthentic. They get a bit of positive feedback - more likes, more comments, more attention - and so they keep doing it. 

The collective influence of the highly engaged audience over the influencer might be even subtler than that. Maybe the influencer watched thousands of videos while growing up, simply seeking what most audiences seek - entertainment. Through that process, they absorbed online norms, gradually developing an intuition about what differentiates popular content on a given topic and within a given online subculture from less popular content within that context. At some point, they decide to try their hand at creating content, but what it occurs to them to create - the range of expressive possibilities - is inevitably shaped by what they've already watched. They're not consciously trying to mimic the existing content that effectively caters to audiences' preferences and values, but are apt to cater to them nonetheless. That influencer becomes popular - they have hundreds of thousands of subscribers and millions of views. But what they say and how they say it must conform to the preferences and values of the highly engaged audience, or else it wouldn't become popular in the first place. 

Again, this is nothing new. Novelists, TV writers, and screenwriters know that in order to reach a large enough audience to make for a sustainable career in a capitalist marketplace, you need to - in some sense - cater to a highly influential subset of that audience (critics, opinion leaders, execs). It's crass to think that way - most creators want to see their work as pure acts of self-expression and creativity uninfluenced by the marketplace. And this is not to say that there isn't any original creativity on the part of the artist or content creator. In fact, all audiences demand it: if you simply serve them up something that already existed, they wouldn't bite. There has to be some element of novelty to it, some spark of originality, individuality, and authenticity...but it also needs to fit within a set of generic conventions, and those conventions are articulated through the habits of audiences or some highly engaged subset of the audiences. In the box office business model of media, one person's dollar is as good as another's. In the ad-supported model, certain demographics are more valued than others. And in the online attention economy, the highly engaged audience determines the downstream visibility of content, and is thus more valuable and more influential.

Reasons to be Skeptical

A lot of people want to believe that influencers (or, more broadly, any content on TikTok, Instagram, YouTube, or X/Twitter) are influential. The content creators themselves (obviously) want to believe that they have influence, and so do many researchers and journalists. It doesn't have to be so, but I think there is a bias among many who study and write about influencers to find evidence of significant influence, to protect their work against accusations of triviality, something that scholars of popular culture have dealt with for decades. My sense is that young researchers are drawn to study social media because they believe that it matters, that it is influential, so there's a bit of self-selection bias - they enter the arena looking for evidence of influence. Social media platforms want to highlight such evidence because it makes their companies more valuable, but also (somewhat less cynically) more important; their work matters. Governments, parents, and pretty much everyone else stand to benefit by ascribing blame for every social ill on social media and, by extension, influencers. There is little downside (at least for the blamers) to blaming greedy billionaires, narcissistic influencers, and opaque algorithms for social ills. Its certainly easier than fixing other long-entrenched causes of systemic inequities or one's own personal issues, or simply accepting that humans were never designed to optimize social harmony or happiness. 

That's not to say we shouldn't strive for greater social harmony and happiness; only that we should avoid seeking comfort in scapegoats. How do we know when influencers, social media platforms, or algorithms are merely scapegoats and not genuine threats? This is the hard work of good media research.

All the Media Content We Cannot See

Like the majority of the electromagnetic spectrum, most of any given high-choice media landscape - be it YouTube, TikTok, or even Netflix - is difficult to see without some kind of aid, and thus easy to forget about. One might argue that the most important stuff - the content that has the most influence on individuals and society, i.e., the popular stuff - is easily visible through Top Ten or "trending" lists on the platform itself or through articles, podcasts, and conversations of cultural critics. But how much of the entire spectrum of content - or, if you take a human-centered approach to the question, viewing hours - are we observing when we talk about this tall head of the distribution tail?

The answer has implications for how we conceive of the culture we live in. Often, we assume we can get a pretty good sense of a culture by observing what media content it chooses to spend its time with. The topics, values, and aesthetics of popular content have long been thought to reflect and/or shape the preoccupations of the culture. This was all easy enough during the era of mass media when choice was limited, although even then it oversimplified the character of a culture. We look back on the late 1960's in America and think of psychedelia and unrest, but plenty of folks living in that place and time were likely oblivious to such trends. Still, it seems safe to say that you could get at least some idea of what most people living in a certain place and time were thinking and feeling by examining its popular media content. 

It's a commonplace that the number of choices for media content has exploded in the past decade or two. Truly understanding how content relates to culture - or trying to derive a sense of culture by examining content - has become trickier. In the era of broadcast TV, it wouldn't take much time for anyone to watch episodes of the 10 most popular TV shows. Out of the total number of viewing hours in a given culture, that might get you, say, 50% of them. The other 50% of the viewing hours would be distributed across less popular programming, so you could make a decent claim to "knowing" a culture by examining 10 popular TV shows. What would a similar approach get you now, if applied to Netflix?

According to recently released data from Netflix, viewers watched a total of roughly 90 billion hours in the first half of 2023. Of those hours, the top ten shows accounted for 4.9 billion - or roughly 5% of the total. Watching episodes of these ten shows, then, wouldn't be a very good way to get an idea of what Netflix viewers, generally, were watching (or, by extension, what they thought or how they felt about anything). It may be that the shows are in some way representative of the larger whole - in terms of their genre, topic, tone, aesthetic, values, etc. - but given the relatively small proportion of the whole it represents, there is reason to suspect that we are missing a lot about this group of people and their preoccupations if we only take into account the most popular content. 

But this is where many of us start, and by "us" I mean scholars and researchers as well as cultural critics, content creators seeking to create content that resonates with an audience, or marketers. What other option do we have? 

One alternative would be to take stratified samples from further down the distribution tail, an approach used in this article from The Hollywood Reporter. It's important to note that such an approach requires that the platforms make their data available in such ways as to make this feasible, and in this respect, Netflix has done us a huge favor. It is more difficult to get underneath the trending surface of TikTok or YouTube to try to get even a rough idea of what the rest of it looks like. 

And with YouTube and TikTok, the problem of unaccounted-for content is likely much worse. 

Let's do some back-of-the-envelope* calculations to try see how little of the content universe we're seeing when we examine, say, the top ten TikTok videos from last year. There are roughly 1.1 billion active monthly TikTok users. The average user spends 95 minutes on the app per day. So, that's a total roughly 104.5 billion minutes per day, or 381.5 trillion minutes per year. The most viewed TikTok video of 2023 had 504 million views and it is roughly 30 seconds long. Obviously, the next nine had fewer views than this, but I'm finding it difficult to obtain raw view numbers for each video (it's easy to find the number of followers, but plenty of people watch TikTok videos created by users they don't follow). So, let's err on the side of overestimating and say that each video is 1 minute long and is watched 500 million times. By watching the top ten TikTok videos, we are accounting for 5 billion minutes of viewing. What proportion of the total are we seeing?

Before we do the math, it's worth remembering our tendency to fail to see meaningful differences among very small proportions. We can pretty easily tell the difference between 20% of something and 5% of it but fail to differentiate between .1% and .01%, even though the difference in magnitude of the latter is more than twice the difference in magnitude of the former. Often, we just think of anything below 1% of something as "very small," whether it's .5% or .05%. But if we're really trying to know something - a culture, a media diet, etc. - it's important to correct for that bias and recognize just how small the proportion really is. 

Watching the top 10 TikTok videos of 2023 would account for less than .001% (one thousandth of one percent) of all TikTok viewing. Given that the top 100 videos would have fewer views than the top video, and given that most of those videos are under 1 minute in duration, watching the top 100 videos (a feasible, if time-consuming, task) would account for less than .01% of content viewed on TikTok. 

Even if we are studying a particular topic or domain within these high-choice environments - say, political messages or health-related messages - sampling only the most popular videos doesn't get us anywhere near the complete or representative sample that it once did in the low-choice days of mass media. Most viewing is happening outside of the sample, further down the distribution tail. Until we reckon with the vast size these media environments and the diversity of users' media diets, it's hard to know what we're missing.

*If anyone has more accurate usage data, I would love to see it! I don't have supreme faith in these data, but it's the best I could find right now. 

So you want to be an influencer

There's something about the name of the major in the department in which I teach - "Creative Media" - that, for many first-year students, brings to mind the career of an influencer. So as to disabuse them of the notion that our major will teach them how to be an influencer, I outline the differences between the career of a media professional - a broad category encompassing screenwriters, directors, producers, editors, camera-people, newscasters, sound engineers, etc. - and the career of an influencer. In searching for a metaphor or parallel to describe the career of an influencer, I typically refer to pop star musicians (though the following is likely applicable to any genre of popular music - rap, country, etc.). 

On the up side, the barrier to entry is low - anyone can start playing music, post that music online, promote it on social media, develop a following, become famous and earn plenty of money. This is in contrast to many media professional positions that require access to expensive equipment, social and/or geographic proximity to connections in the business, competitive apprenticeships, and a track record of proven success. On the down side, there is a lot more competition when the barrier is low. There's always someone younger, hotter, funnier, edgier, and more novel than you, and they're so eager and hungry for attention that they'll be happy to take that sponsorship deal you turn up your nose at. There's no incentive for platforms like YouTube, TikTok or Spotify to share much revenue with creators because there's a never-ending talent pipeline, and so they tend to pay creators very little.

Generally, pop star careers are shorter than those of many media professionals, again because of the low barrier to entry, their replaceability, and the audience's desire for novelty. Of the small percent of influencers who achieve success, it's hard to find ones who maintain it for more than a few years. This is in contrast to all of the aforementioned media professional careers that typically last decades, with salaries and job security typically increasing over time. 

There's also the challenge of maintaining a pace of output that being an influencer demands. Whereas audiences are trained to expect a new song from a musician maybe once a year, 13 new episodes of a TV show every year, and a new film from a well-known director every several years, influencers are expected to generate new content at least once a month. Maintaining that pace for years can be taxing. Looking at the Wikipedia entries of several popular influencers from the 2010's, the word "hiatus" frequently appears - an understandable response to the non-stop production schedule. This is to say nothing of the effects of public scrutiny on one's mental health, the blurring of personal and professional identities, the loss of privacy - none of which are issues for the average editor, screenwriter, or audio engineer. 

Other influencers try to make the jump to the mainstream, collaborating with established media professionals, making movies or TV shows, parlaying their success on the web into something more lasting. Some succeed while most do not. Gradually, I think influencers and the entertainment industry will get better at intuiting which personalities will transfer to the big screen and which are better suited to TikTok, YouTube, podcasts, etc. 

Another antecedent to the influencer is the career of reality TV star, though they seem to rely more heavily on personal appearance or sponsorship gigs than influencers, who seem to more effectively monetize their content and exert more control over their image from the start. Maybe the similarity is less related to their career trajectory and more to their relationship to audiences - more intimate and ordinary than the average actor or director.

This all sounds like I'm trying to dissuade students from pursuing the life of an influencer, which I'm not. The fact is that tens (or maybe hundreds?) of thousands of influencers (broadly defined) make enough money to live on. I'd guess that this is more than the number of people who make a living at being a pop star, but maybe less than those who make a living as a musician. 

Being an influencer, like being a pop star, seems to require "natural talent." There's only so much you can be taught about how to succeed in those realms, and a college classroom certainly isn't the place to learn it. Better to just watch some tutorial videos, go out there, and do it. And if you got it, you got it, and if you don't, you don't. I can't think of a reason not to pursue both paths - the path of the influencer and the path of the media professional - simultaneously, though the time demands of either path will eventually force you to decide. 

As the influencer phenomenon continues to age, we'll get better at answer these questions about that career: Do influencers get enough revenue coming in from their videos that were popular years before to make a living? Do sponsorship deals persist or do they dry up? What does the second (or third) act of the career of an influencer look like?

Do people care who (or what) wrote this?

Generative A.I. as a writing tool has limitations. But what I've discovered over the past week is that my perceptions of those limitations can drastically change when I learn about a new way to use it. Before, I'd been giving ChatGPT fairly vague prompts: "Describe the town of Cottondale Alabama as a news article." Listening to a copy.ai prompt engineer on Freakonomics helped me understand that being more specific in your prompts about the length of the output ("500-1000 words") and the audience ("highly-educated audience") makes all the difference. 

The other key lesson is to think of writing with A.I. as an iterative collaboration: ask the program to generate five options, use your gold ol' fashioned human judgment to select the best one, then ask it to further refine or develop that option. If you find it to be boring, ask it to vary the sentence structure or generate five new metaphors for something and then pick the best one. I sensed that writing with generative A.I. could be more like a collaboration with a co-author than an enhanced version of auto-correct; this helped me to see what, exactly, that collaboration looks like, and how to effectively collaborate with the program. 

As the output got better and better, I wondered, "has anyone done a blind test of readers' ability to discern A.I.-assisted writing from purely human writing?" I'd heard of a few misleading journalistic stunts where writers trick readers into thinking that they're reading human writing when, in fact, they are not. But I'm looking for something more rigorous, something that compares readers' abilities to discern that difference across genres of writing: short news articles, poetry, short stories, long-form journalism, short documentary scripts, etc. It seems likely that readers will prefer the A.I.-assisted version in some cases, but it's important to know what types of cases those will be. 

I also wondered what our reactions - as readers and writers - to all of this. I can think of three metaphors for possible reactions to A.I.-assisted writing:

1) the word processor. It's use changed how writers write. It changed the output. Like most disruptive technologies, it was met with skepticism and hostility. But eventually, it was widely adopted. Young writers who hadn't grown up writing free-hand had an easier time adapting to this new way of writing. The technology became "domesticated" - normal to the point of being invisible, embedded in pre-existing structures of economy and society. 

2) machine generated art. Machines have been generating visual art for decades. Some of that art is indiscernible from human generated visual art. Some of it embodies the kinds of aesthetic characteristics that people value. And yet machine generated art has never risen beyond a small niche. The market for visual art largely rejects it, in part because those who enjoy art care about how it is created. Something about the person who created it and the process by which it was created is part of what they value about art. 

3) performance enhancing drugs. Output from A.I.-assisted writing is superior - in some cases far superior - to unaided human writing, and there is market demand for it - the public sets aside its qualms and embraces good writing regardless of how it came about. This situation is perceived by writers, some industries, and some governments as unfair or possibly dangerous, maybe in terms of what bad actors could do with such a tool or how profoundly disruptive its widespread use would be for economies and society. Therefore, they regulate it, discourage its use through public shaming, or, in some cases, explicitly forbid its use. 

The quality of A.I.-assisted writing's output is only part of what will determine its eventual place in our lives. The general public's reaction to it is another part worth paying attention to. 

An ethical case for using A.I. in creative domains

A few months after first considering the promise and threat of A.I. in creative domains, it's still the threats that are getting the most attention. I tend to hear less about the possibility that by allowing A.I. to be used widely (which helps it grow more sophisticated) we are hastening a machine-led apocalypse and more about what we would lose if we replaced human writers with A.I. It would be an obvious loss for the people who write for a living, but they make the case that it would be a loss for society. Creativity would decline, mediocrity would flourish, and we would lose the ineffable sense of humanity that makes great art. By taking the power to create out of the hands of the many writers and putting it in the hands of the few big tech companies, we would exacerbate inequality and consolidate control over culture. 

There are a few steps in this hypothetical process worth scrutinizing. First, this argument assumed that if A.I. is allowed to be used in a creative field (screenwriting, journalism, education), it will necessarily lead to the replacement of human labor. There's a market logic to this: if you owned a company and you could automate a process at a fraction of the cost of paying someone to do it, you would have to automate it. If you didn't, your competition would automate it, be able to produce an equivalent good or experience at a lower cost, charge consumers less for it, be a better value to shareholders as a publicly traded company, and put you out of business. You could point to examples of such things happening in the past as evidence of this logic (though I have to admit, I found it hard to find examples that used human communication rather than physical labor. I'd assumed chatbots had led to steep declines in customer service labor, but all I could find was editorials about how it will lead to steep declines and competing editorials about how customers find chatbots enraging and still demand human customer service agents). 

But I still have trouble thinking of this particular replacement-of-human-labor trajectory as inevitable. I can't help but think of A.I. as a tool that humans use rather than a replacement for humans, more like a word processor or the internet than a brain. I can't not see a future (and, honestly, a present) in which writers of all kinds use A.I. for parts of the writing process: formatting, idea generation, wordsmithing. Humans prompt the A.I., evaluate its output, edit it, combine it with something they generated, and share an attribution with the A.I. You could call this collaboration or you could call this supervision, depending on how optimistic or pessimistic you are, but the work that it generates is likely better than what A.I. generates on its own and it is generated faster than what humans generate on their own. But humans who prompt, edit, evaluate, and contribute to creating quality work are as necessary as they were before. They can still use that necessity to make their case when bargaining with corporate ownership. 

I also have trouble seeing a marketplace in which all content is generated by A.I. If the A.I. can only generate mediocre content, won't people recognize its mediocrity and prefer human-made creative work? It's hard not to see this particular facet of the argument against A.I. in creative fields as elitist snobbery - "of course the masses will choose the A.I.-generated dreck they're served! We highly-educated members of the creative class must save them by directing them toward 'True Art,' (which we just happened to create and have a financial stake in preserving)."

And that is an ethical argument for A.I. in creative fields that I have yet to hear: the argument that the class of people who are currently paid for being creative are protectionist. If they can just keep us thinking about Big Tech and the obscenely wealthy studio execs, we won't have to think about the vast number of smart, creative, compassionate people who happen to not know how to write well, or to write well in a particular language. I worked hard at becoming a good writer, spending a lot of time and money to acquire this marketable skill. Does that make it morally right to deprive others of the ability to use a writing tool that levels the creative playing field? I assume there are millions of people with the life experience and creativity to be great writers who simply lack the educational experience to craft grammatically correct prose. Who am I to insist they take out loans and wait years before they can make worthy artistic contributions?

I do understand the replacement-of-human-labor argument against A.I. None of the anti-protectionism argument really resolves or even speaks to the market logic argument. I suppose this is what smart regulation does - limit the use of technology in cases where we see clear evidence of social harm but allow it where there are opportunities for social good. As an educator, I want to make sure that students understand how to recognize the characteristics of "good" (i.e., compelling, effective at communicating, lasting the test of time) writing, even if they need a little help getting their subjects and verbs to agree.

It can be hard to see the good of A.I. in creative realms at this stage in the development cycle. It is hard to see the would-be writers and the untold stories, but any ethical approach to the question of A.I. in creative fields must consider them. 

Types of audience fragmentation

 I'm embarking on a new large-scale project relating to audience fragmentation. Or rather, I have been embarking on it for the past year - such is the leisurely pace of the post-tenure research agenda. It started as a refutation of the echo chamber as an intuitive but overly simplistic characterization of audiences' media diets in the age of information abundance. Then I realized that someone already wrote that book. 

In researching the idea, I was surprised to find how few studies about fragmenting audiences and echo chambers even tried to capture what I felt was the right kind of data: data capturing the whole of people's media diets - not aggregate audience data, not what individual users post on a particular platform, not even the amount of time or what individuals see on a particular platform, but ALL of what they see across all platforms and media. Unless you capture that, you really have no way of knowing whether individuals have any overlap with one another in what content they consume and/or how many of them are sequestering themselves in ideologically polarized echo chambers. 

In defense of researchers, this is a hard kind of data to get. What media content people consume is often a private matter. It's just hard - for an academic researcher, a company, a government - to get people to trust them enough to get that data. Observing people might cause them to change their behavior. Still, some researchers have made in-roads - working with representative samples, trying to get precise, granular data on precisely what content people are seeing - and I think that if we start to piece together what they have gathered and supplement it with new data, we'll be able to get a better sense of what audience fragmentation actually looks like. 

I've started the process of collecting that data. In a survey, I've asked a sample of college students to post URLs of the last 10 TikTok videos they watched, the last 10 YouTube videos they watched, and the last 10 streaming or TV shows they watched. I'm anticipating that there will be more overlap in the TV data than in the YouTube of TikTok data. But I wonder what counts as meaningful when it comes to overlap or fragmentation. I return to the age-old question: so what?

Let's say you have a group of 100 people. In one scenario, 50 of them watch NFL highlight videos, 25 watch far-right propaganda videos, and 25 watch far-left propaganda videos. In another scenario, all 100 of them watch 100 different videos about knitting. The latter audience, as a whole, is more fragmented than the former audience. The former is more polarized in terms of the content it consumes - half of the sample can be said to occupy echo chambers, either on the right or left. 

It's clear to me while the polarization of media diets matters - it likely drives political violence, instability, etc. But why does fragmentation, in and of itself, matter? 

I guess one fear is that we will no longer have any common experiences, and that will make it harder to feel like we all live in the same society - not as bad as being ideologically polarized, but it's plausible to think that it might lead to a lack of empathy or understanding. But what counts as a common experience? Do we have to have consumed the same media text? Stuart Hall would tell you, in case you didn't already know, that different people watching the same TV episode can process it in different ways, leading to different outcomes. But at least there would be some common ground or experience. 

But what if we watched the same genre of television show, or watched the same type of video (e.g., videos about knitting)? If we contrast the 100 people who all watched different knitting videos to 100 people who all watched videos about 100 very different topics (e.g., knitting, fistfights, European history, coding, basketball highlights, lifestyle porn, etc.), I would think that the former group would have more to talk about - more common ground and experience - than the latter, despite the fact that there is an equal amount of overlap (which is to say, no overlap) in terms of each discrete video they watched. 

Instead of just looking at fragmentation across discrete texts, it would also be useful to look at it across genres or types. It could get tricky determining what qualifies as a meaningful genre or TikTok video. Some TikTok videos share a set of aesthetic conventions but may not convey the same set of values, or vice versa. There will be some similarities across the texts in people's media diets, even if there is no overlap in the discrete texts. The challenge now is to decide what similarities are meaningful. 

Micro-blogging, Take 2

As a social media platform, you know you've achieved success when others start cloning you. It's easy to call to mind the successful copycat platforms that, in several cases, far exceeded their predecessors:  Facebook (MySpace, Friendster), Reddit (Digg). It's a bit harder to recall the many clones that never make it (Voat, Google+, Orkut), typically because the network effects that are intrinsic to platforms' success put those with small userbases at a distinct disadvantage or because they lack the infrastructure and/or revenue to support a rapidly growing userbase. In other words, there typically aren't enough people to make the place interesting or valuable, or there are too many people to keep running/moderating the platform for free. 

But Meta/Facebook/Instagram's introduction of Threads is different in this regard, giving us a chance to see what a clone could do if it didn't have to worry about those two problems. Threads has already successfully ported 100 million users from Instagram, maintaining the network structure among interest/affinity groups and connections between established influencers and their audiences. It also has Meta's massive infrastructure at its disposal - growth won't be a problem. And so we have a rare opportunity to see if this version of a micro-blogging platform - already operating at a scale similar to the existing leader, Twitter - will be all that different than what came before. 

Mark Zuckerberg has pitched Threads as a friendlier version of Twitter. Broad generalizations about the emotional valence of any social space are inherently oversimplifying - you can find pockets of friendliness and hostility among virtually any large group of people, online or offline. Still, it's entirely possible that one space could have the tendency to be friendlier than another - that's an empirically testable claim (provided you can agree on how to measure "friendliness"). 

Before trying to determine whether Threads has or is likely to achieve this goal (or whether such a goal is desirable, or if friendliness and ideological heterogeneity are mutually exclusive), it's worth considering how it might go about achieving it. Most obviously, more content moderation might tamp down overt hostility. Less obviously, there are facets of the platform that affect linkages among users - which users' posts are visible to other users. 

By importing lists of followers and popular accounts from Instagram, Threads imported a set of cultural norms, one that evolved over the last decade and privileged attractive or attention-getting still images over words, audio, or video. Broadly speaking, there's a kind of showy-ness to Instagram, a content ranking system that rewards positivity (some would argue to toxic levels). Then there's the sociotechnical context in which Threads is being deployed - as a kind of antidote to Twitter's perceived problem with negativity, conflict, and abuse. If Twitter wasn't an especially friendly place before Elon Musk took it over, it is much less so now. This might create demand for such a place, which Threads is well positioned to serve.

Then there's that pesky algorithm - the necessarily obscure formula that controls which posts appear at the top of your feed. Despite widespread skepticism toward algorithms, its hard to imagine a popular social media platform without one, particular one that aspires not to link small groups of people together (e.g., Facebook, Discord, GroupMe, and the way some people use Snapchat) but to give everyone the chance - however remote - to command an audience. Imagine ranking YouTube or TikTok videos chronologically, or at random. Some weighted combination or popularity, engagement (e.g., number of comments or shares), and predicted affinity (amount of time you've spent on similar posts) is the best way to keep users coming back for more. 

One way to go about ranking posts is to defer to the masses - showcase whatever is broadly popular, as Twitter does with its prominently displayed list of trending topics. Another way is to tailor it to each user's preferences - the niche approach favored by TikTok. The first kind of ranking creates a "main character of the day" a target for attention and ridicule on and beyond the platform. The second kind, supposedly, creates echo chambers (though evidence is mounting that, as intuitive as this understanding of personalized ranking is, it doesn't fit what most users actually see on social media). Inheriting its structure from Instagram, Threads seems to privilege, as Kyle Chayka put it, banal celebrities and self-branding. As masspersonal media where any user can potentially reach millions of other users, Threads cannot help but encourage a kind of performativity over connecting with a small group. 

Then there's the shift from images and short video to text. The whole reason Threads is being talked about as a Twitter clone is because its primarily intended to be used for mass conversation. In their branching/nested structure (you can reply to a reply to a post, with each reply "nested" under the previous message), conversations on Threads resemble conversations on Reddit, and it will be interesting to see if future designs of Threads nudge users to engage more in the replies.

But I wonder about the brevity of text and what that does to conversations. The whole point of Twitter - what put the "micro" in "micro-blogging" - was the character limit (originally 140, upped to 280). It's well-suited to a fragmented attention universe, but I wonder if the tone of Twitter (witty, sure, but also mean) is an inevitable symptom of its mandatory brevity. Is there something about short-form writing that is bound to regress toward snark? Is that simply the nature of the medium, regardless of the combination of people and level of moderation? That's what Threads might give us a chance to observe.

When subreddits go dark

Among the many unforeseen effects of ChatGPT's release, there is a change in policy at Reddit that has caused a significant disruption among its community moderators. Reddit has served as a valuable and, to date, free source of training data for ChatGPT and other large language model (LLM) AI - billions of utterances from hundreds of millions of people about thousands of topics over a 15 year span. These LLM AIs are already worth billions of dollars, more than Reddit was ever worth during its first 15 years. It is therefore understandable that Reddit as a company wants to stop the practice of giving its back catalog of data away for free. They're not the only ones keen to point out that the training data used by LLMs, while ostensibly free to access, were created and facilitated by others who, it could be argued, were indispensable in the creation of now-popular AI programs like ChatGPT.

This isn't the only reason why Reddit would want to turn off the spigot of free access to its vast archives of posts and comments via an API. An ecosystem of third party apps has flourished under this policy, resulting in the loss of untold hours of user attention to ads on Reddit's official app, and thus lost revenue. Many users have become accustomed to accessing Reddit this way, and are understandably upset at having to migrate to the official Reddit app, widely regarded as inferior to the third party apps. 

Then there's the issue of how subreddit moderators use the API to more effectively moderate their communities. They can use the API to quickly assess a user's posting or comment history to see if a disruptive comment or post is part of a larger pattern and thus worth banning the user (this includes spambots and trolls that, unmoderated, could overwhelm a subreddit with useless or disruptive content). They use them to determine when a question asked by a user has already been answered in the past, and to highlight that answer. APIs are also relied upon by some users with disabilities, as a way of access the platform. This post from the moderators of r/AskHistorians has a good summary of other ways in which mods rely on the API. 

Reddit as a company seems interested in addressing the disability accessibility issue, carving out an exception for third party apps specifically designed for users with disabilities. Beyond that, they don't seem particularly interested in walking back their decision to charge for access to the API...yet. 

The conflict between the administrators (paid employees of the company) and moderators (unpaid volunteers who manage Reddit's tens of thousands of active communities) is a familiar one - management vs. labor. As with any such conflict, labor's ability to get what they want assumes that they can't easily be replaced by more willing labor - be it human or automated. Can the company still produce something of value to the consumer without the willing participation of current labor? 

In most cases, replacement labor produces something different than what was produced by the original labor, and in most cases, its (at least initially) regarded as inferior (labor certainly has an interest in highlighting its inferiority). But the question of fungibility of labor, from management's standpoint, in the world of social media is a tricky one. The culture and communities that live on popular social media platforms - the things that makes them valuable - are constantly shifting. As in most cultures and communities in the physical world, users frequently lament these shifts, blaming new entrants to the community or powerful authorities. If they hate the changes enough, they leave. 

So, when moderators of a popular subreddit choose to go on strike, effectively killing that subreddit, what happens to its users? 

One possibility is that they leave the platform. This is, I would think, what the mods are trying to accomplish - driving traffic off the platform, hurting Reddit's bottom line, and getting management some bad press. Another possibility is that the users of that subreddit migrate to other existing subreddits - ones with moderators that didn't strike - and find that these other subreddits are roughly as good at satisfying their need for distraction, information, community, amusement, comfort, etc., resulting in a surge of activity on those subreddits. Yet another possibility is that new subreddits arise to meet the demand created by the absence of striking subreddits. "Splinter" subreddits - subreddits that are created to cater to disgruntled "refugees" from a subreddit that has changed in some disagreeable way - have always been a part of the subreddit ecosystem. Modularity is a defining feature of Reddit, something that sets it apart from the single, amorphous conversation on Twitter and the atomized, fleeting comments on TikTok, YouTube, and Instagram. In this case, it makes it harder for labor to force management to do anything. Unless a critical degree of solidarity among moderators is reached and sustained (a tall order, given the number and diversity of subreddits), its hard to prevent within-platform user migration. 

Reddit's design - a feed or list of posts aggregated from subreddits to which users subscribe - can make it hard to even notice the absence of a striking subreddit. When I went to the site, it took me awhile to realize what was missing; I was still seeing pictures of cute animals, funny things that people said or did, captivating vistas and infuriating news. Of course, the impact is going to vary from user to user. For some users, access to a particular subreddit can be as valuable as access to a close friend, one whose absence would result in a sense of profound loss. It's hard to see how many users are like that - management has an interest in making it seem as though they are a small, vocal minority while labor has an interest in making it seem as though most users are upset by the changes - so upset that they are already in the process of leaving.

It's possible that a large number of currently-popular subreddits die as a result of this disruption, and that a large number of users leave the platform and don't come back. It's easy to point to moribund platforms like Digg or MySpace that never found a replacement community that could sustain the business. But that doesn't take into account the current business climate for these businesses, news organizations, and even video and audio streaming services that seem (with varying degrees of success) to be training consumers to pay - in subscription fees or attention to ads - for what they consume. If the era of free high-quality user-generated-content is over, there may be no substitutable platform for disgruntled users to migrate to. 

It also may underestimate how organic and unpredictable large groups of people are. We get used to versions of these platforms - used to seeing a certain type of post or comment at the top of the feed, the popularity of which reflects the collective sensibilities of a voting constituency of users. That sensibility persists even in the face of high turnover among contributors - it is generated not by a stable group of super-users but by a rotating cast who cater to the preferences of the constituency. Disruptions like the current one can change the voting constituencies and thus change what we see at the top of our feeds. Perhaps mods' lack of free access to APIs will make it effectively impossible to manage subreddits beyond a certain size (say, 1,000 active contributors), leading to a Reddit with no large communities and more moderately sized communities that, when they grow unmanageably large, spawn offshoots - something a bit more like Discord. That version of Reddit might be far more diverse in its interests and sensibilities, and ultimately more successful, than the version we're now accustomed to.

The promise and threat of A.I. in creative domains

Though we've been expecting AI to get good enough at writing to actually perform a decent number and variety of writing tasks "by itself" (it still needs humans to engineer prompts), we hadn't been confronted with that reality until recently, when ChatGPT became more widely used. To test it out, I gave it some of the (relatively simple) prompts I give my undergraduate film history students and saw what it came up with. Then, I asked it to write a pilot episode of a teen drama set in Montana, a grant proposal for a research study on social media use and political polarization, a research paper on the environmental impact of microplastics, a quiz on 1970's media cultural studies, a review of Beck's new album, an amicus brief on microplastics, and a summary of the 1998 NBA Finals in the style of Proust. The results, as many have noted, aren't perfect but are a lot closer to perfect than what we're used to seeing from programs. It's hard to objectively convey the quality of the output and the limitations of the tool; it's best to just try it out yourself. 

I'm sure such tools will improve, but it's hard to say at what rate and to what degree, so I'll try to just stick to evaluating the impact of the current iteration of generative text programs like ChatGPT on two facets of writing: assessment (specifically, educators' ability to assess students' understanding of concepts, thinking, and communication skills) and its applications in various domains (i.e., journalism, creative writing, scientific writing). 

In terms of assessment, the ability of students to avoid actually doing the work of writing for an assignment and to avoid detection has increased significantly. However, the truth of this statement is highly dependent on the assignment. If the assignment just requires students to gather information (particularly older info on an extensively covered topic, person, or event) and synthesize it into a simple, readable essay or article, then ChatGPT seems to be quite good at that. But what about, say, watching a particular video on YouTube and applying something from a particular reading to that video? I would assume that ChatGPT can't easily extract the information that's in the video, so it would have a tough time with this task, and that it's ability to do anything with a particular reading is contingent upon that reading being freely available and accessible online somewhere. If the reading is in a textbook that doesn't appear online (or only appears on a few hard-to-access pirating sites), then this will be harder for ChatGPT to work with than, say, the Magna Carta or any other text that's in the public domain. But in any of these cases, I think that A.I. co-authoring (where ChatGPT does some of the work and the student changes and adds a few things) will be essentially impossible to detect. 

Another approach to ChatGPT's threat to assessment is to simply proctor writing assignments. That's easier to do for short writing assignments but difficult for longer ones, and impractical for online classes. Still, as a professor who has an interest in fairly and accurately assessing his student's writing, I'm leaning toward more in-class writing assignments and fewer outside-of-class writing assignments because of how challenging it will be to tell the difference between A.I.-generated writing and my students' writing outside the classroom. 

But the topic of applications of AI is more interesting to me, and in some sense pre-empts questions about assessment. If AI can accomplish much of what we're trying to train our students to accomplish, what is the point of such training? The rapid adoption of ChatGPT has forced me to reconsider the relationship between writing and thinking. Writing is often the medium through which thought is expressed, though not the only one; spontaneous discussions and conversation are a good alternative. When is writing chiefly a vehicle for thought and when is it more an integral part of the process of thought? Those who defend the sanctity of human writing, I surmise, would say either that writing is very often part of thinking, or that we may not be very good at being able to tell the difference between writing-as-vehicle-for-thought and writing-as-thinking. Therefore, ceding the task of any but the most formulaic types of writing to AI risks sacrificing the ability to think. 

We still want to get better at thinking, and we still want our students to get better at thinking (e.g., critical thinking, creative thinking, logical reasoning, problem solving). Even in a world of prevalent AI, having those abilities will make us better at prompt engineering, better at judging the value of what ChatGPT and similar AI spit out, and better at making refinements to that output. The same would apply for non-linguistic AI: prompting an AI architectural design program and judging/refining the results; prompting an AI coding program and judging/refining the results. In any application I can think of, humans are still part of the process. Whether AI is producing a kind of structural scaffold for ideas, identifying sources of information, or generating new ideas, it has to be set in motion, its output needs to be evaluated, and in its current iteration, its output requires a fair amount of refinement. That process of prompting and refining can be similar to a good conversation or collaborative improvisation ("yes, and..." thinking). 

I sense that there's a widespread antipathy toward applying AI in creative domains. I don't consider myself an AI optimist. I acknowledge how tempting it will be for students, businesses, creators, and governments to use AI in an indiscriminate manner, not bothering to vet or refine its output. I acknowledge that such use could cultivate a kind of passivity and (further) overreliance on technology. More people would get out of the habit of "thinking for themselves." Creativity would be increasingly derivative in the name of efficiency. 

But does this warrant an outright ban on AI? What would such an approach sacrifice?

I feel as though we've been good at articulating the risks of AI (aside from the aforementioned, there's scaling up spam and disinformation, and deceiving job recruiters or potential partners on dating sites). We're less good at articulating its potential upside. I think of that upside chiefly in terms of resource reallocation (which, yes, sounds very cold and business-speaky, but hear me out). 

Let's treat ChatGPT and the like as co-authors. They generate first drafts or templates but don't write the entire text or finished product. Human writers' role in the creative process shifts from crafting sentences and paragraphs to gathering primary original sources and identifying needs in their audiences and communities. What if writers could take the time they would have spent struggling to write formulaic copy and instead spent it cultivating twice as many primary sources as they would have otherwise, or co-writing twice as many stories, or ten times as many stories? Consider all the un-written stories or accounts of events; consider what's missing from historical accounts and news coverage, in part because the people who know how to create thorough and entertaining writing only have so much time. Some people's stories are told, some people and events are remembered, but the vast majority are forgotten. What if we could fill in some of those blanks? We don't have to be indiscriminate in our use of AI to turn data into stories that no one cares to read. Remember, humans and their understanding of what is valued are still part of the process. And what if we could offer those stories in twice as many accessible formats? If editors and readers valued these qualities more than they feared AI's downsides, we could use AI to improve our information environment. 

The genie is out of the bottle. If one country or group of people wants to spend time trying to stuff the genie back into the bottle, that won't stop other countries or groups of people from using it. We needn't be totalizing in how we conceive of the application of AI in creative domains. We have been discriminate in our use of technologies in the past, often making mistakes, attempting to correct those mistakes by introducing limitations or redesigning the technology, fitfully moving along but rarely un-inventing anything. Maybe we should look around and see where the successes and failures are right now. 

Futures of Twitter: Scenario Testing

Yes, I've railed against the appetite for future forecasting in this blog before. In fact, pretty much every post from the past few years is, in its own way, a screed against the kind of prediction of complex systems of long periods of time that news readers and journalists who interview academics can't seem to get enough of. And yet, when something "big" happens - a pandemic, the election of Donald Trump, Elon Musk taking over Twitter - I feel the same insatiable pull toward forecasting that everyone else feels. These are important events with far-reaching implications, and so despite their complexity, we can't seem to just say, "who knows what will happen!" We have to venture a guess.

And not just any guess. When we see worst-case-scenarios as plausible, we detest a future in which we ignored the threat so much that we have to predict doom. The unpleasantness of comprehending those futures is outweighed by our fear of feeling guilty about (and of being guilty of) not having done enough to prevent them. The other side of the argument is that there has never been a time in human history when worst-case-scenarios weren't plausible, and to spend time constantly ringing alarms is both joyless and unproductive, and that our inability or unwillingness to recall the false alarms of yesterday keeps us from learning anything about how complex systems actually work, to actually improve our prediction accuracy, and - by extension - to improve our world. I do get both sides of that argument, and so to me it seems unresolvable. 

So, to further refine my stance against long-term predictions about complex systems (the global economy, geopolitics, culture, developments in technology, etc.), I'll say that I prefer something less like predicting whether some event will spell doom for society (super vague, no timeline for when "doom" will occur, what shape it will take, etc., and so unfalsifiable) and more like fivethirtyeight's scenario testing. You start with several plausible scenarios - each one mutually exclusive to the degree that it's possible, and fairly specific about outcomes and timelines but not so specific as to be inevitably falsified by some minor quirk of history. This, at least, would allow us to learn something if our predictions are wrong, allow us to do a post-mortem and see what we missed, what variables we weighed too heavily, etc. Of course, this is dissatisfying if you are a doom-predictor because all the learning in the world won't amount to a hill of beans after the apocalypse that you failed to predict! But assuming there's at least a few people left on planet Earth, learning will be a good thing. To my mind, entertaining somewhat optimistic scenarios isn't mutually exclusive with being prepared for (though not actually predicting) the worst. 

In that spirit, here are a few scenarios regarding Musk's takeover of Twitter, some more optimistic than others. They use three metrics of success/failure: financial success (does the company turn a profit, stay solvent, continue to exist), cultural relevance (does the general public care about what's said on Twitter as much as they do now, more than they do now, or less than they do now? Does some other platform become the proxy for vox populi that Twitter is right now?), and "externalities" (affective polarization, political violence, political revolutions, cancelling/calling out people. Externalities could be both "good" [revolts against dictatorships, publicly shaming those who deserve it] or "bad" [downfalls of democracies, publicly shaming those who don't deserve it]).

Scenario 1: Financial success with lower cultural relevance and few "externalities." The most intriguing part of Musk's takeover to me, so far, is his apparent willingness to blow up the existing way that Twitter makes (or attempts to make) money. Perhaps the changes he instates drive user count WAY down, but this won't necessarily make Twitter less of a financial success. He could charge a portion of users $8, or pay the 100 most followed $1,000,000 and charge anyone who tweets more than 100 times a year $50 (a kind of 'lottery-ticket' model that, when you think about it, isn't that different than would-be filmmakers shelling out for expensive equipment and film school for a crack at a studio contract). My personal favorite idea is Willy Staley's "congestion pricing" plan - when a certain number of users have tweeted about a topic, the price of tweeting about it increases, which would - in theory - encourage people to tweet less about trending topics and more about a diverse array of topics. Or maybe a revived Vine drives profits and allows Twitter to continue to be a loss leader. Regardless of how he does it, it's possible that Twitter becomes profitable.

Meanwhile, the platform loses some of its cultural cache. The behavior (on Twitter and off) of Musk and a handful of high-profile Twitter users who would have been regulated under the old regime turns off journalists and tastemakers to such a degree that they stop paying attention to it (regardless of how outlandish it is) and pay more attention to how YouTubers, TikTokkers, and podcasters react to the day's events. Twitter will always have the brevity and "portability" of text on its side - it's easier, in the course of conversation, to tell someone what another person Tweeted than to try to describe even a short video. So, maybe a Twitter alternative (short text, highlighting trending topics) pops up and steals at least part of the cultural spotlight from Twitter. 

Regarding "externalities," there's no convincing evidence that what people say on Twitter causes political violence, revolutions, voter intimidation, cancelling, etc. These things happened before and they continue to happen, but it's just as easy to point to what people are saying on TikTok, YouTube, and podcasts as it is to point to what people are saying on Twitter as the cause of it, and so the argument that a Musk-led Twitter is leading to the downfall of society never really gains traction. 

Scenario 2: Financial success with the same level of cultural relevance and no apparent increase in externalities. Same as Scenario 1 except people (journalists, podcasters, YouTubers, TikTokkers, and - by extension - people who pay attention to what those people have to say) keep paying attention to Twitter. Maybe what's said on Twitter doesn't change all that much, or maybe it turns into a different kind of dumpster fire that people can't ignore. This is basically a kind of 'status quo' scenario. Sure, somethings change (the way Twitter monetizes users, the number of users, the character of the userbase), but no seismic changes to culture or to Twitter as a corporate entity. To me, this still feels like the most likely scenario. Habit and inertia are powerful forces - people will want to keep using the platform they've gotten into the habit of using, and reporters will want to keep referring to Twitter as vox populi. The bulk of everyday users who don't engage with trending topics will muddle along - perhaps as long as they have no interest in reaching large audiences with their tweets, they'll be allowed to use the platform for free. 

Scenario 3: Financial success with the same level of cultural relevance and "bad" externalities. In this scenario, there are a few instances of political violence that can be clearly and obviously linked to what people say on Twitter. Musk will point out that people are saying the exact same things on other platforms like YouTube and TikTok and that his company is being judged by a double-standard, but the sizable portion that already has it in for him won't want to hear it. On a "normal" ad-supported social media platform, this would start a death spiral - advertisers pull out, revenue craters, and they can't keep the lights on. But it's interesting to consider what would happen if Musk pivoted away from an ad-supported model, which he already seems to be doing. How bad and/or obvious would the externalities have to get before governments blocked the platform? I suppose middle-layer companies (occupying the space between users and platforms) like Apple, Alphabet, and AT&T might say 'enough is enough' and drop Twitter, but then maybe Musk uses Starlink to do an end run around them! This sounds a bit far-fetched to me, but you never know. 

Scenario 4: Financial failure. Enough high-profile Twitter users leave that the platform becomes less attractive to many everyday users, and the ad-supported models becomes unviable. Musk's attempts to charge subscription fees results in users trying to circumvent the fees, leading to draconion crack-downs, further turning off everyday users who head over to YouTube, TikTok, podcasts, Reddit, and Twitter clones. It's odd to think of a platform that still has cultural cache going belly up, but to some extent this is what happened to Vine. If you can't make the numbers work, your impact on culture is a moot point - you just cease to exist...or you get acquired, sold off, merged, etc. What might Twitter's afterlife look like if that happens? 

This, I think, gets us to focus on particular observables - financials, number of users, the extent to which people are talking about/paying attention to what's said on Twitter, political disruption - which is all I'm really hoping for in conversations about the future. 

How platform-specific is influence?

In the past, I've railed against making predictions about the future as a way to understand the present. This is different than predictions used as a way of dealing with uncertainty through humor - a pretty common, totally understandable reaction to crises. I'm thinking more of op-ed hot takes that seem to want to be taken seriously.

Predictions like this are often so vague that they're impossible to prove wrong. For example, if I predict that Twitter/Facebook/Reddit will die/fail, how and when will I know if this turns out to be true? If their userbases, sometime over the next 5 years, decline by 35%, have they died/failed? How about 50%? Do we have to wait another 5 years to judge the prophecy? There's also the problem of lacking a counterfactual. If something horrible happens after someone uses social media, we can't compare it to a world in which they did not use social media. It's possible the horrible thing would have happened anyway (or perhaps something even...horribler?), just through some other mechanism. There is no disincentive for continuing to make bold, wrong predictions because there is no mechanism for keeping score, no highly visible public record of wrong takes (though spectacularly wrong takes do tend to catch flak).

And yet I'm finding it hard not to use the occasion of Elon Musk's (apparently successful?) attempt to buy Twitter as a time to indulge in some predictions. I'll compensate for this indulgence by offering something more in the spirit of this blog - using the occasion as a way to gain insight into something more general about the uses and impact of social media. But first...

I can think of two likely scenarios:

Scenario 1: very little changes. Twitter use habits, like any kind of habit, are hard to change. There have been many instances in which social media platforms have done something that users have not liked, but most users do not then abandon the platform. We can more easily recall the instances in which they did abandon the platform: MySpace and Digg come to mind for me. But it seems that nearly every significant change to a platform - be it functionality, moderation policy, or ownership - tends not to result in a wholesale change in user behavior. The "culture" of Twitter may change, but linking this to Musk's ownership can be a tricky claim to verify, as cultures are always changing. In this scenario, there is no massive change in Twitter discourse (i.e., who participates and what is said or amplified) that can be directly and obviously linked to Musk's ownership of the platform. A handful of influential Twitter users on the Left will defect, as will a few hundred thousand less-influential users (including many journalists who will write about the experience). They will be displaced by a few newly influential users on the Right and a few hundred thousand less-influential tech bros and Far Right/Alt Right users. Musk might put the worst of the Far Right users in seemingly random "time outs," less out of any political conviction and more to reassert dominance get a laugh at their righteous indignation at having their trust betrayed. Losing the revenue they generate through ad exposure or subscriptions won't hurt, but the board will chastise him for playing fast and loose and he'll promise to be better. Pundits will pin everything the Right does on Musk and Twitter, but the connection won't be obvious and the claims won't be of interest to anyone outside of a bubble of journalists, news junkies, and academics. I consider this the more likely of the two scenarios. 

Scenario 2: things go poorly, less because of how Musk changes the functioning of the site and more because of the signal it sends to current and potential users. Far Right fringe groups and apolitical trolls will see Musk's unwillingness to regulate content as an invitation to push things as far as they can, inciting small-but-significant numbers of Far Right "activists" to engage in terrorism and assassination. A handful of violent political acts will be linked to Twitter in a way that will seem obvious and undeniable, not just to readers of New York Times opinion pieces but to the average politically-disengaged citizen, who will come to associate Musk and all his brands (e.g., Tesla, SpaceX) with toxicity rather than innovation. Musk will then belatedly recognize the damage to his brands and sell the company. 

So as to make these scenarios more falsifiable, I'll say that one will happen by the end of 2023. 

With that out of the way, I'll return to the title of the blog post, returning to a topic that I'd like to write about at greater length in the future: what is the nature of influence on social media platforms, and how does it differ (in duration, scope, domain-specificity, etc.) from mass media or face-to-face influence?

In thinking about the possibility of influential Twitter users defecting to another platform, I realized that platform-abandonment might involve losing one's audience and the attendant influence. All fine and well to say "follow me to YouTube/TikTok/Substack/Tumblr/Medium/Mastadon/whatever!", but it's hard to coordinate a mass exodus to another platform, and harder still to sustain that interest over time, especially when the new content home offers more of an a la carte content experience rather than the smorgasbord we've come to expect from popular platforms. Smaller platforms have trouble handling large influxes of digital refugees. Perhaps especially high-profile influencers will easily move their following to another platform (or to podcasting), but what of the Twitter users with followings of 500 to 50,000? How portable is their influence? 

Those who jump ship may lose some of their audience but find a more intimate relationship between themselves and a smaller group of followers on another platform, and this may be more rewarding for them. Those who stick around a less-regulated Twitter may find themselves in more direct battles with other users with whom they disagree than had previously been the case. The 'block' function and tweaks to the much-discussed-but-poorly-understood algorithms will help determine how easy it is for users to tolerate dissent in the name of maintaining influence. But no matter how it shakes out, I think we can learn something about influence by seeing what happens to the public personae of Twitter users with decent-sized followings who leave Twitter. Maybe Twitter's loss will be YouTube's/TikTok's/Substack's/Tumblr's/Reddit's gain, both in terms of users and in terms of cultural influence, however one chooses to define and measure that slippery concept.