Science, Social Media, and the Loss of Context

I know it’s not an airport, and I’m not announcing my departure. But I’ve been asked why I deleted various social media accounts, so I’ll explain my thinking.

We lose something important that can be easily overlooked when we take our scientific discourse to algorithmic, for-profit social media platforms.

I used to be an advocate for science communication and exchange using social media. At its peak, #AcademicTwitter was an amazing space. I learned from it. Discovered papers and conferences. I know colleagues who found jobs on Twitter. It was great. But now I’ve chosen to mostly opt out. This goes for my social media usage generally, but I’ve also concluded that our current social media technologies, specifically for-profit algorithmic-based platforms, are probably not a great thing for science anymore. And they might be more harmful than we realize.

This isn’t a manifesto or a call to action. Nor a condemnation. I am not a moral authority. I’m just a guy with a few thoughts, none of which are original. Most of what I share here is a repackaging of arguments from Lanier, Newport, Ward, and others, many of which I’ve included in my antisocialmedia bibliography. It’s also a product of a many-year-long discussion with my closest friend, Brett Wertz. I’m only sharing for those who ask or come across this on their own account (impressions be damned). But first, if you read only one essay about science and social media, it should be Simon DeDeo’s The 11th Reason to Delete your Social Media Account: the Algorithm will Find You. In fact, you should probably just go read that instead.

But if you’re here for my rant, here it is:

Many academics feel obligated to be active users of social media. Either to be informed about current trends or to be engaged in scholarly debate and discourse. Or perhaps to learn about job and grant opportunities for themselves, their students, or colleagues. And for some, to communicate their expertise to the broader public. These are all reasonable and even noble motivations. For scientists, talking about science is fun. It’s our passion. And for those of us who don’t have easy access to intellectual colleagues, social media seems like an easy solution.

Social media is also a marketing tool, and academics are professionals whose livelihoods often depend on their ability to promote their work. I do think today, in 2026, social media is useful for many people. Despite this, many people, academics included, simultaneously realize how unpleasant and distorting our current social media landscapes often are. I think most would agree that social media in 2026 is something different than what it was in 2016. These platforms often bring out something other than our best selves. I’m sure we all have that colleague. So kind and endearing and always in good faith, in real life, but yet whose social media avatar manages to regularly spew vitriol 280 characters (or more) at a time. Still, many of us, and many of my colleagues whom I deeply respect, remain active users. Perhaps due to habit, compulsion, or sunk costs. Or perhaps they’re cognizant about the pros and cons and still come out in favor of their personal usage. Certainly, it can feel like there’s no other option and that this is just the way things are now. So, we can accept it or get left behind. I do think there are benefits to using social media platforms, particularly for some individuals and in some contexts, or to achieve certain ends. But what are the costs? And are the assumptions we use to justify relinquishing the format and content of our scientific dialogues to black box algorithms and a handful of tech CEOs actually warranted?

Academics often treat widely used social media platforms, like X, as a digital public square, where there is equal opportunity for individual expression and ideas compete on merit. It’s an idea marketplace. It is indeed a market. But the invisible hand is not what neoclassical economics has in mind. Utility is not user-focused, and the user is the product, not the consumer. Just to be clear: you are the product (even if you’re paying). As we’re all well aware, proprietary, opaque algorithms are in control. The algorithm is the real user. Those algorithms do not work for the “users,” i.e., you and I. They reward outrage, novelty, and emotional display. Not because they care about these emotions, per se. But because this is what drives engagement. Evidence and nuance are not necessarily valued. What we might think is earnest scientific dialogue might actually be a manicured exchange (by the real user, i.e., the algorithm, of the product, i.e., the people) designed to maximize engagement across the entire network, aka to make money.

Science is a human enterprise, conducted by humans, and we are a deeply social species. Our communication is intrinsically embedded in local and cultural cues, etiquette, and layers of norms that make cooperative exchange possible. When we move that communication onto social media platforms, many of those cues disappear. The mechanisms we’ve developed over millennia to facilitate effective, cooperative dialogue, the ones that reduce confrontation and allow complex ideas to travel between minds, are either absent or not functioning effectively. That’s a vulnerable position to be in. Algorithms exploit it. They selectively amplify and suppress elements of what could be meaningful exchange to capture a particular kind of attention. The result is communication that is complex in its subject matter but also stripped of everything that makes complex communication work. That disjunction is a core feature of the product.

Offline, we are constantly observing and processing contextual cues. Often quite subtle. Tone, posture, dress, and status signals allow us to gauge the intentions of others and regulate our behavior. Online, many of these cues disappear. When we don’t really know who we’re speaking to, we might assume similarity or hostility. Academics who would normally tread carefully in epistemological minefields can easily forget to do so online because the cues that elicit empathy are gone, let alone the prestige economy of social media metrics promoting certain types of engagement, often less than prosocial. Perhaps there are or could be norms of online social behavior and communication. Maybe we have or will develop culturally evolved online norms, just as we have in real life. But that doesn’t change the fact that online behavioral interactions occur under algorithmic control, lack face-to-face interactions, and often do not include prolonged interactions. You can always just block people. So, I’m skeptical these platforms would allow for the cultural selection of prosocial communication norms. Academics are people (for the time being), and people are status-seekers. And academics are often hyper-status-seekers. That’s not a pejorative. It’s a fact of human psychology and of how the profession works. Social media exploits these evolved incentives. The prestige economy of social media can make scholars feel that their tightly packaged insights are delivering real scientific communication. But that feedback may actually be pushing them toward performative displays, optimizing for the appearance of science rather than its content, even if that’s not a conscious rationalization. It’s just operant conditioning.

We also might think these platforms act as a leveling mechanism. Anyone with something worth saying gets heard. But the algorithm that curates, amplifies, and buries content without context is itself a form of ideological and social stratification, and maybe a more controlling force than what it replaced.

Perhaps the highest cost we pay when we share thoughts and conversations on algorithmically driven social media is the loss of control over context. Social media is void of the many layers of structure that promote serious discussion. Things like a shared vocabulary, peer review (formal or informal), shared standards, and disciplinary norms. These are replaced with decontextualized snippets and often reactive discourse.

Imagine being invited to present your work, but when you get there, you discover it’s actually a competition (a TED Talk Battle Royale). But you don’t know the rules. You don’t know who the other contestants are or what they’re saying on the stages next to yours. You don’t know who’s in the audience, what they watched before they got there, or what they’ll watch after. And the host, who has their own agenda, is deciding on the spot whose mic gets turned up, whose gets turned off, and what gets shown to whom. That’s roughly what it means to have a scientific conversation on social media. In a journal, a seminar, or a conference, you know the “room.” On social media, you mostly don’t know what’s going on, and someone else (e.g., the algorithm, platform CEO, engineers) is running the show. There is little to no capacity to control how what you share is broadcast. Science communication is already a delicate process, even under the best of scenarios. When we have scientific discussions on social media, that content becomes fodder for the algorithm and will be presented to other users with the single goal of capturing their attention, to monetize it, most typically through advertising. There’s a version of this debate that gets stuck on which platform. Heterodox types will tell you Bluesky is a left-wing echo chamber and a curated safe space for progressives who’d rather not be challenged. Progressives will tell you X is a cesspit shaped by one man’s politics, with content moderation gutted to serve his ideology. Both are probably right about the other. What neither camp often admits is that the echo-chamber problem isn’t only a product of which platform you choose. It’s also a product of algorithmic curation itself. Every for-profit algorithm is, by design, a bubble. It learns what keeps you engaged and feeds you more of it. The diversity of perspectives (or lack thereof) you feel you’re getting is part of the product. If you think your feed has escaped this, if you feel like you’re genuinely encountering the full range of opinion on a given platform, the algorithm is working, and it’s got you (cf. DeDeo).

I want to be clear that using social media and investing in other forms of scientific communication are not mutually exclusive. Someone can have an active presence on X or Bluesky and still write long-form essays, maintain rich email correspondences, and show up to regional conferences. I’ll admit I’m not fully off these platforms myself. I autoposted this blog to Bluesky to allow comments through the AT Protocol. I think that’s the kind of deliberate, narrow use that can be productive and avoid some of the costs I outline. But that’s not the only trade-off I’m thinking about. What I’ve noticed, and what concerns me most, is that social media platforms are increasingly serving as replacements for those other forms of interactions rather than additions to them. And if that’s the case, then we need to be honest about what we’re accepting. If algorithmic platforms become the primary medium of scientific communication, then the companies designing those algorithms, their proprietary objectives, their CEOs, and their engineering teams will have real and largely unaccountable influence over how science is communicated, encountered, and ultimately shaped. I don’t think this is an overly doomer take or a hypothetical risk. It’s already happened, and it’s a structural feature of the arrangement we’re quietly and often uncritically accepting.

I won’t argue against the fact that algorithmic social media offers real benefits, even for scientists. You might discover an idea you wouldn’t have found otherwise. A new colleague. A funding opportunity. A job. A way to increase your book sales. There’s a real benefit in these tools for advancing careers and, in some cases, science itself. But what we’re not discussing enough is that as long as we’re filtering those discoveries through algorithmically driven, for-profit platforms, it will only be certain ideas, certain colleagues, certain opportunities that surface. The algorithm decides what comes across your feed, and it is not optimizing for scientific progress. I don’t think we’re seriously considering what that might mean in the long run. If the primary medium through which scientists encounter each other and each other’s work is controlled by an objective function that has nothing to do with the health of science, we should at least be asking what that does to the enterprise over time. I’m skeptical it’s a good thing.

There’s one cost I want to point to that’s harder to quantify. The behavioral logic of algorithmic platforms doesn’t just influence what we see on our screens. It will eventually, and inevitably, start to shape how we think. The feedback loop of likes and engagement is a form of conditioning, and one of its subtler effects is that you stop encountering ideas as ideas. You start seeing them as potential posts. The content of your intellectual life begins to get transposed to and filtered through the question of how it would “perform.” That’s a predictable outcome of sustained exposure to these incentive structures, not a bug in how some people use the platforms.

I’m not a tech doomer. I’m not arguing against the internet, or even against social media as a whole. But I think we’re not sufficiently honest about what algorithmic platforms cost us, and we mostly only see it from one side. We see what we gain. We rarely see what’s through the looking glass. I’m aware there’s no clear alternative. Even platforms less driven by algorithmic feeds, like Bluesky or Substack, carry versions of the same problems. Likes and reposts strip context regardless of who owns the server. Substack has its own engagement and algorithmic dynamics worth being skeptical of. But I also think we’ve quietly accepted a premise worth questioning, that science requires this kind of scale and this kind of speed. Does it? I don’t think it does. Maybe it does to get a job these days. That’s another problem. But it’s not just the PhD students and postdocs railing and trying to make a name for themselves on social media. I think science progressed reasonably effectively without any of this. Networks were built through emails, listservs, phone calls, societies, conferences, and the slow accumulation of trust between known people working on shared problems. That still works. It just requires effort. And maybe that’s not a problem. Maybe there’s something worth guarding in a science that moves deliberately, that is selective, purposeful, and grounded in substantive exchange with people you actually know, rather than optimized to capture the attention of strangers and make algorithms happy.

I have plenty of friends and colleagues who are fully bought in on one platform or another. And most of them aren’t naive about it. They recognize the costs, the distortions, sometimes even the dangers of the broader ecosystem. I also recognize there are many ways to be a user (product) of these technologies, and casual users (products) do not have the same experience as more frequent ones. Some of my colleagues seem to have found what feels like genuine utility in their own particular usage, and they’ll tell you so. I’m not thinking of any one person here, or any one field. But I’ve noticed a consistency in the justifications I hear. So I want to work through some of the counterpoints that come up most often, and offer my gut response to each.

“I just block trolls and only interact with thoughtful colleagues.”

Algorithms still shape what you see and influence how your posts get rewarded. Even carefully curated feeds exist within a system that promotes controversy, emotional reactivity, and engagement-enhancing performance. You can’t fully opt out of the architecture of algorithmic manipulation. You can only accept it.

“It’s the easiest way to meet collaborators and stay visible.”

Visibility is not the same as credibility or impact. Online networking often rewards those who perform accessibility or wit, not necessarily those producing rigorous scholarship, thereby reinforcing shallow reputational hierarchies instead of meaningful scientific discourse.

“It lets me communicate science directly to the public.”

Algorithmic distribution distorts that communication, amplifying controversy and suppressing nuance. Public outreach on social media is probably less educational than spectacle, where engagement metrics can easily deceive and posture as understanding.

“Social media democratizes visibility for those outside elite institutions.”

In practice, the same hierarchies carry over online. Algorithms favor those already prominent, fluent in dominant languages, and culturally attuned to what performs well (consciously or not). And those who make the algorithms happy will consistently have greater visibility. Structural inequities are still there.

“I get encouragement and useful feedback from peers and readers.”

Positive feedback loops are deceptive. They provide dopamine and instant gratification and can move us away from careful self-reflection. It can also push scholars toward performative self-presentation, without realizing it.

“Academic publishing is slow; this is a real-time conversation.”

Speed can come at the expense of reflection and accuracy. Social media replaces slow, cumulative discussion with reactive commentary that can fade away as quickly as it circulates.

“Posting about injustice or science helps raise awareness.”

Platforms simulate activism while discouraging real action. They reward performative outrage and symbolic gestures rather than coordinated, collective action, channeling energy back into engagement metrics. Taking the conversation or activity offline is what the algorithm most fears.

There’s nowhere else to reach such a large audience.

Scale is not the same as impact. Academic influence depends on credibility, context, and real attention, all of which social media systematically erodes. Large audiences don’t necessarily mean meaningful communication.

There are, of course, valid counterpoints to all of these takes. But I think the deeper problem isn’t just that algorithmic social media is a poor venue for scientific discourse. It’s that it is a convincing simulacrum of one. It gives you the feeling of building an audience, developing ideas, and engaging a community, while also crowding out the slower, more effortful forms of communication where those things happen better.

Some alternatives worth investing in instead:

Cultivate small-scale communication ecologies. What’s easy to overlook is that algorithmic platforms didn’t just bring more to our interactions. They are replacing and eliminating some alternative forms. Our attention is finite, and social media is designed to win that battle. Sometimes now, when I send a group email or text a photo or article to a handful of friends, it almost feels strange, like a relic of a different era. But recently, a senior scholar in my field, a true landmark figure, emailed two colleagues and me about a paper we’d published. It was so refreshing. We replied. He replied. There was a thread with a beginning, a few points of engagement, and a resolution. And just recently, I saw him at a conference. We were able to continue that conversation in person. And it was ours. It wasn’t a spectacle for the algorithm. That is a scientific relationship. A conversation driven by those conversing. There are also more formal alternatives worth investing in, such as small regional gatherings in my field, like FOSSILS, NEWEPS, the Northwest Evolutionary Science meeting, or the French Network for the Evolutionary Study of Humans. I’m still in a WhatsApp group from my postdoc institution. It’s mostly people asking who’s getting lunch or who got locked out of the building, but fairly often, something scientifically interesting comes up. And for the researchers currently there, they’re using it to have more drawn-out scientific conversations, which they can then take to lunch or the pub. Those communities don’t maintain themselves. They take a bit of leadership and a bit of investment. But what they offer is a conversation that belongs to the people in it, not to an algorithm.

Focus on depth over reach. Reinvest time spent chasing engagement into writing for long-form venues: Substack, Aeon, SAPIENS, The Conversation, Works in Progress, or university or personal blogs. Long-form writing allows for argument, reflection, and context, the features that make scholarship most meaningful. Even a thousand words can be enough to put an idea out there. Anyone can start a personal blog on their own website for free, including a DOI. Public comments are probably a good thing.

Engage public audiences directly. Work with schools, museums, or libraries. Give short talks or Q&As for students and community members. These forms of engagement are slower and reach a smaller audience, but are likely far more impactful. They build real relationships.

Use private or semi-private forums for discourse. Create online communities that are not optimized for “user engagement.” Things like moderated Slack, Zulip, or Discord groups, mailing lists, or Mastodon servers/Google groups organized around specific research interests. These allow dialogue without the messiness of metrics.

Practice slow outreach. Instead of reacting to trending topics, work on essays, podcasts, or short video explainers that communicate research clearly and on your own schedule. Communication can be scholarship, and need not be content. It’s no coincidence that blogs and vlogs are no longer a feature of our information and communication landscape. It’s not that they stopped being useful. It’s that ad-based, algorithmically driven platforms have developed a monopoly over our attention economies. Like moths to a flame.

Collaborate with journalists and editors. If visibility is a goal, partner with professionals who understand and value storytelling and the ethics of representation. Work to get your research to circulate through curated, editorially accountable platforms rather than algorithmic feeds.

Build micro-networks of mentorship and exchange. Use professional associations, graduate seminars, or small workshops as spaces of idea exchange. That was the original purpose of the scholarly community, before social media intermediated it.

In a recent episode of the Ezra Klein Show, guest Derek Thompson paraphrased Robert Putnam, the author of Bowling Alone, sharing an observation that has stayed with me. Putnam suggested that, too often, we adopt a technology, and then we adopt that technology’s values, without thinking about how to incorporate that technology into our own values. Scientists are generally pretty sensitive to costs and benefits. But I don’t think we’re always as honest with ourselves about the values embedded in the platforms we use, and the subtle ways by which those values begin to shape not only how we communicate science, but how we think about it, and perhaps how we think.