Companies allowed more harmful content on user’s feeds, knowing their algorithms ran on outrage, BBC hears.
The political effects of X’s feed algorithm
Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.
The political effects of X’s feed algorithm
Feed algorithms are widely suspected to influence political attitudes. However, previous evidence from switching off the algorithm on Meta platforms found no political effects1. Here we present results from a 2023 field experiment on Elon Musk’s platform X shedding light on this puzzle. We assigned active US-based users randomly to either an algorithmic or a chronological feed for 7 weeks, measuring political attitudes and online behaviour. Switching from a chronological to an algorithmic feed increased engagement and shifted political opinion towards more conservative positions, particularly regarding policy priorities, perceptions of criminal investigations into Donald Trump and views on the war in Ukraine. In contrast, switching from the algorithmic to the chronological feed had no comparable effects. Neither switching the algorithm on nor switching it off significantly affected affective polarization or self-reported partisanship. To investigate the mechanism, we analysed users’ feed content and behaviour. We found that the algorithm promotes conservative content and demotes posts by traditional media. Exposure to algorithmic content leads users to follow conservative political activist accounts, which they continue to follow even after switching off the algorithm, helping explain the asymmetry in effects. These results suggest that initial exposure to X’s algorithm has persistent effects on users’ current political attitudes and account-following behaviour, even in the absence of a detectable effect on partisanship.
AI-powered hate content is on the rise, experts say | CBC News
Experts say that artificial intelligence technology is allowing for a rapid increase in the amount of hateful content and misinformation online.
Changes to the Facebook Algorithm Decreased News Visibility Between 2021-2024
Platforms, especially Facebook, are primary news sources in the US. In its widely criticized "War on News," Meta algorithmically deprioritized news and political content. We use data from 40 news organizations (5,243,302 Facebook posts, 7,875,372,958 user reactions) and 21 non-news pages (396,468 posts; 1,909,088,308 reactions) between January 1, 2016 and February 13, 2025 to examine how these changes influenced news visibility on the platform. Reactions to news declined by 78% between 2021 and 2024 while reactions to non-news pages increased, indicating targeted suppression of news visibility. Low-quality sources were especially suppressed, yet the 2025 end to "War on News" increased user reactions to news, especially low-quality ones. These changes do not reflect decreased news supply, Facebook user base, or interest in news over this period.
Redesigning algorithms to intervene on social norm misperceptions during a national election
For the first time in history, civic discourse commonly occurs in digital environments in which algorithms influence exposure to social information1,2. It is increasingly important to understand whether and how these algorithms affect political discourse3–5. Here we built custom feed-ranking algorithms with full control over their features, and randomly assigned 2,000 participants to use them for 8 weeks (before and after the 2024 US presidential election). We tested whether an engagement-based algorithm (used on major social media platforms6,7) amplifies intergroup, moralized and emotional (IME) information in ways that skew perceptions of social norms around political dialogue5,8, and whether it increased engagement with IME content and perceptions of partisan animosity (compared with a reverse-chronological feed9,10). We also developed and tested a ‘diversified extremity’ algorithm to reduce the influence of extreme users11–13 to improve the accuracy of social norm perception14–16 and reduce perceptions of partisan animosity. We found that engagement-based feeds amplified IME and toxic content relative to reverse-chronological feeds, with the largest increases in moral outrage and political content. Engagement-based feeds also reduced prescriptive norm perception accuracy (albeit in an unexpected direction) and increased perceived partisan animosity. However, they did not significantly alter users’ own engagement behaviours. The diversified extremity algorithm reduced IME and toxic content exposure, improved prescriptive norm accuracy, yet maintained comparable platform enjoyment—suggesting that reducing the influence of extreme users can curb algorithmic distortions without diminishing user experience.
Bridging-Based Ranking
There is significant concern about the engagement-based ranking systems used by TikTok, Facebook, YouTube, etc. to recommend content. Bridging-based ranking systems can address one of the most dangerous aspects of such algorithmic recommendations—the push toward polarization and divisiveness that is tearing nations apart—and do so without reducing anonymity or increasing censorship. This report explores what bridging-based ranking is, how it helps (overcoming downsides of chronological feeds and middleware), addresses common objections, and provides early examples of its use and benefits in the wild. The report concludes by providing next steps for platforms, governments, funders, and researchers in order to accelerate the deployment of bridging.
Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field ...
Social media, extremism, and radicalization
Fears that YouTube recommendations radicalize users are overblown, but social media still host and profit from dubious and extremist content.
“Influencing the influencers:” a field experimental approach to promoting effective mental health communication on TikTok
A substantial body of social scientific research considers the negative mental health consequences of social media use on TikTok. Fewer, however, consider the potentially positive impact that mental health content creators (“influencers”) on TikTok can have to improve health outcomes; including the degree to which the platform exposes users to evidence-based mental health communication. Our novel, influencer-led approach remedies this shortcoming by attempting to change TikTok creator content-producing behavior via a large, within-subject field experiment (N = 105 creators with a reach of over 16.9 million viewers; N = 3465 unique videos). Our randomly-assigned field intervention exposed influencers on the platform to either (a) asynchronous digital (.pdf) toolkits, or (b) both toolkits and synchronous virtual training sessions that aimed to promote effective evidence-based mental health communication (relative to a control condition, exposed to neither intervention). We find that creators treated with our asynchronous toolkits—and, in some cases, those also attending synchronous training sessions—were significantly more likely to (i) feature evidence-based mental health content in their videos and (ii) generate video content related to mental health issues. Moderation analyses further reveal that these effects are not limited to only those creators with followings under 2 million users. Importantly, we also document large system-level effects of exposure to our interventions; such that TikTok videos featuring evidence-based content received over half a million additional views in the post-intervention period in the study’s treatment groups, while treatment group mental health content (in general) received over three million additional views. We conclude by discussing how simple, cost-effective, and influencer-led interventions like ours can be deployed at scale to influence mental health content on TikTok.
In the face of rampant AI, is ‘data poisoning’ a new form of civil disobedience?
Boycotts, sabotage and other types of civil disobedience have long served collective action against injustice.
In the face of rampant AI, is ‘data poisoning’ a new form of civil disobedience?
Boycotts, sabotage and other types of civil disobedience have long served collective action against injustice.
Why the Algorithm Loves a Villain, And How to Beat It
When the internet is full of distortions, fake news, and AI-generated slop, how can facts and journalism rise to the top? Former BBC and Vice journalist Sophia Smith Galer has one possible way to…
X's Algorithm Feeds Off Ragebait and Impacts Democrats More, Study Finds
X is driving engagement by making users fight in the replies.
Elon Musk's Grokipedia Pushes Far-Right Talking Points
The new AI-powered Wikipedia competitor falsely claims that pornography worsened the AIDS epidemic and that social media may be fueling a rise in transgender people.
Everyone Cheering The Social Media Addiction Verdicts Against Meta Should Understand What They’re Actually Cheering For
First things first: Meta is a terrible company that has spent years making terrible decisions and being terrible at explaining the challenges of social media trust & safety, all while prioritiz…
