Do social media threaten democracy?

Facebook, Google and Twitter were supposed to save politics as good information drove out prejudice and falsehood. Something has gone very wrong

Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study

Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.

Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study

Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.

Shifts in U.S. Social Media Use, 2020-2024: Decline,...

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper traces how the U.S. social media landscape has shifted across platforms,...

The Pulse of The Planet, Flatlined: Why Twitter's Is Failing to Grow

No, it's not rampant abuse, weak leadership, or a product that intimidates new users. The roots of Twitter's stagnation go back to 2010.

Custodians of the Internet

A revealing and gripping investigation into how social media platforms police what we post online—and the large societal impact of these decisions Most use...

Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper describes how U.S. social media use has shifted across platforms, demographics, and politics. Overall platform reach declined, driven by growth in the share of Americans — especially the youngest and oldest cohorts — who report using no social media. Visiting and posting activity on Twitter/X and Facebook have fallen by nearly 50% since 2020, with the decline on Twitter/X driven primarily by reduced participation among Democratic users. While Facebook, YouTube, and Twitter/X lost ground, TikTok and Reddit grew modestly, consistent with a more fragmented digital public sphere. Platform audiences aged and became slightly more educated and racially diverse. Politically, most platforms shifted toward Republican users while remaining, on balance, Democratic-leaning. Twitter/X experienced the largest change: among posters, the partisan balance swung over 70 percentage points from Democrats to Republicans. Across platforms, political posting remains closely tied to affective polarization, as the most partisan respondents are also the most active. As casual users disengage while polarized partisans remain vocal, online discourse becomes narrower and more ideologically extreme.

How Twitter Found Its Money Mojo

The not-so-dismal science of Twitternomics

Fact-checking an engagement drop on MAGA pages because of the Yandex data center strike | Joohn Choe

Zelenskyy tweet: https://x.com/ZelenskyyUa/status/2109602415397749044?s=20 Res Futura Data House tweet: https://x.com/Polityka_wSieci/status

Did Ukrainian strikes on Russian datacentres reduce online disinformation?

While some viral online graphs suggest less bot activity, Zelenskyy’s claims of improved social media discourse do not entirely pass muster

Was their any substantial backlash to the internet when it first started becoming popular? I can imagine that going from a comparatively disconnected world to one where instantaneous global connection 24/7 were commonplace was a rather massive (and not always positive) shift in many ways.

2.7M subscribers in the AskHistorians community. The Portal for Public History. Please read the rules before participating, as we remove all comments…

Do links hurt news publishers on Twitter? Our analysis suggests yes

Engagement for tweets from @nytimes (53 million followers) is dwarfed by engagement for tweets from @GlobeEyeNews (866,000 followers).

Facebook-Cambridge Analytica: A timeline of the data hijacking scandal

Facebook is desperately trying to mend its damaged reputation following the continued fallout of a massive data scandal.