Social media companies including Meta have pulled back on efforts to protect voters from election-meddling lies
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
Google research shows the fast rise of AI-generated misinformation | CBC News
From fake images of war to celebrity hoaxes, AI technology has spawned new forms of reality-warping misinformation online. New analysis co-authored by Google researchers shows just how quickly the problem has grown.
SLOPAGANDA! How to combat MAGA’s AI manipulation POISONING politics
Political disinformation concentrates in repeated claims across media
The prevalence and dynamics of disinformation remain contentious, partly because measures have relied on posts containing URLs from untrustworthy sources. We move beyond this by detecting disinformation in text and across posts. Using retrieval-augmented generation, we match six million Facebook, Instagram, and X posts by politicians from the United States, United Kingdom, and Germany, and two million articles from news outlets to fact-checked claims. We show that disinformation is concentrated in a small number of combinations of policy issues and parties and that just over hundred distinct claims account for around half of all disinformation across the three countries. They often appear first on a single platform, later on multiple, and subsequently in news coverage before being fact-checked. These findings suggest that disinformation is concentrated in a narrow segment of political communication, potentially creating repeated exposure that may shape the terms of public debate through both social and news media.
Brazil's Lula accuses social media giant Meta of election interference amid tight run-off campaign
Social media platform Meta has 48 hours to respond to claims by Brazil's leftwing candidate and incumbent President Luiz Inacio Lula da Silva's campaign accusing the tech platform of blocking content…
Why is Meta destroying its engineering organization?
Leadership at the social media giant has been on an AI-fueled rampage through its engineering org. We report what’s happened
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.
Generative propaganda: Evidence of AI’s impact from a state-backed disinformation campaign
Abstract Can AI bolster state-backed propaganda campaigns, in practice? Growing use of AI and large language models has drawn attention to the potential for accompanying tools to be used by malevolent actors. Though recent laboratory and experimental evidence has substantiated these concerns in principle, the usefulness of AI tools in the production of propaganda campaigns has remained difficult to ascertain. Drawing on the adoption of generative-AI techniques by a state-affiliated propaganda site with ties to Russia, we test whether AI adoption enabled the website to amplify and enhance its production of disinformation. First, we find that the use of generative-AI tools facilitated the outlet’s generation of larger quantities of disinformation. Second, we find that use of generative-AI coincided with shifts in the volume and breadth of published content. Finally, drawing on a survey experiment comparing perceptions of articles produced prior to and following the adoption of AI tools, we show that the AI-assisted articles maintained their persuasiveness in the postadoption period. Our results illustrate how generative-AI tools have already begun to alter the size and scope of state-backed propaganda campaigns.
We're getting the social media crisis wrong
The bigger problem isn't disinformation. It's degraded democratic publics
We're getting the social media crisis wrong
The bigger problem isn't disinformation. It's degraded democratic publics
We're getting the social media crisis wrong
The bigger problem isn't disinformation. It's degraded democratic publics
Google Researchers Say AI Now Leading Disinformation Vector (and Are Severely Undercounting the Problem)
It’s much easier to produce AI-generated disinformation than it is to fact check it.
Social media is populist and polarising; AI may be the opposite
Large language models elevate expert consensus and moderate views, in sharp contrast to social platforms
How AI Will Reshape Public Opinion
Social media democratised public opinion, shifting influence away from elites and experts to ordinary people. LLMs will partly reverse this trend. They are a powerful, new technocratising force.
Rational Silence and False Polarization: How Viewpoint Organizations and Recommender Systems Distort the Expression of Public Opinion
Social media platforms are one of the most important domains in which artificial intelligence (AI) has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive attention has been devoted to the polarization that social media platforms appear to facilitate. However, a key implication of the transformation we are experiencing due to these AI-powered platforms has received much less attention: how platforms impact what observers of online discourse come to believe about community views. These observers include policymakers and legislators, who look to social media to gauge the prospects for policy and legislative change, as well as developers of AI models trained on large-scale internet data, whose outputs may similarly reflect a distorted view of public opinion. In this paper, we present a nested game-theoretic model to show how observed online opinion is produced by the interaction of the decisions made by users about whether and with what rhetorical intensity to share their opinions on a platform, the efforts of viewpoint organizations (such as traditional media and advocacy organizations) that seek to encourage or discourage opinion-sharing online, and the operation of AI-powered recommender systems controlled by social media platforms. We show that signals from ideological viewpoint organizations encourage an increase in rhetorical intensity, leading to the rational silence of moderate users. This, in turn, creates a polarized impression of where average opinions lie. We also show that this observed polarization can also be amplified by recommender systems that, pursuant to a platform’s incentive to maximize engagement, encourage the formation of viewpoint communities online that end up seeing a skewed sample of opinion. Unlike existing models, these well-known online phenomena are not here attributed to distortion in the formation of opinions nor to the seeking out of like-minded others, but rather to the interaction of the incentives of users, viewpoint organizations, and platforms implementing recommender systems. In addition to showing how these interactions can play out in simulations, we also identify practical strategies platforms can implement, such as reducing exposure to signals from ideological viewpoint organizations and a tailored approach to content moderation.