Content Moderation As a Political Issue: The Twitter Discourse Around Trump's Ban

Content moderation — the regulation of the material that users create and disseminate online — is an important activity for all social media platforms. While routine, this practice raises significant questions linked to democratic accountability and civil liberties. Following the decision of many platforms to ban Donald J. Trump in the aftermath of the attack on the U.S. Capitol in January 2021, content moderation has increasingly become a politically contested issue. This paper studies that process with a focus on the public discourse on Twitter. The analysis includes over 9 million tweets and retweets posted by over 3 million unique users between January 2020 and April 2021. First, the salience of content moderation was driven by left-leaning users, and "Section 230" was the most important topic across the ideological spectrum. Second, stance towards Section 230 was relatively volatile and increasingly polarized. These findings highlight relevant elements of the ongoing process of political contestation surrounding this issue, and provide a descriptive foundation to understand the politics of content moderation

Model evaluation for extreme risks

Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security

“Be Nice or Leave Me Alone”: An Intergroup Perspective on Affective Polarization in Online Political Discussions

Affective polarization—growing animosity and hostility between political rivals—has become increasingly characteristic of Western politics. While this phenomenon is well-documented through surveys, few studies investigate whether and how it manifests in the digital context, and what mechanisms underpin it. Drawing on social identity and intergroup theories, this study employs computational methods to explore to what extent political discussions on Reddit’s r/politics are affectively polarized, and what communicative factors shape these affective biases. Results show that interactions between ideologically opposed users were significantly more negative than like-minded ones. These interactions were also more likely to be cut short than sustained if one user referred negatively to the other’s political in-group. Conversely, crosscutting interactions in which one of the users expressed positive sentiment toward the out-group were more likely to attract a positive than a negative response, thus mitigating intergroup affective bias. Implications for the study of online political communication dynamics are discussed

The paradox of poor representation: How voter–party incongruence curbs affective polarisation

Research on the relationship between ideology and affective polarisation highlights ideological disagreement as a key driver of animosity between partisan groups. By operationalising disagreement on the left–right dimension, however, existing studies often overlook voter–party incongruence as a potential determinant of affective evaluations. How does incongruence on policy issues impact affective evaluations of mainstream political parties and their leaders? We tackle this question by analysing data from the British Election Study collected ahead of the 2019 UK General Election using an instrumental variable approach. Consistent with our expectations, we find that voter–party incongruence has a significant causal impact on affective evaluations. Perceived representational gaps between party and voter drive negative evaluations of the in-party and positive evaluations of the opposition, thus lowering affective polarisation overall. The results offer a more nuanced perspective on the role of ideological conflict in driving affective polarisation