Bruce Desmarais

Xinyu Wang, Emma Carpenetti, Bruce Desmarais et al.

Unlike the more observable phenomenon of group opinion reinforcement, self-censorship online has received comparatively less attention. Our goal in this work is to dissect the phenomena of self-censorship and to examine the implications of restrained expression for participation in public discourse, particularly in polarized contexts. We explore how social media users express their opinions online through analyses of 390 survey responses and 20 semi-structured interviews using a mixed-methods approach. We ask social media users about the differences between their publicly shared opinions and privately held beliefs, highlighting the influence of contextual factors on self-expression. Our findings show that self-censorship is associated with community context; social media users embedded within larger audiences, with lower posting frequency and perceived support, are less likely to express their opinions, and those who do speak often adjust their expressed views to align with perceived group norms. The study complements the rich literature on echo chambers and opinion reinforcement on social media platforms, highlighting the silence within the noise and its potential consequences for public discourse, which have become increasingly pertinent in an era where online platforms are pivotal to social and political narratives.

Yuehong Cassandra Tai, Khushi Navin Patni, Nicholas Daniel Hemauer et al.

Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the veracity of content. We evaluate multiple GenAI models across tasks that involve the rating of, and perceived reasoning about, the credibility of information. The information in our experiments comes from content that subnational U.S. politicians post to Facebook. We find that GPT-4o, one of the most used AI models in consumer applications, outperforms other models, but all models exhibit only moderate agreement with human coders. Importantly, even when GenAI models accurately identify low-credibility content, their reasoning relies heavily on linguistic features and ``hard'' criteria, such as the level of detail, source reliability, and language formality, rather than an understanding of veracity. We also assess the effectiveness of summarized versus full content inputs, finding that summarized content holds promise for improving efficiency without sacrificing accuracy. While GenAI has the potential to support human fact-checkers in scaling misinformation detection, our results caution against relying solely on these models.