Conference Agenda (All times are shown in Greenwich Mean Time (GMT) unless otherwise noted)

Polarization of views (known as ideological polarization) is one of the greatest societal challenges of our time, potentially sewing distrust and hate among individuals and communities and threatening to undermine the fabric of democracy. Divisive issues such as abortion and gun control are ever-present and can drive issue polarization, and even affective polarization—a disdain for ‘the other side,’ which can further divide society. Social media has been flagged as a breeding ground for polarized views, with private groups and personalized algorithms facilitating self-creation of echo chambers that may lead to polarization. While there is prior research on the technological influences on view strengthening, scant Human-centered research exists and most of it has focused on view change in general, rather than view strengthening specifically. To investigate peoples’ experiences of view strengthening on social media, we interviewed 10 people who recently strengthened their views on important topics. While some took steps to avoid creating echo chambers (e.g., by seeking out opposing views), others intentionally created them to allow their views to strengthen without interference. These findings have important implications for designing social media platforms that support careful and conscious view strengthening while mitigating against the risk of information manipulation.

The fear of crime is an emotional response individuals have toward crime or the anticipation related to being the victim of crime. The increasing exposure to crime information presents considerable risks to people’s psychological health and well-being. Nevertheless, the fear of crime in online discourses is under-researched despite abundant conversations about crime. This work presents a mixed-methods study to comprehend how people disclose the fear of crime and what linguistic content or cues are associated with the fear. We gathered conversations about crime in the Baltimore subreddit. The content analysis revealed a necessity to differentiate between "experienced" and "expressive" fear of crime. The regression modeling identified strong factors related to the fear of crime, such as negative sentiment, objective expression, and first-person pronouns. This work extends the conceptualization of the fear of crime in online discourses and suggests potential ways to detect the fear automatically.

The mutual causal interdependence between offensive language use and negative emotions has been largely underexplored in the public event. Using 784,179 posts about the Tangshan violence event collected from Sina Weibo, nine themes were recognized based on framing theory. The mutual causal relationship between offensive language and negative emotions under each theme was examined through Convergent Cross Mapping. Results suggested that the mutual causal relationships between offensive language and negative emotion intensity under various themes were different with bidirectional causality under moral judgement, emotional venting, and power conflict, unidirectional causality under the vulnerable framework and the trust framework, and no causality under other themes. More detailed examination revealed special bidirectional or unidirectional causality between offensive language and some fine-grained negative emotions under the vulnerable framework, the trust framework, and secondary opinion. This study provides insight into the interaction between offensive language and negative emotions and helps emergency managers make targeted strategies to solve the problems of offensive language use and negative emotions.

Twitter has emerged as an important forum for discussion among academic librarians. We took a mixed-methods approach to study the thematic content and sentiment of tweets authored by academic librarians in the United States, Canada, and the United Kingdom. We found differences in the semantic content and themes present in the data from each country that point to differences in how librarians in each country engage on Twitter. We also present a methodological intervention by using two different sentiment analysis methods, VADER, and Zero Shot Learning, to classify posts by academic librarians. While more work remains to be done, we cast new light on how members of professional communities use social media. Our qualitative analysis identified 11 thematic categories in academic librarians’ Twitter discussions, with a focus on professional topics. U.K. librarians exhibited a higher frequency of labor- and employment-related terms compared to their U.S. and Canadian counterparts. Using the state-of-the-art Zero Shot Learning (ZSL) approach for sentiment classification, we significantly outperformed an off-the-shelf classifier like Vader. Sentiment ratios for U.S. and Canadian tweets were similar, while the U.K. displayed nearly double the positive-to-negative tweet ratio.