We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information. Besides discovering information flow patterns among users, we construct communities among users and develop the propagation forecasting capability, enabling tracing and understanding of how information is disseminated at a higher level. A demo video and more are available at https://info-pathways.github.io.

Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable.Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications.To address these limitations, in this work, we propose a computational approach to efficiently model users' latent susceptibility levels.As shown in previous work, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people's reposting behavior on social media.To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people's sharing behavior.Using COVID-19 as a testbed, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach.Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors.Our analysis reveals that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation, and shows that susceptibility is unevenly distributed across different professional and geographical backgrounds. 1

Growing concerns have been raised about the detrimental effects of uncivil comments on the web towards democracy. However, there is still a lack of understanding about online incivility's nuanced and complicated nature and its impact on conversation development and user behaviors. This work aims to fill that research gap by modeling incivility and its relationship to political discussions. We develop a comprehensive and fine-grained taxonomy that characterizes incivility with vulgarity, name-calling (inter-personal and third-party attacks), aspersion, and stereotypes, and then apply the framework to quantify the level of each incivility category in over 40 million comments from Reddit. Using large-scale quantitative analysis, we investigate the types of interactions and contexts in which incivility is more likely to occur, model how incivility shapes subsequent conversations, and examine user engagement patterns and behavioral changes after exposure to incivility. Our findings show that conversations that start out uncivil tend to become more uncivil in responses, and exposure to different incivility categories has differing effects on community members' engagement. We conclude with the implications of our research in assisting the design and moderation of online political communities.