This project, run through ARLIS, investigates how emotion impacts information propagation and sharing in Polish and Lithuanian social media. Teams of in-country annotators independently assess each post, including all multimedia content, for over 22 distinct emotions inclusively on 0 to 100 scales--meaning that the same post will have different scores for different emotions. We will conduct multi-level statistical analyses of the impact of emotions, specific topics, and account and message features on social media sharing and engagement.
Developing novel techniques - through the application of state-of-the-art machine learning - to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems.
Online communities, from question and answer sites to general-purpose discussion forums, are increasingly working to solve hard problems together, often through a process of open sharing and discussion of ideas and information.
Data shared by 30M+ Facebook users world-wide will enable earlier detection of COVID-19 outbreaks, help communities to prepare, inform policymakers, and reveal patterns in effective preventative measures.
Xiaoyun is currently exploring how people present themselves across their social media ecosystems and what factors could influence their self-presentation.
