Clustering X Users based on Changes in Sentiment in Response to Conflict

Oregon Convention Center 

Controversial posts from social media users during major national or international events reflect real-time reactions to topics discussed on the platform. Capturing shifts in public morale from social media posts can effectively detect public perceptions of particular events and help guide public responses. Assessing changes in sentiment of social media posts from users who are opinion leaders can help evaluate the impact of information disseminated in the cycle. We collected user tweets from Brandwatch, transformed the observed sentiment series into transition pairs, and utilized sparse multivariate functional data analysis to model the transitions among sentiments from Twitter posts on conversations related to Israel-Hams conflict, reflecting the immediate effects of information diffusion. Rather than solely considering users' sentiments over time, this work focuses on switches in sentiment that represent an instant reaction toward the information consumed from social media platforms. We aim to provide interpretable clustering memberships representing reactions to information from different opinions to a specific crisis event over time, and identify potential individuals who are amenable to the information diffusion. This approach can be generalized to cluster users from any social media platform for any event, and it is implemented in the \texttt{clustersc()} function from the \texttt{R} package \texttt{catfda}.

shift

crisis event

markov chain

sparse multivariate functional data

Social Media 

Section on Nonparametric Statistics