Abstract Number:
3183
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Xiaoxia Champon (1), Leonard Stefanski (1), Ana-Maria Staicu (1), Chathura Jayalah (2), William Rand (1), Ivan Garibay (3)
Institutions:
(1) North Carolina State University, N/A, (2) University of Central Florida, Orlando, FL, (3) University of Central Florida, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Posts from social media users during major events reflect real-time reactions to topics discussed on the platform. Capturing the shift in sentiment over time can effectively detect public perceptions of a particular event and help guide public responses. Assessing changes in sentiment from opinion leaders, who are ordinary users, can help evaluate the impact of information disseminated in the cycle. We use continuous-time Markov decision processes combined with non-parametric intensity functions from Poisson point processes to model the immediate effects of information diffusion. The method is applied to data related to the Israel-Hamas conflict from Twitter. Rather than solely considering users' sentiment over time, this work focuses on changes that reflect the effect of the information consumed from social media platforms. We aim to provide interpretable clustering memberships representing reactions over information from different opinion on a specific event over time. This approach can be generalized to cluster users from any social media platform for any event and will be implemented in the mkpoisson function from the R package catfda.
Keywords:
shift|crisis event|markov chain|sparse multivariate functional data|Social Media|
Sponsors:
Section on Nonparametric Statistics
Tracks:
Applications of nonparametric methods
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