Influence Pathway Discovery on Social Media
Xinyi Liu, Ruijie Wang, Dachun Sun, Jinning Li, Christina Youn, You, Lyu, Jianyuan Zhan, Dayou Wu, Xinhe Xu, Mingjun Liu, Xinshuo Lei, Zhihao Xu,, Yutong Zhang, Zehao Li, Qikai Yang, Tarek Abdelzaher

TL;DR
This paper introduces a scalable influence pathway discovery algorithm on social media using unsupervised ideological embeddings, enabling analysis of influence dynamics across different political and social contexts.
Contribution
It presents a novel method combining ideological embeddings with influence analysis, allowing for scalable detection of influence pathways in social media data.
Findings
Identified influence pathways during the French election
Detected ideological shifts during Philippine political discussions
Analyzed influence patterns in Russian messaging during Ukraine conflict
Abstract
This paper addresses influence pathway discovery, a key emerging problem in today's online media. We propose a discovery algorithm that leverages recently published work on unsupervised interpretable ideological embedding, a mapping of ideological beliefs (done in a self-supervised fashion) into interpretable low-dimensional spaces. Computing the ideological embedding at scale allows one to analyze correlations between the ideological positions of leaders, influencers, news portals, or population segments, deriving potential influence pathways. The work is motivated by the importance of social media as the preeminent means for global interactions and collaborations on today's Internet, as well as their frequent (mis-)use to wield influence that targets social beliefs and attitudes of selected populations. Tools that enable the understanding and mapping of influence propagation through…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Computational and Text Analysis Methods · Complex Network Analysis Techniques
