Analyzing X's Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures

TL;DR

The paper tackles the problem of how credibility and popularity of news articles shape influence on the X platform. It introduces a non-parametric Transfer Entropy framework to build directed influence networks and augments this with multiplex layers that categorize edges by credibility (TM, UM, TF, UF) and popularity, applied to three events (Skripal, Navalny, Ukraine). The approach enables identification of hidden influencers and a nuanced analysis of cross-type influence via multiplex measures and pairwise co-occurrence. Findings show asymmetries in how trustworthy versus untrustworthy sources are used across events, with trustworthy actors sometimes diversifying into untrustworthy sources, and untrustworthy actors tending to focus on a narrower set of sources; the Ukraine event in particular exhibits concentrated trustworthy influence. Overall, the combination of Transfer Entropy and multiplex networks provides new insights into influence propagation and the strategic use of news credibility and popularity on social media.

Abstract

The dissemination of news articles on social media platforms significantly impacts the public's perception of global issues, with the nature of these articles varying in credibility and popularity. The challenge of measuring this influence and identifying key propagators is formidable. Traditional graph-based metrics such as different centrality measures and node degree methods offer some insights into information flow but prove insufficient for identifying hidden influencers in large-scale social media networks such as X (previously known as Twitter). This study adopts and enhances a non-parametric framework based on Transfer Entropy to elucidate the influence relationships among X users. It further categorizes the distribution of influence exerted by these actors through the innovative use of multiplex network measures within a social media context, aiming to pinpoint influential actors during significant world events. The methodology was applied to three distinct events, and the findings revealed that actors in different events leveraged different types of news articles and influenced distinct sets of actors based on the news category. Notably, we found that actors disseminating trustworthy news articles to influence others occasionally resort to untrustworthy sources. However, the converse scenario, wherein actors predominantly using untrustworthy news types switch to trustworthy sources for influence, is less prevalent. This asymmetry suggests a discernible pattern in the strategic use of news articles for influence across social media networks, highlighting the nuanced roles of trustworthiness and popularity in the spread of information and influence.

Figures (5)

• Figure 1: The full framework pipeline of transfer entropy with multiplex networks for analyzing influential actors' behavior
• Figure 2: Construction of the Multiplex Network for TM Actors Across Four Layers Demonstrating the Direction of Influence Towards Various Types of Targets
• Figure 3: Comparing the distribution of TM, TF, UM, and UF sources on four different type of targets in Skripal, Navalny, and Ukraine events
• Figure 4: The participant coefficient of TM, TF, UM, and UF actors in their corresponding multiplex networks in Skripal, Navalny, and Ukraine events Note: The scales on the X-axis are adjusted according to the highest influence in the multiplex network. A single scale was not used in order to highlight the diversity of influence and to distinguish the distributions.
• Figure 5: The pairwise co-occurrence of influential actors within a multiplex network being active in other types of influential multiplex networks The asterisk (*) in the heatmaps signifies that influence extended to all types of targets (TM, TF, UM, UF)