  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.
