分析X的影响网络：通过传递熵与多层网络测度从可信度和流行度角度剖析新闻传播动态
Analyzing X's Web of Influence: Dissecting News Sharing Dynamics through Credibility and Popularity with Transfer Entropy and Multiplex Network Measures
AI总结：
本研究针对X平台大规模网络中隐藏影响者难识别的问题，采用改进的传递熵非参数框架结合多层网络测度，分析三类重大事件中用户的新闻传播影响模式，发现可信新闻传播者偶尔使用不可信来源的不对称策略规律。
摘要：
新闻文章在社交媒体平台上的传播会显著影响公众对全球议题的认知，而这些文章的属性在可信度和流行度上存在差异。衡量这种影响并识别关键传播者是一项极具挑战性的任务。传统的基于图的指标（如各类中心性测度和节点度方法）虽能为信息流动提供一定见解，但不足以识别X（前身为Twitter）这类大规模社交媒体网络中的隐藏影响者。本研究采用并改进了一种基于传递熵的非参数框架，以厘清X用户之间的影响关系。研究进一步通过在社交媒体场景中创新性地运用多层网络测度，对这些主体施加的影响分布进行分类，旨在定位重大国际事件中的有影响力主体。该方法被应用于三个不同事件，研究结果显示，不同事件中的主体会利用不同类型的新闻文章，并根据新闻类别影响不同的主体群体。值得注意的是，我们发现传播可信新闻文章以影响他人的主体偶尔会借助不可信来源。然而，相反的情况——即主要使用不可信新闻类型的主体转而采用可信来源施加影响——则较少出现。这种不对称性表明，在社交媒体网络中策略性使用新闻文章施加影响存在可辨识的模式，凸显了可信度和流行度在信息与影响传播过程中的微妙作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
