论文标题
使用定量的话语分析在Twitter上检测繁殖的传播器
Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis
论文作者
论文摘要
外国参与者努力影响舆论的努力因其影响民主选举的潜力而引起了人们的关注。因此，识别和抵消虚假信息的能力越来越成为政府实体的重中之重，以保护民主进程的完整性。这项研究提出了一种使用中心共振分析和Clauset-Newman-Moore社区检测在Twitter上识别俄罗斯虚假信息机器人的方法。该数据反映了已知的俄罗斯虚假信息机器人与在2016年美国总统大选期间的Twitter用户的控制中有很大的话语差异。数据还证明了基于社区聚类的统计学意义分类功能（MCC = 0.9070）。预测算法在识别真正的阳性（bot）方面非常有效，但由于控制用户之间缺乏话语相似性，因此无法解决真正的负面影响（非机器人）。这导致了一种高度敏感的手段，可以在Twitter上以高度的话语相似性来识别虚假信息的传播者，这意味着限制了可能影响民主进程的虚假信息的传播。
Efforts by foreign actors to influence public opinion have gained considerable attention because of their potential to impact democratic elections. Thus, the ability to identify and counter sources of disinformation is increasingly becoming a top priority for government entities in order to protect the integrity of democratic processes. This study presents a method of identifying Russian disinformation bots on Twitter using centering resonance analysis and Clauset-Newman-Moore community detection. The data reflect a significant degree of discursive dissimilarity between known Russian disinformation bots and a control set of Twitter users during the timeframe of the 2016 U.S. Presidential Election. The data also demonstrate statistically significant classification capabilities (MCC = 0.9070) based on community clustering. The prediction algorithm is very effective at identifying true positives (bots), but is not able to resolve true negatives (non-bots) because of the lack of discursive similarity between control users. This leads to a highly sensitive means of identifying propagators of disinformation with a high degree of discursive similarity on Twitter, with implications for limiting the spread of disinformation that could impact democratic processes.
