Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence.
翻译:大规模在线运动,无论是恶意还是其他,都需要参与者之间大力协调,这引起了对协调在线行为研究的兴趣。 最新的发现协调行为的最新方法进行静态分析,无视协调的时间动态。 在这里,我们首次对协调行为进行动态分析。为了实现我们的目标,我们建立一个多重时间网络,并进行动态社区检测,以识别及时表现出协调行为的用户群体。 由于我们的新做法,我们发现:(一) 协调社区具有不同程度的时间不稳定性;(二) 需要动态分析来说明这种不稳定性,静态分析的结果可能不可靠,几乎不能代表不稳定社区;(三) 一些用户表现出具有重要实际影响的截然不同的拱门行为;(四) 内容和网络特征有助于解释用户为何离开和加入协调社区。我们的成果显示了动态分析的好处,并打开了对在线辩论的展开、协调社区的战略以及在线影响模式进行的新研究方向。