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Detecting Pivotal Points in Social Conflicts via Topic Modeling of Twitter Content

机译：通过Twitter内容主题建模检测社会冲突中的关键点

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The linkages between intensity and topicality of online discussions, on one hand, and those of offline on-street political activity, on the other hand, have recently become a subject of studies around the world. But the results of quantitative assessment of causal relations between onsite and online activities of citizens are contradictory. In our research, we use conflicts with violent triggers and the subsequent lines of events that include street rallies, political manifestations, and/or peaceful mourning, as well as public political talk, to trace the pivotal points in the conflict via measuring Twitter content. We show that in some cases Granger test does not work well, like in the case of Cologne mass harassment, for detecting the causality between online and onsite activities. In order to suggest a way to qualitatively assess the linkages between online and offline activities of users, we deploy topic modeling and further qualitative assessment of the changes in the topicality to link the topic saliency to the time of offline events. We detect several periods with varying topicality and link them to what was going on in the offline conflict.
机译：另一方面，在线讨论的强度和主题之间的联系，另一方面，离线街道政治活动最近成为世界各地研究的主题。但是，公民现场与在线活动之间的因果关系的定量评估结果是矛盾的。在我们的研究中，我们使用与暴力触发的冲突以及随后的事件，包括街头集会，政治表现和/或和平哀悼，以及公共政治谈判，通过测量Twitter内容来追踪冲突中的关键点。我们表明，在某些情况下，格兰杰考试不起作用，就像在科隆群众骚扰的情况一样，用于检测在线和现场活动之间的因果关系。为了建议使用用户的在线和离线活动之间的联系方式，我们部署主题建模和进一步定性评估题目中的更改，以将主题显着性与离线事件的时间联系起来。我们检测到几个不同的题词，并将它们链接到离线冲突中发生的事情。

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