Center of Excellence for Complex Systems Research, Faculty of Physics, Warsaw University of Technology, Warsaw, Poland.
PLoS One. 2011;6(7):e22207. doi: 10.1371/journal.pone.0022207. Epub 2011 Jul 27.
E-communities, social groups interacting online, have recently become an object of interdisciplinary research. As with face-to-face meetings, Internet exchanges may not only include factual information but also emotional information--how participants feel about the subject discussed or other group members. Emotions in turn are known to be important in affecting interaction partners in offline communication in many ways. Could emotions in Internet exchanges affect others and systematically influence quantitative and qualitative aspects of the trajectory of e-communities? The development of automatic sentiment analysis has made large scale emotion detection and analysis possible using text messages collected from the web. However, it is not clear if emotions in e-communities primarily derive from individual group members' personalities or if they result from intra-group interactions, and whether they influence group activities.
METHODOLOGY/PRINCIPAL FINDINGS: Here, for the first time, we show the collective character of affective phenomena on a large scale as observed in four million posts downloaded from Blogs, Digg and BBC forums. To test whether the emotions of a community member may influence the emotions of others, posts were grouped into clusters of messages with similar emotional valences. The frequency of long clusters was much higher than it would be if emotions occurred at random. Distributions for cluster lengths can be explained by preferential processes because conditional probabilities for consecutive messages grow as a power law with cluster length. For BBC forum threads, average discussion lengths were higher for larger values of absolute average emotional valence in the first ten comments and the average amount of emotion in messages fell during discussions.
CONCLUSIONS/SIGNIFICANCE: Overall, our results prove that collective emotional states can be created and modulated via Internet communication and that emotional expressiveness is the fuel that sustains some e-communities.
电子商务社区是一种在线互动的社交群体，最近已成为跨学科研究的对象。与面对面的会议一样，互联网交流不仅可以包含事实信息，还可以包含情感信息——参与者对讨论主题或其他小组成员的感受。反过来，人们知道情绪在许多方面对线下交流中的互动伙伴有重要影响。互联网交流中的情绪会影响他人，并系统地影响电子商务社区轨迹的数量和质量方面吗？自动情感分析的发展使得使用从网络收集的文本消息进行大规模情感检测和分析成为可能。但是，尚不清楚电子商务社区中的情绪主要源自个别小组成员的个性，还是源自小组内部的互动，以及它们是否会影响小组活动。
方法/主要发现：在这里，我们首次展示了从博客、Digg 和 BBC 论坛下载的四百万条帖子中大规模观察到的情感现象的集体特征。为了测试社区成员的情绪是否会影响其他人的情绪，我们将帖子分为具有相似情感值的消息群。长群的频率远高于随机发生情绪时的频率。群长度的分布可以通过优先过程来解释，因为条件概率随着群长度呈幂律增长。对于 BBC 论坛线程，前十个评论中绝对平均情感值较大时，平均讨论长度较高，消息中的平均情绪量在讨论过程中下降。
结论/意义：总的来说，我们的研究结果证明了可以通过互联网交流来创建和调节集体情感状态，并且情感表达是维持某些电子商务社区的燃料。
