分析Reddit评论网络结构,揭示用户互动以单对单短对话为主。
Structure and dynamics of growing networks of Reddit threads
- 将评论线程建模为随时间演化的复杂网络
- 发现全局聚类系数极低且路径长度递增
- 结果支持社会判断理论,适合研究在线评判行为
数百万用户使用在线社交网络以增强归属感,例如通过给予或请求反馈来实现社会认可与自我确认。在表达反馈时,人们常出现观点分歧。建模和分析此类互动对于理解人们面对不同意见时的价值讨论至关重要。本文研究一个Reddit社区,用户参与其中对某种行为进行评判或接受评判,该社区成为研究在线表达判断的宝贵资源。我们将该社区的评论线程建模为随时间增长的复杂用户交互网络,并分析其结构属性的演化。结果显示,尽管这类网络与其他真实社交网络同属一类,但其演化特征不同:全局聚类系数极小,平均最短路径长度随时间增加。这反映出用户在评论中主要与一人互动,且常仅通过一条消息完成交流。我们进一步分析了分歧与互惠在对话中的作用。还发现该社区线程演化由两个子图驱动,其增长速度差异显著高于其他社区,原因在于用户指南强制规范了特定互动模式。最终,我们基于社会判断理论解释了用户行为模式。
原文摘要 · Abstract (English)
Millions of people use online social networks to reinforce their sense of belonging, for example by giving and asking for feedback as a form of social validation and self-recognition. It is common to observe disagreement among people beliefs and points of view when expressing this feedback. Modeling and analyzing such interactions is crucial to understand social phenomena that happen when people face different opinions while expressing and discussing their values. In this work, we study a Reddit community in which people participate to judge or be judged with respect to some behavior, as it represents a valuable source to study how users express judgments online. We model threads of this community as complex networks of user interactions growing in time, and we analyze the evolution of their structural properties. We show that the evolution of Reddit networks differ from other real social networks, despite falling in the same category. This happens because their global clustering coefficient is extremely small and the average shortest path length increases over time. Such properties reveal how users discuss in threads, i.e. with mostly one other user and often by a single message. We strengthen such result by analyzing the role that disagreement and reciprocity play in such conversations. We also show that Reddit thread's evolution over time is governed by two subgraphs growing at different speeds. We discover that, in the studied community, the difference of such speed is higher than in other communities because of the user guidelines enforcing specific user interactions. Finally, we interpret the obtained results on user behavior drawing back to Social Judgment Theory.
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