Flamino James, Gong Bowen, Buchanan Frederick, Szymanski Boleslaw K
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Entropy (Basel). 2021 Dec 7;23(12):1642. doi: 10.3390/e23121642.
Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing "microscopic" trends to describe "macroscopic" phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.
在线社交媒体为背景、兴趣和信仰各异的用户提供了大量开放式平台，便于他们进行互动和辩论，从而促进了围绕无数主题展开的无数讨论。随着越来越多独特的声音融入不断增长的信息流，考虑社交媒体帖子引发的对话类型如何反映帖子本身就变得至关重要。我们假设，用户的偏见和倾向会导致他们以不一定完全由发送者意图的不同方式对不同话题做出反应。在本文中，我们引入了一组独特的特征来捕捉话语模式，使我们能够实证探索一个话题与其引发的对话之间的关系。利用“微观”趋势来描述“宏观”现象，我们建立了一个通过话题引发的用户反应来分析信息传播的范式，无需分析讨论中涉及的文本。使用Reddit数据集，我们发现我们的特征不仅能使分类器准确区分内容类型，还能识别单个主题下内容中更细微的语义差异，以及分离出主题与常态有显著差异的异常值。
