用于探索在线话语的实体图
Entity Graphs for Exploring Online Discourse
AI总结：
本研究提出实体图（entity graph）概念空间，将概念设为静态节点、交流者动态移动，基于Reddit数据分析了在线话语的演变规律，发现对话随时间从主题发散趋于收敛至流行概念。
摘要：
大量的人类交流发生在网上。这些自然人类交流的数字痕迹，加上自然语言处理技术的最新进展，为这些讨论的计算分析提供了条件。在社交网络研究中，典型的视角是将用户视为节点，将概念视为在社交网络内的用户节点之间流动。在本研究中，我们采取相反的视角：我们将大量的群组讨论提取并组织到一个我们称之为实体图（entity graph）的概念空间中，其中概念和实体是静态的，而人类交流者通过他们的对话在这个概念空间中移动。基于这一视角，我们对来自Reddit的大量在线话语进行了几项实验和比较分析。在定量实验中，我们发现话语很难预测，尤其是随着对话的进行。我们还开发了一个交互式工具，用于在实体图上可视化检查对话轨迹；尽管对话很难预测，但我们发现，总的来说，对话最初倾向于发散到大量主题，但随着对话的进行，最终倾向于收敛到简单和流行的概念。认知心理学领域的扩散激活函数的应用也从数据中提供了引人注目的视觉叙述。
英文摘要：
Vast amounts of human communication occurs online. These digital traces of natural human communication along with recent advances in natural language processing technology provide for computational analysis of these discussions. In the study of social networks the typical perspective is to view users as nodes and concepts as flowing through and among the user-nodes within the social network. In the present work we take the opposite perspective: we extract and organize massive amounts of group discussion into a concept space we call an entity graph where concepts and entities are static and human communicators move about the concept space via their conversations. Framed by this perspective we performed several experiments and comparative analysis on large volumes of online discourse from Reddit. In quantitative experiments, we found that discourse was difficult to predict, especially as the conversation carried on. We also developed an interactive tool to visually inspect conversation trails over the entity graph; although they were difficult to predict, we found that conversations, in general, tended to diverge to a vast swath of topics initially, but then tended to converge to simple and popular concepts as the conversation progressed. An application of the spreading activation function from the field of cognitive psychology also provided compelling visual narratives from the data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
