论文标题
情境化在线对话网络
Contextualizing Online Conversational Networks
论文作者
论文摘要
在线社交联系发生在特定的对话环境中。社交媒体数据网络分析中的先前工作试图通过过滤将数据背景化。我们提出了一种自动将在线对话连接进行环境化的方法，并使用Twitter数据说明了此方法。具体而言，我们详细介绍了一个能够根据其文本，主题标签，URL和相邻推文来表示向量空间中的推文的图形神经网络模型。一旦表示推文，推文的簇就会发现对话上下文。我们将方法应用于与450万条推文讨论2020年美国大选的数据集。我们发现，即使被过滤的数据包含许多不同的对话上下文，用户参与了多种上下文。上下文化网络中的中央用户与整个网络中的中央用户有很大差异。该结果意味着，面对多个对话环境，对社交媒体数据的标准网络分析可能是不可靠的。我们进一步证明，对会话环境的动态分析可以对对话流进行定性的理解。
Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple contexts. Central users in the contextualized networks differ significantly from central users in the overall network. This result implies that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts. We further demonstrate that dynamic analysis of conversational contexts gives a qualitative understanding of conversational flow.
