Measuring Information Propagation in Literary Social Networks
Measuring Information Propagation in Literary Social Networks
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DOI：
10.18653/v1/2020.emnlp-main.47
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Matthew Sims;David Bamman
中科院分区：
文献类型：
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作者：
Matthew Sims;David Bamman
我们提出了在文学建模的信息传播的任务，在其中，我们试图识别从字符A到字符B到字符C传递的信息片段，只给出了它们在文本中的活动的描述。我们描述了一个新的管道测量信息传播在这一领域，并发布了一个新的数据集的扬声器属性，使这个管道的一个重要组成部分的评估范围更广的文学文本比以前研究。利用这条管道，我们分析了5,000多部小说作品中的信息传播动态，发现信息通过填补连接不同社区的结构漏洞的角色流动，并且女性角色被描绘成比男性角色更频繁地填补这个角色。
We present the task of modeling information propagation in literature, in which we seek to identify pieces of information passing from character A to character B to character C, only given a description of their activity in text. We describe a new pipeline for measuring information propagation in this domain and publish a new dataset for speaker attribution, enabling the evaluation of an important component of this pipeline on a wider range of literary texts than previously studied. Using this pipeline, we analyze the dynamics of information propagation in over 5,000 works of fiction, finding that information flows through characters that fill structural holes connecting different communities, and that characters who are women are depicted as filling this role much more frequently than characters who are men.