Where Does Your News Come From? Predicting Information Pathways in Social Media
Where Does Your News Come From? Predicting Information Pathways in Social Media
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DOI：
10.1145/3539618.3592087
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Alexander K. Taylor;Nuan Wen;Po-Nien Kung;Jiaao Chen;Violet Peng;W. Wang
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作者：
Alexander K. Taylor;Nuan Wen;Po-Nien Kung;Jiaao Chen;Violet Peng;W. Wang
随着社交网络在现代社会中的进一步根深蒂固，了解和预测信息（例如，对特定事件的新闻报道）如何在社交媒体（即信息途径）中传播的信息变得越来越重要，这有助于了解对理解的理解现实世界的信息。因此，在本文中，我们提出了一项新颖的任务，信息途径预测（IPP），它描绘了给定段落作为社区树（植根于信息源）的传播路径在构造的社区交互图上，我们首先汇总单个用户进入围绕新闻来源和有影响力的用户形成的社区，然后根据社区节点阐明跨媒体的信息传播模式。我们认为这是一项重要且有用的任务，因为一方面，社区级交互比用户级别的交互更高。另一方面，单个用户通常会受其社区的影响，建模社区级信息传播将有助于传统的链接预测问题。为了解决IPP任务，我们介绍了闪电，这是一种新颖的内容吸引的链接预测GNN模型，并使用一个大型Twitter数据集进行了证明，该数据集由所有相关的推文组成，该推文以大量的余额优于先进的链接预测基线。
As social networks become further entrenched in modern society, it becomes increasingly important to understand and predict how information (e.g., news coverage of a given event) is propagated across social media (i.e., information pathway), which helps the understandings of the impact of real-world information. Thus, in this paper, we propose a novel task, Information Pathway Prediction (IPP), which depicts the propagation paths of a given passage as a community tree (rooted at the information source) on constructed community interaction graphs where we first aggregate individual users into communities formed around news sources and influential users, and then elucidate the patterns of information dissemination across media based on such community nodes. We argue that this is an important and useful task because, on one hand, community-level interactions offer more stability than those at the user level; on the other hand, individual users are often influenced by their community, and modeling community-level information propagation will help the traditional link-prediction problem. To tackle the IPP task, we introduce Lightning, a novel content-aware link prediction GNN model and demonstrate using a large Twitter dataset consisting of all COVID related tweets that Lightning outperforms state-of-the-art link prediction baselines by a significant margin.