解开osn上的信息洪流:发现值得关注的帖子和话题

P. Caso, Martino Trevisan, L. Vassio
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摘要

在线社交网络(OSN)是现代生活中不可或缺的一部分,用于分享思想、故事和新闻。网红生态系统会以帖子的形式产生大量内容,其中一些内容与网红的粉丝群有着不同寻常的高参与度。这些帖子与引起用户特别兴趣的热门讨论话题有关:COVID-19大流行就是一个突出的例子。研究这些现象有助于了解OSN的情况,并需要适当的方法。本文提出了一种方法来发现值得注意的帖子,并根据他们的相关主题进行分组。通过结合异常检测、图形建模和社区检测技术,我们可以自动定位显著事件,并能够调整它们的数量。我们使用一个大型Instagram数据集来展示我们的方法,并从140万篇帖子中提取出一些值得注意的每周话题。然后,我们举例说明了从COVID-19爆发到体育赛事的一些用例。
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Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics
Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events.
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