Finding Bursty Topics from Microblogs
Finding Bursty Topics from Microblogs
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发表时间：
2012-07
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通讯作者：
Qiming Diao;Jing Jiang;Feida Zhu;Ee-Peng Lim
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作者：
Qiming Diao;Jing Jiang;Feida Zhu;Ee-Peng Lim
像推特这样的微博反映了公众对重大事件的反应。微博上的热门话题揭示了哪些事件在网络上最受关注。尽管之前已经对文本流中的突发事件检测进行了研究，但之前的研究可能不适用于微博，因为与新闻文章和科学出版物等其他文本流相比，微博帖子特别多样化且嘈杂。为了找到微博上具有热门模式的话题，我们提出了一个话题模型，该模型同时捕捉到两个观察结果：（1）大约在同一时间发布的帖子更有可能具有相同的话题，（2）同一用户发布的帖子更有可能具有相同的话题。前者有助于找到事件驱动的帖子，而后者有助于识别和过滤掉“个人”帖子。我们在一个大型推特数据集上进行的实验表明，与LDA基线以及我们模型的两个简化变体相比，我们的模型返回的排名靠前的结果中有更多有意义且独特的热门话题。我们还展示了一些案例研究，这些研究表明了在从微博中检测热门话题时考虑时间信息和用户个人兴趣的重要性。
Microblogs such as Twitter reflect the general public's reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneously captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) posts published by the same user are more likely to have the same topic. The former helps find event-driven posts while the latter helps identify and filter out "personal" posts. Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model than an LDA baseline and two degenerate variations of our model. We also show some case studies that demonstrate the importance of considering both the temporal information and users' personal interests for bursty topic detection from microblogs.