Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection
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DOI：
10.1109/tkde.2012.239
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发表时间：
2014-01-01
影响因子：
8.9
通讯作者：
Yamanishi, Kenji
中科院分区：
文献类型：
--
作者：
Takahashi, Toshimitsu;Tomioka, Ryota;Yamanishi, Kenji
由于社交网络的快速增长，对新兴主题的检测现在重新受到关注。传统的基于词频的方法在这种情况下可能不合适，因为在社交网络帖子中交换的信息不仅包括文本，还包括图像、URL和视频。我们专注于这些网络的社会方面发出信号的主题的出现。具体来说，我们关注的是用户的提及--通过回复、提及和转推动态（有意或无意）生成的用户之间的链接。我们提出了一个概率模型的社交网络用户的提及行为，并建议检测一个新的主题的出现，通过该模型测量的异常。聚集数百名用户的异常分数，我们表明，我们可以检测到新兴的主题，仅基于社交网络帖子中的回复/提及关系。我们展示了我们的技术在几个真实的数据集，我们从Twitter收集。实验表明，所提出的基于提及异常的方法可以检测到新的主题，至少早于基于文本异常的方法，并且在某些情况下，当主题被帖子中的文本内容识别得很差时，可以更早地检测到新的主题。
Detection of emerging topics is now receiving renewed interest motivated by the rapid growth of social networks. Conventional-term-frequency-based approaches may not be appropriate in this context, because the information exchanged in social-network posts include not only text but also images, URLs, and videos. We focus on emergence of topics signaled by social aspects of theses networks. Specifically, we focus on mentions of users-links between users that are generated dynamically (intentionally or unintentionally) through replies, mentions, and retweets. We propose a probability model of the mentioning behavior of a social network user, and propose to detect the emergence of a new topic from the anomalies measured through the model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social-network posts. We demonstrate our technique in several real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-based approaches, and in some cases much earlier when the topic is poorly identified by the textual contents in posts.