Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter
Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter
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DOI：
10.1145/2651403
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Jan Vosecky;Di Jiang;K. Leung;Kai Xing;Wilfred Ng
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文献类型：
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作者：
Jan Vosecky;Di Jiang;K. Leung;Kai Xing;Wilfred Ng
Twitter 等微博平台已经在最近的文化、社会和政治事件中发挥了重要作用。因此，从社交流中发现潜在主题对于许多下游应用程序（例如聚类、分类或推荐）非常重要。然而，依赖词袋假设的传统主题模型不足以揭示 Twitter 中主题的丰富语义和时间方面。特别是，微博内容经常受到外部信息源（例如从 Twitter 帖子链接的 Web 文档）的影响，并且通常关注特定实体（例如人员或组织）。这些外部来源为理解微博提供了有用的语义，我们通常将这些语义称为辅助语义。在本文中，我们解决了上述问题，并提出了一个用于 Twitter 流多方面主题建模的统一框架。我们首先通过对与主题标签相关的社交聊天进行建模，从 Twitter 中提取社交语义。我们进一步从链接的 Web 文档中提取术语和命名实体，以在主题建模期间充当辅助语义。然后提出多方面主题模型（MfTM）来联合建模来自 Twitter 的社交术语、来自链接的 Web 文档的辅助术语和命名实体之间的潜在语义。此外，我们捕获每个主题的时间特征。开发了一种高效的 MfTM 在线推理方法，使我们的模型能够应用于大规模流数据。我们的实验评估显示了我们的模型与最先进的基线相比的有效性和效率。我们评估框架的各个方面，并展示其在推文聚类背景下的实用性。
Microblogging platforms, such as Twitter, have already played an important role in recent cultural, social and political events. Discovering latent topics from social streams is therefore important for many downstream applications, such as clustering, classification or recommendation. However, traditional topic models that rely on the bag-of-words assumption are insufficient to uncover the rich semantics and temporal aspects of topics in Twitter. In particular, microblog content is often influenced by external information sources, such as Web documents linked from Twitter posts, and often focuses on specific entities, such as people or organizations. These external sources provide useful semantics to understand microblogs and we generally refer to these semantics as auxiliary semantics. In this article, we address the mentioned issues and propose a unified framework for Multifaceted Topic Modeling from Twitter streams. We first extract social semantics from Twitter by modeling the social chatter associated with hashtags. We further extract terms and named entities from linked Web documents to serve as auxiliary semantics during topic modeling. The Multifaceted Topic Model (MfTM) is then proposed to jointly model latent semantics among the social terms from Twitter, auxiliary terms from the linked Web documents and named entities. Moreover, we capture the temporal characteristics of each topic. An efficient online inference method for MfTM is developed, which enables our model to be applied to large-scale and streaming data. Our experimental evaluation shows the effectiveness and efficiency of our model compared with state-of-the-art baselines. We evaluate each aspect of our framework and show its utility in the context of tweet clustering.