Topical semantics of twitter links
Topical semantics of twitter links
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DOI：
10.1145/1935826.1935882
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
Michael J. Welch;Uri Schonfeld;Dan He;Junghoo Cho
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文献类型：
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作者：
Michael J. Welch;Uri Schonfeld;Dan He;Junghoo Cho
Twitter是一个微型博客平台，估计每月有2 000万独立访问者和1亿多注册用户，以每秒超过600条推文的速度提供丰富的结构化数据。最近的努力，利用这些社会数据，排名用户的质量和主题相关性主要集中在“跟随”的关系。然而，Twitter的数据提供了用户之间额外的隐含关系，比如“转推”和“提及”。在本文中，我们调查的语义的关注和转推关系。具体来说，我们表明，主题相关性的传递性更好地保存在转发链接，并转发用户是一个显着更强的指标，主题兴趣比跟随他。我们证明了这些属性的PageRank算法的两个变种的用户排名;一个基于以下子图和一个基于隐式retweet子图。我们进行了用户研究，以评估由此产生的排名靠前的用户的主题相关性。
Twitter, a micro-blogging platform with an estimated 20 million unique monthly visitors and over 100 million registered users, offers an abundance of rich, structured data at a rate exceeding 600 tweets per second. Recent efforts to leverage this social data to rank users by quality and topical relevance have largely focused on the "follow" relationship. Twitter's data offers additional implicit relationships between users, however, such as "retweets" and "mentions". In this paper we investigate the semantics of the follow and retweet relationships. Specifically, we show that the transitivity of topical relevance is better preserved over retweet links, and that retweeting a user is a significantly stronger indicator of topical interest than following him. We demonstrate these properties by ranking users with two variants of the PageRank algorithm; one based on the follows sub-graph and one based on the implicit retweet sub-graph. We perform a user study to assess the topical relevance of the resulting top-ranked users.