Discovering geographical topics in the twitter stream
Discovering geographical topics in the twitter stream
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DOI：
10.1145/2187836.2187940
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
Liangjie Hong;Amr Ahmed;S. Gurumurthy;Alex Smola;Kostas Tsioutsiouliklis
中科院分区：
文献类型：
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作者：
Liangjie Hong;Amr Ahmed;S. Gurumurthy;Alex Smola;Kostas Tsioutsiouliklis
微博服务已成为网络用户传播突发新闻、目击者描述、个人表达以及抗议群体信息不可或缺的交流工具。最近，推特以及其他在线社交网络服务，如四方（Foursquare）、高瓦拉（Gowalla）、脸书（Facebook）和大众点评网（Yelp），已开始在其消息中支持定位服务，要么明确地让用户选择地点，要么隐含地通过启用地理标记（即把消息与纬度和经度相关联）来实现。这一功能使研究人员能够解决一系列令人兴奋的问题：1）信息是如何在不同地理位置创建和共享的；2）不同地区的人的空间和语言特征有何差异；3）如何对人类的移动性进行建模。尽管已经为解决这些问题进行了许多尝试，但以前的方法要么实施起来复杂，要么过于简单而无法产生合理的效果。由于这些位置共享服务上的数据量巨大且语言变体多样，从这些带有地理标记的消息中发现主题并识别用户兴趣是一项具有挑战性的任务。在本文中，我们以推特为重点，提出了一种基于主题多样性、地理多样性和用户兴趣分布对推文多样性进行建模的算法。此外，我们考虑了用户位置的马尔可夫性质。我们的模型利用属性的稀疏因子编码，从而使我们能够有效地处理大量不同的协变量。我们的方法对于用户画像、内容推荐和主题跟踪等应用至关重要。我们基于我们的模型在位置估计方面显示出了较高的准确性。此外，该算法还能根据位置和语言识别有趣的主题。
Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with latitudes and longitudes. This functionality allows researchers to address an exciting set of questions: 1) How is information created and shared across geographical locations, 2) How do spatial and linguistic characteristics of people vary across regions, and 3) How to model human mobility. Although many attempts have been made for tackling these problems, previous methods are either complicated to be implemented or oversimplified that cannot yield reasonable performance. It is a challenge task to discover topics and identify users' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services. In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user. Furthermore, we take the Markovian nature of a user's location into account. Our model exploits sparse factorial coding of the attributes, thus allowing us to deal with a large and diverse set of covariates efficiently. Our approach is vital for applications such as user profiling, content recommendation and topic tracking. We show high accuracy in location estimation based on our model. Moreover, the algorithm identifies interesting topics based on location and language.