Tweethood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining
Tweethood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining
复制标题
Tweethood：用于位置挖掘的可变深度模糊 k-最近好友的凝聚聚类
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
L. Khan
中科院分区：
文献类型：
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作者：
Satyen Abrol;L. Khan
根据研究公司 ABI Research 最近的一份报告，到 2014 年，基于位置的社交网络收入可能高达 133 亿美元[1]。 Foursquare 和 Gowalla 等社交网络在位置战中处于不分胜负的状态。但是，尽管出于隐私和安全原因理解这一点很重要，但 Twitter 等社交网站上的大多数人都不愿意明确指定他们的位置。这就产生了对基于与用户相关的隐式属性来挖掘用户位置的软件的需求。在本文中，我们建议开发一种工具 TweetHood，该工具可以根据用户的社交网络预测用户的位置。我们展示了算法的演变，强调了不同方法的缺点以及我们克服这些缺点的方法。我们进行了大量的实验，以证明我们的系统在准确性和运行时间方面的有效性。实验表明，我们的系统在城市级别的准确率达到 72.1%，在国家级别的准确率达到 80.1%。实验结果表明，TweetHood 优于基于地名词典的地理标记方法。
According to a recent report by research firm ABI Research, location-based social networks could reach revenues as high as $13.3 billion by 2014 [1]. Social Networks like Foursquare and Gowalla are in a dead heat in the Location War. But, having said that it is important to understand for privacy and security reasons, most of the people on social networking sites like Twitter are unwilling to specify their locations explicitly. This creates a need for software that mines the location of the user based on the implicit attributes associated with him. In this paper, we propose the development of a tool TweetHood that predicts the location of the user on the basis of his social network. We show the evolution of the algorithm, highlighting the drawbacks of the different approaches and our methodology to overcome them. We perform extensive experiments to show the validity of our system in terms of both accuracy and running time. The experiments performed demonstrate that our system achieves an accuracy of 72.1% at the city level and 80.1% at the country level. Experimental results show that TweetHood outperforms the gazetteer based geo-tagging approach.