Abstract
The spread of microblogging services, such as Twitter, has made it possible to extract geographical characteristics such as keywords specific to a geographical region, with fine granularity. The results of content analysis of microblogging services are easily affected by users who post excessive messages. In addition, because geographical granularity of users’ interests differs, it is preferable to support multiple levels of granularity for usability. Thus, we propose a ranking method of location-dependent keywords based on a term frequency-inverse document frequency method to extract geographical characteristics. In our method, ranking scores are weighted by diversity of information sources so that the effect of loud users is mitigated. Multiple zoom levels of geographical areas are supported by approximation while databases at only several zoom levels are maintained. We evaluated our ranking method with a real dataset from Twitter and showed its effectiveness. We also describe a prototype implementation of a system using our ranking method.
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Ikeda, S., Kami, N., Yoshikawa, T. (2013). Ranking Location-Dependent Keywords to Extract Geographical Characteristics from Microblogs. In: Cordeiro, J., Krempels, KH. (eds) Web Information Systems and Technologies. WEBIST 2012. Lecture Notes in Business Information Processing, vol 140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36608-6_15
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DOI: https://doi.org/10.1007/978-3-642-36608-6_15
Publisher Name: Springer, Berlin, Heidelberg
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