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Classifying Urban Events by Analyzing Common Friends in Location-based Social Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Sato, Shoya Yonezawa, Takuro Kawano, Makoto Nakazawa, Jin Kawasaki, Hitoshi Oota, Ken Inamura, Hiroshi Tokuda, Hideyuki |
| Copyright Year | 2016 |
| Abstract | Recently, many researchers focus to detect and classify urban events by analyzing information on social network. Previous work mainly use text analysis of users’ posts on social networks for detecting urban events; however, this approach has a limitation that users’ posts must mention event information. We develop a new method to detect and classify urban events by extracting users’ interests from location-based social network information without using text analysis. Our method analyses common friends in users who exist in the area of on-going events, and extract common friends’ attributes from related Wikipedia information. We designed and implemented the proposed method, and carried out experiment for evaluating our method. Our experimental result shows that our method can classify events, where participants have similar interests, with high similarity by compared with ground truth created by questionnaire. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://ipsj.ixsq.nii.ac.jp/ej/index.php?action=pages_view_main&active_action=repository_action_common_download&attribute_id=1&block_id=8&file_no=1&item_id=175052&item_no=1&page_id=13 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
