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Extracting hierarchical structure of content groups from different social media platforms using multiple social metadata
Title: | Extracting hierarchical structure of content groups from different social media platforms using multiple social metadata |
Authors: | Takehara, Daichi Browse this author | Harakawa, Ryosuke Browse this author | Ogawa, Takahiro Browse this author →KAKEN DB | Haseyama, Miki Browse this author →KAKEN DB |
Keywords: | Social media platform | Cross-platform application | Hierarchical structure | YouTube | Wikipedia |
Issue Date: | Oct-2017 |
Publisher: | Springer |
Journal Title: | Multimedia Tools and Applications |
Volume: | 76 |
Issue: | 19 |
Start Page: | 20249 |
End Page: | 20272 |
Publisher DOI: | 10.1007/s11042-017-4717-7 |
Abstract: | A novel scheme for retrieving users’ desired contents, i.e., contents with topics in which users are interested, from multiple social media platforms is pre- sented in this paper. In existing retrieval schemes, users first select a particular platform and then input a query into the search engine. If users do not specify suitable platforms for their information needs and do not input suitable queries corresponding to the desired contents, it becomes difficult for users to retrieve the desired contents. The proposed scheme extracts the hierarchical structure of content groups (sets of contents with similar topics) from different social media platforms, and it thus becomes feasible to retrieve desired contents even if users do not specify suitable platforms and do not input suitable queries. This paper has two contributions: (1) A new feature extraction method, Locality Preserving Canoni- cal Correlation Analysis with multiple social metadata (LPCCA-MSM) that can detect content groups without the boundaries of different social media platforms is presented in this paper. LPCCA-MSM uses multiple social metadata as auxiliary information unlike conventional methods that only use content-based information such as textual or visual features. (2) The proposed novel retrieval scheme can re- alize hierarchical content structuralization from different social media platforms. The extracted hierarchical structure shows various abstraction levels of content groups and their hierarchical relationships, which can help users select topics re- lated to the input query. To the best of our knowledge, an intensive study on such an application has not been conducted; therefore, this paper has strong nov- elty. To verify the effectiveness of the above contributions, extensive experiments for real-world datasets containing YouTube videos and Wikipedia articles were conducted. |
Rights: | This is a post-peer-review, pre-copyedit version of an article published in "Multimedia Tools and Applications". The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-017-4717-7 |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/71557 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 原川 良介
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