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Extracting hierarchical structure of content groups from different social media platforms using multiple social metadata

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/71557

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)

Submitter: 原川 良介

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