Domain-Level Cross-Social Media Context Aggregation

• Item type: SIG Technical Reports(1)
• 公開日: 2014-07-25
• タイトル: Domain-Level Cross-Social Media Context Aggregation
• タイトル / 言語: en
• タイトル: Domain-Level Cross-Social Media Context Aggregation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: ソーシャルメディア
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information, Production and Systems, Waseda University
• 著者所属: Graduate School of Information, Production and Systems, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information, Production and Systems, Waseda University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information, Production and Systems, Waseda University
• 著者名: Chamwenga M., Chilufya; Mizuho, Iwaihara
• 著者名(英): Chamwenga M., Chilufya; Mizuho, Iwaihara
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Aggregating content across social media or Social Networking Services (SNS) has the benefit of discovering interesting information that may not be available on a single social media. However, in the face of information overload it becomes imperative to employ fine grained cross-social media aggregation. Social media interaction is characterized by threaded conversations initiated by a post on domain specific topics for example, politics, health or personal life; this creates a post-feedback context. Research on cross-social media aggregation has focused mainly on high-level identification of trending topics, however, providing users with a parallel view of contexts from multiple social media, irrespective of popularity, can realize discovery of related contents with less effort. In this paper, we propose a framework that, given a context on one social media retrieves highly relevant context on another social media by using informative keywords extracted from a given context and special “#” prefixed words called hashtags, while maintaining the premise of being relevant in time.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Aggregating content across social media or Social Networking Services (SNS) has the benefit of discovering interesting information that may not be available on a single social media. However, in the face of information overload it becomes imperative to employ fine grained cross-social media aggregation. Social media interaction is characterized by threaded conversations initiated by a post on domain specific topics for example, politics, health or personal life; this creates a post-feedback context. Research on cross-social media aggregation has focused mainly on high-level identification of trending topics, however, providing users with a parallel view of contexts from multiple social media, irrespective of popularity, can realize discovery of related contents with less effort. In this paper, we propose a framework that, given a context on one social media retrieves highly relevant context on another social media by using informative keywords extracted from a given context and special “#” prefixed words called hashtags, while maintaining the premise of being relevant in time.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10112482
• 書誌情報: 研究報告データベースシステム（DBS）; 巻 2014-DBS-159, 号 25, p. 1-6, 発行日 2014-07-25
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会