| 書目名稱 | Provenance Data in Social Media | | 編輯 | Geoffrey Barbier,Zhuo Feng,Huan Liu | | 視頻video | http://file.papertrans.cn/763/762046/762046.mp4 | | 叢書名稱 | Synthesis Lectures on Data Mining and Knowledge Discovery | | 圖書封面 |  | | 描述 | Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an inter | | 出版日期 | Book 2013 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01904-3 | | isbn_softcover | 978-3-031-00776-7 | | isbn_ebook | 978-3-031-01904-3Series ISSN 2151-0067 Series E-ISSN 2151-0075 | | issn_series | 2151-0067 | | copyright | Springer Nature Switzerland AG 2013 |
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