Provenance Data in Social Media (Paperback)

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 interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media. Table of Contents: Information Provenance in Social Media / Provenance Attributes / Provenance via Network Information / Provenance Data

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Synthesis Lectures on Data Mining and Knowledge Discovery
Release date:	May 2013
First published:	2013
Authors:	Geoffrey Barbier • Zhuo Feng • Pritam Gundecha • Huan Liu
Dimensions:	235 x 191mm (L x W)
Format:	Paperback
Pages:	72
ISBN-13:	978-3-03-100776-7
Languages:	English
Subtitles:	English
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	3-03-100776-X
Barcode:	9783031007767

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