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Facilitating Efficient Information Seeking in Social Media

abstract: Online social media is popular due to its real-time nature, extensive connectivity and a large user base. This motivates users to employ social media for seeking information by reaching out to their large number of social connections. Information seeking can manifest in the form of requests for personal and time-critical information or gathering perspectives on important issues. Social media platforms are not designed for resource seeking and experience large volumes of messages, leading to requests not being fulfilled satisfactorily. Designing frameworks to facilitate efficient information seeking in social media will help users to obtain appropriate assistance for their needs

and help platforms to increase user satisfaction.

Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the

syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on

issues of public importance.

Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Identiferoai:union.ndltd.org:asu.edu/item:48451
Date January 2017
ContributorsRanganath, Suhas (Author), Liu, Huan (Advisor), Lai, Ying-Cheng (Advisor), Tong, Hanghang (Committee member), Vaculin, Roman (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral Dissertation
Format149 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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