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EVENT:

Finding credible information sources in social networks based on content and social structure
Conferences & Talks

 

description

A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and implicit judgments of credibility. The results of the study indicate that both the topical content of information sources and social network structure affect source credibility. Based on these results, we designed a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic. We performed empirical studies to compare a variety of alternative ranking algorithms and a proprietary service provided by a commercial website specifically designed for the same purpose. Our findings show a great potential for automatically identifying and ranking credible users for any given topic.

 

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The Cutting Edge of Artificial Intelligence
Tolga Kurtoglu, Margareta Ackerman, Andra Keay, Piero Scaruffi, Allen Saakyan
22 February 2018 | George E. Pake Auditorium, PARC
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Printed Electronics: From Vision to Disruption
Markus Larsson
13 March 2018 | Munich, Germany
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The Ghost in the Smart Machine (Panel)
Tolga Kurtoglu, Panelist
22 March 2018 | Chicago, IL
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Robots, AI and the Future of Work (Plenary Session)
Tolga Kurtoglu
17 April 2018 | Miami, FL
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Bringing Reliable (and Transparent) AI to Business (Keynote)
Tolga Kurtoglu, Keynote Speaker
26 April 2018 | Seattle, WA
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