Data sharing sensitivity, relevance, and social traits in social information retrieval Online publication date: Sun, 17-Dec-2017
by Christoph Fuchs; Georg Groh
International Journal of Social Computing and Cyber-Physical Systems (IJSCCPS), Vol. 2, No. 1, 2017
Abstract: This paper evaluates a social information retrieval system where users can query each other for information. The approached user can reply to queries ('manual mode scenario') based on her knowledge. In addition, a social product search scenario is evaluated, where results are enriched with items bought or viewed by a social peer group ('social product search scenario'). Each scenario is tested with more than 120 participants. Our findings suggest that asking others reveals relevant results. The injection of socially motivated result items in result list for product queries did not increase quality. Users share more private information using strong ties. In addition, a reactive sharing pattern (which does not rely on previously published material, but queries directly using a pull regime) increases the amount of data for social information retrieval. The majority of questions covered recommendations in media, travel, technology, and food and personal opinions on technology-related topics and travel.
Online publication date: Sun, 17-Dec-2017
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