NDLI: The Social World of Content Abusers in Community Question Answering

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The Social World of Content Abusers in Community Question Answering

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The Social World of Content Abusers in Community Question Answering

Content Provider	ACM Digital Library
Author	Iamnitchi, Adriana Bonchi, Francesco Kayes, Imrul Quercia, Daniele Kourtellis, Nicolas
Abstract	Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.
Starting Page	570
Ending Page	580
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741674
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Content abusers Community question answering
Content Type	Text
Resource Type	Article

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