The dark side of news community forums: opinion manipulation trolls

Author:

Mihaylov Todor,Mihaylova Tsvetomila,Nakov Preslav,Màrquez Lluís,Georgiev Georgi D.,Koychev Ivan Kolev

Abstract

Purpose The purpose of this paper is to explore the dark side of news community forums: the proliferation of opinion manipulation trolls. In particular, it explores the idea that a user who is called a troll by several people is likely to be one. It further demonstrates the utility of this idea for detecting accused and paid opinion manipulation trolls and their comments as well as for predicting the credibility of comments in news community forums. Design/methodology/approach The authors are aiming to build a classifier to distinguish trolls vs regular users. Unfortunately, it is not easy to get reliable training data. The authors solve this issue pragmatically: the authors assume that a user who is called a troll by several people is likely to be such, which are called accused trolls. Based on this assumption and on leaked reports about actual paid opinion manipulation trolls, the authors build a classifier to distinguish trolls vs regular users. Findings The authors compare the profiles of paid trolls vs accused trolls vs non-trolls, and show that a classifier trained to distinguish accused trolls from non-trolls does quite well also at telling apart paid trolls from non-trolls. Research limitations/implications The troll detection works even for users with about 10 comments, but it achieves the best performance for users with a sizable number of comments in the forum, e.g. 100 or more. Yet, there is not such a limitation for troll comment detection. Practical implications The approach would help forum moderators in their work, by pointing them to the most suspicious users and comments. It would be also useful to investigative journalists who want to find paid opinion manipulation trolls. Social implications The authors can offer a better experience to online users by filtering out opinion manipulation trolls and their comments. Originality/value The authors propose a novel approach for finding paid opinion manipulation trolls and their posts.

Publisher

Emerald

Subject

Economics and Econometrics,Sociology and Political Science,Communication

Reference90 articles.

1. PMI-cool at SemEval-2016 task 3: experiments with PMI and goodness polarity lexicons for community question answering,2016

2. Thread-level information for comment classification in community question answering,2015

3. Effects of complaint behaviour and service recovery satisfaction on consumer intentions to repurchase on the internet;Internet Research,2014

4. Trolls just want to have fun;Personality and Individual Differences,2014

5. Do not feel the trolls,2010

Cited by 33 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3