Who Leaves Malicious Comments on Online News? An Empirical Study in Korea

被引:4
作者
Baek, Hyunmi [1 ]
Jang, Moonkyoung [2 ]
Kim, Seongcheol [1 ]
机构
[1] Korea Univ, Sch Media & Commun, Seoul, South Korea
[2] Gachon Univ, Coll Business, Seongnam, South Korea
基金
新加坡国家研究基金会;
关键词
Online news; online news comments; malicious comments; malicious commenters; commenting activities; prediction model; INCIVILITY; UNCIVIL; FACEBOOK; PATTERNS; OPINION; CUES;
D O I
10.1080/1461670X.2022.2031258
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Because online news comments have a strong influence on the reader's perception of public opinion, there is a call for efforts to reduce the adverse impact of online news comments, particularly malicious ones. Although many online news platforms currently use technology to detect malicious comments automatically, there is still a technical limit in identifying malicious comments. To improve detection accuracy, it is necessary to understand not only malicious comments but also malicious commenters. Despite the importance of understanding malicious commenters, there is little empirical research on their characteristics. This study aims to understand the characteristics of malicious commenters and develop a prediction model based on their features using real data of users and commenting activities from Naver, a leading Internet news portal in Korea. This study found that the demographic characteristics of malicious commenters tend to be those of males and older people. In terms of commenting activities, the online news commenters who leave more comments per news article and per day, delete more comments, and leave longer comments tend to be malicious commenters.
引用
收藏
页码:432 / 447
页数:16
相关论文
共 49 条
[1]  
Abdul-Mageed MM, 2008, TRIPLEC-COMMUN CAPIT, V6, P59
[2]  
[Anonymous], 2015, P INT AAAI C WEB SOC, DOI [10.1609/icwsm.v9i1.14583, DOI 10.1609/ICWSM.V9I1.14583]
[3]  
[Anonymous], 2021, INTERNET TREND
[4]   Deep Learning for Hate Speech Detection in Tweets [J].
Badjatiya, Pinkesh ;
Gupta, Shashank ;
Gupta, Manish ;
Varma, Vasudeva .
WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2017, :759-760
[5]   penalizedSVM: a R-package for feature selection SVM classification [J].
Becker, Natalia ;
Werft, Wiebke ;
Toedt, Grischa ;
Lichter, Peter ;
Benner, Axel .
BIOINFORMATICS, 2009, 25 (13) :1711-1712
[6]   Dark personalities on Facebook: Harmful online behaviors and language [J].
Bogolyubova, Olga ;
Panicheva, Polina ;
Tikhonov, Roman ;
Ivanov, Viktor ;
Ledovaya, Yanina .
COMPUTERS IN HUMAN BEHAVIOR, 2018, 78 :151-159
[7]   Does It Matter Where You Read the News Story? Interaction of Incivility and News Frames in the Political Blogosphere [J].
Borah, Porismita .
COMMUNICATION RESEARCH, 2014, 41 (06) :809-827
[8]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[9]   Breakdown of Democratic Norms? Understanding the 2016 US Presidential Election Through Online Comments [J].
Chen, Gina Masullo ;
Riedl, Martin J. ;
Shermak, Jeremy L. ;
Brown, Jordon ;
Tenenboim, Ori .
SOCIAL MEDIA + SOCIETY, 2019, 5 (02)
[10]   The impacts of identity verification and disclosure of social cues on flaming in online user comments [J].
Cho, Daegon ;
Kwon, K. Hazel .
COMPUTERS IN HUMAN BEHAVIOR, 2015, 51 :363-372