TY - GEN
T1 - Four Types of Toxic People
T2 - 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020
AU - Mall, Raghvendra
AU - Nagpal, Mridul
AU - Salminen, Joni
AU - Almerekhi, Hind
AU - Jung, Soon Gyo
AU - Jansen, Bernard J.
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/25
Y1 - 2020/10/25
N2 - Identifying types of online users' toxic behavior reveals important insights from social media interactions, including whether a user becomes "radicalized" (more toxic) or "pacified" (less toxic) over time. In this research, we design two metrics to identify toxic user types: F score that captures the changes in a user's toxicity, and G score that captures the direction of the shift taking place in the user's toxicity pattern. We apply these metrics to a dataset of 4M user comments from Reddit by defining four toxic user types based on the toxicity scores of a user's comments: (a) Steady Users whose toxicity scores are steady over time, (b) Fickle-Minded Users that switch between toxic and non-toxic commenting, (c) Pacified Users whose commenting becomes less toxic in time, and (d) Radicalized Users that become gradually toxic. Findings from the Reddit dataset indicate that fickle-minded users form the largest group (31.2%), followed by pacified (25.8%), radicalized (25.4%), and steadily toxic users (17.6%). The results suggest that the most typical behavior type of toxicity is switching between toxic and non-toxic commenting. This research has implications for preserving the user-friendliness of online communities by identifying continuously toxic users and users in danger of becoming radicalized (in terms of their toxic behavior), and designing interventions to mitigate these behavior types. Using the metrics we have defined, identifying these user types becomes possible. More research is needed to understand why these patterns take place and how they could be mitigated.
AB - Identifying types of online users' toxic behavior reveals important insights from social media interactions, including whether a user becomes "radicalized" (more toxic) or "pacified" (less toxic) over time. In this research, we design two metrics to identify toxic user types: F score that captures the changes in a user's toxicity, and G score that captures the direction of the shift taking place in the user's toxicity pattern. We apply these metrics to a dataset of 4M user comments from Reddit by defining four toxic user types based on the toxicity scores of a user's comments: (a) Steady Users whose toxicity scores are steady over time, (b) Fickle-Minded Users that switch between toxic and non-toxic commenting, (c) Pacified Users whose commenting becomes less toxic in time, and (d) Radicalized Users that become gradually toxic. Findings from the Reddit dataset indicate that fickle-minded users form the largest group (31.2%), followed by pacified (25.8%), radicalized (25.4%), and steadily toxic users (17.6%). The results suggest that the most typical behavior type of toxicity is switching between toxic and non-toxic commenting. This research has implications for preserving the user-friendliness of online communities by identifying continuously toxic users and users in danger of becoming radicalized (in terms of their toxic behavior), and designing interventions to mitigate these behavior types. Using the metrics we have defined, identifying these user types becomes possible. More research is needed to understand why these patterns take place and how they could be mitigated.
KW - Reddit
KW - online toxicity
KW - social media behavior
KW - user analysis
UR - http://www.scopus.com/inward/record.url?scp=85123041001&partnerID=8YFLogxK
U2 - 10.1145/3419249.3420142
DO - 10.1145/3419249.3420142
M3 - Conference contribution
AN - SCOPUS:85123041001
T3 - ACM International Conference Proceeding Series
BT - NordiCHI 2020 - Proceedings of the 11th Nordic Conference on Human-Computer Interaction
PB - Association for Computing Machinery
Y2 - 25 October 2020 through 29 October 2020
ER -