Exploring Malicious Comments behavior on Social Media: An Analysis from Cognition, Affect , and Conation

With the development of social media, user behavior on these platforms has become a focal point of research. However, less attention has been paid to irrational information behaviors in the study of social media user behavior. Malicious comments fundamentally reflect the irrational behavior of social media users. This paper, based on the cognition-affect-conation theoretical model, constructs a structural equation model that includes malice, individual constraints, radicalized emotions, and malicious comment behavior. Through 214 questionnaires, SEM is used to analyze the path relationships between cognitive bias, radicalized emotions, and malicious comment behavior, as well as the factors influencing radicalized emotions. The study findings indicate: (1) the objective perception of information malice is not related to malicious comment behavior. (2) Subjectively perceived information malice significantly positively influences both radicalized emotions and malicious comment behavior. (3) Individual cognitive constraints significantly positively influence both radicalized emotions and malicious comment behavior. (4) Radicalized emotions, acting as a mediating variable, significantly positively influence malicious comment behavior. The conclusions of this study will provide theoretical references for publishing accurate information on social media, reducing online hostility, and building a healthy and harmonious online environment.

Authors

Institutions

Publication Details

Journal
Frontiers in Humanities and Social Research
Published
2025-06-07
DOI
https://doi.org/10.71465/fhsr261
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Malicious Comments behavior on Social Media: An Analysis from Cognition, Affect , and Conation

Xiangyu Kong
Frontiers in Humanities and Social Research
Hate Speech and Cyberbullying Detection
article

Exploring Malicious Comments behavior on Social Media: An Analysis from Cognition, Affect , and Conation

Xiangyu Kong
article en

Abstract

With the development of social media, user behavior on these platforms has become a focal point of research. However, less attention has been paid to irrational information behaviors in the study of social media user behavior. Malicious comments fundamentally reflect the irrational behavior of social media users. This paper, based on the cognition-affect-conation theoretical model, constructs a structural equation model that includes malice, individual constraints, radicalized emotions, and malicious comment behavior. Through 214 questionnaires, SEM is used to analyze the path relationships between cognitive bias, radicalized emotions, and malicious comment behavior, as well as the factors influencing radicalized emotions. The study findings indicate: (1) the objective perception of information malice is not related to malicious comment behavior. (2) Subjectively perceived information malice significantly positively influences both radicalized emotions and malicious comment behavior. (3) Individual cognitive constraints significantly positively influence both radicalized emotions and malicious comment behavior. (4) Radicalized emotions, acting as a mediating variable, significantly positively influence malicious comment behavior. The conclusions of this study will provide theoretical references for publishing accurate information on social media, reducing online hostility, and building a healthy and harmonious online environment.

Frontiers in Humanities and Social ResearchVol. 2(3)
Southwest University (CN)
Openalex Percentile: Top 94%
Hate Speech and Cyberbullying Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.