Dark Personality Traits and Online Toxicity: Linking Self-Reports to Reddit Activity
- URL: http://arxiv.org/abs/2512.10113v1
- Date: Wed, 10 Dec 2025 22:06:30 GMT
- Title: Dark Personality Traits and Online Toxicity: Linking Self-Reports to Reddit Activity
- Authors: Aldo Cerulli, Benedetta Tessa, Giuseppe La Selva, Oronzo Mazzeo, Lorenzo Cima, Lucia Monacis, Stefano Cresci,
- Abstract summary: Dark personality traits have been linked to online misbehavior such as trolling, incivility, and toxic speech.<n>Sadistic and psychopathic tendencies are most strongly associated with overtly toxic language.<n>Bright and dark traits interact in nuanced ways, with extraversion reducing trolling tendencies and conscientiousness showing modest associations with entitlement and callousness.
- Score: 0.2336460276005258
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dark personality traits have been linked to online misbehavior such as trolling, incivility, and toxic speech. Yet the relationship between these traits and actual online conduct remains understudied. Here we investigate the associations between dark traits, online toxicity, and the socio-linguistic characteristics of online user activity. To explore this relationship, we developed a Web application that integrates validated psychological questionnaires from Amazon Mechanical Turk users to their Reddit activity data. This allowed collecting nearly 57K Reddit comments, including 2.2M tokens and 152.7K sentences from 114 users, that we systematically represent through 224 linguistic and behavioral features. We then examined their relationship to questionnaire-based trait measures via multiple correlation analyses. Among our findings is that dark traits primarily influence the production rather than the perception of online incivility. Sadistic and psychopathic tendencies are most strongly associated with overtly toxic language, whereas other dark dispositions manifest more subtly, often eluding simple textual proxies. Self-reported engagement in hostile behavior mirrors actual online activity, while existing hand-crafted textual proxies for dark triad traits show limited correspondence with our validated measures. Finally, bright and dark traits interact in nuanced ways, with extraversion reducing trolling tendencies and conscientiousness showing modest associations with entitlement and callousness. These findings deepen understanding of how personality shapes toxic online behavior and highlight both opportunities and challenges for developing reliable computational tools and targeted, effective moderation strategies.
Related papers
- The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
arXiv Detail & Related papers (2025-09-03T21:27:10Z) - Toxic behavior silences online political conversations [0.0]
We investigate the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior.<n>Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence.<n>Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics.
arXiv Detail & Related papers (2024-12-07T20:39:20Z) - CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a
Context Synergized Hyperbolic Network [52.85130555886915]
CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations.
We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
arXiv Detail & Related papers (2023-03-02T17:30:43Z) - Relationship Between Online Harmful Behaviors and Social Network Message
Writing Style [0.0]
We consider whether measurable differences in writing style relate to different personality types.
We study messages from nearly 2,500 users from two online communities (Twitter and Reddit)
We find that we can measure significant personality differences between regular and harmful users from the writing style of as few as 100 tweets or 40 Reddit posts.
arXiv Detail & Related papers (2022-12-14T22:13:55Z) - Twitter Users' Behavioral Response to Toxic Replies [1.2387676601792899]
We studied the impact of toxicity on users' online behavior on Twitter.
We found that toxicity victims show a combination of the following behavioral reactions: avoidance, revenge, countermeasures, and negotiation.
Our results can assist further studies in developing more effective detection and intervention methods for reducing the negative consequences of toxicity on social media.
arXiv Detail & Related papers (2022-10-24T17:36:58Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z) - Aligning to Social Norms and Values in Interactive Narratives [89.82264844526333]
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games.
We introduce the GALAD agent that uses the social commonsense knowledge present in specially trained language models to contextually restrict its action space to only those actions that are aligned with socially beneficial values.
arXiv Detail & Related papers (2022-05-04T09:54:33Z) - A deep dive into the consistently toxic 1% of Twitter [9.669275987983447]
This study spans 14 years of tweets from 122K Twitter profiles and more than 293M tweets.
We selected the most extreme profiles in terms of consistency of toxic content and examined their tweet texts, and the domains, hashtags, and URLs they shared.
We found that these selected profiles keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other, and have a high likelihood of bot-like behavior.
arXiv Detail & Related papers (2022-02-16T04:21:48Z) - Annotators with Attitudes: How Annotator Beliefs And Identities Bias
Toxic Language Detection [75.54119209776894]
We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations.
We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity.
Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
arXiv Detail & Related papers (2021-11-15T18:58:20Z) - ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter [5.723363140737726]
This paper provides a dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr)
Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment.
Our observations show that individual tweets do not provide sufficient evidence for toxic behavior, and meaningful use of context in interactions can enable highlighting or exonerating tweets with purported toxicity.
arXiv Detail & Related papers (2020-08-14T17:02:55Z) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.