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arxiv: 2512.10113 · v2 · submitted 2025-12-10 · 💻 cs.CY · cs.HC

Dark Personality Traits and Online Toxicity: Linking Self-Reports to Reddit Activity

Aldo Cerulli , Benedetta Tessa , Giuseppe La Selva , Oronzo Mazzeo , Lorenzo Cima , Lucia Monacis , Stefano Cresci This is my paper

Pith reviewed 2026-05-16 22:59 UTC · model grok-4.3

classification 💻 cs.CY cs.HC
keywords dark personality traitsonline toxicityreddit activityself-reportslinguistic featuresincivilitycomputational social sciencepersonality and behavior
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0 comments X

The pith

Dark personality traits associate with self-reported uncivil online behavior but show no link to actual linguistic toxicity in Reddit comments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper links validated questionnaire measures of dark personality traits to participants' real Reddit activity through a secure web app, creating a dataset of 57,000 comments from 114 users. It finds that these traits reliably predict what people say about their own engagement in uncivil interactions. Yet none of the standard dark trait dimensions forecast the toxicity or linguistic patterns actually present in the comment text. Self-reported experiences of giving or receiving toxic behavior do align with features such as negativity, moral language, and emotional intensity. The work therefore shows a clear separation between stable personality constructs and the surface signals extractable from online language.

Core claim

Validated dark personality dimensions correlate consistently with self-reported engagement in uncivil online interactions, but no such dimension significantly predicts text-derived toxicity scores or other linguistic features from the Reddit comments. Self-reported experiences of engaging in or being targeted by toxic behavior, however, show robust associations with negativity, moral framing, and emotional intensity in users' language. The findings indicate a gap between personality traits and their expression in observable linguistic signals, with computational features capturing behavioral engagement but not serving as reliable proxies for underlying traits.

What carries the argument

A secure web application that links validated psychological questionnaire responses collected via Amazon Mechanical Turk to participants' actual Reddit comment history, yielding linguistic and behavioral features from 57K comments.

If this is right

  • Computational linguistic features can capture self-reported behavioral engagement in online incivility.
  • Validated dark personality measures do not serve as reliable proxies for text-based toxicity within the current feature set.
  • Grounding computational approaches in validated psychological measures remains necessary for linking personality to online behavior.
  • Richer, context-aware representations are needed to connect stable traits with observable language patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Platforms could prioritize behavioral language signals over personality profiles when designing toxicity detection systems.
  • Longitudinal tracking of the same users might reveal whether dark traits predict future behavior even if they miss current language patterns.
  • Refining toxicity proxies to include thread context or reply chains could close the observed gap between self-reports and text features.

Load-bearing premise

The selected linguistic and behavioral features extracted from Reddit comments are valid and sufficient proxies for online toxicity, and self-report measures accurately reflect real behavior.

What would settle it

A larger replication study in which any validated dark personality dimension significantly predicts text-derived toxicity scores or linguistic markers in Reddit-style data.

Figures

Figures reproduced from arXiv: 2512.10113 by Aldo Cerulli, Benedetta Tessa, Giuseppe La Selva, Lorenzo Cima, Lucia Monacis, Oronzo Mazzeo, Stefano Cresci.

Figure 1
Figure 1. Figure 1: Overview of our approach for linking answers to a validated psychological questionnaire with [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of self-reported trait and trolling behavior scores. Each vertical axis represents one of [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of the combinations of dimensions exhibited by participants in our study. Dimensions [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the combinations of platforms used, in addition to Reddit, by participants in our [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Spearman rank correlation coefficients between dimension scores and social media habits ( [PITH_FULL_IMAGE:figures/full_fig_p020_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Spearman rank correlation coefficients between dimension scores and social media habits ( [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Spearman rank correlation coefficients between dimension scores and social media habits ( [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗
read the original abstract

Dark personality traits have long been associated with antisocial and toxic online behaviors, yet their relationship with observable online activity remains unclear. We investigate the association between validated dark personality measures, self-reported experiences of online incivility, and linguistic and behavioral features extracted from real-world user activity. To this end, we developed a Web application that securely links responses to validated psychological questionnaires collected via Amazon Mechanical Turk with participants' Reddit activity. This yielded a dataset of nearly 57K comments (2.2M tokens) from 114 users, represented through a broad set of linguistic and behavioral features. Our analyses reveal a clear distinction between self-reported and observed behavior. Dark personality traits show consistent associations with self-reported engagement in uncivil interactions. However, no validated dark personality dimension significantly predicts text-derived toxicity or linguistic features. In contrast, self-reported experiences of engaging in or being targeted by toxic behavior are robustly reflected in users' language, exhibiting consistent associations with measures of negativity, moral framing, and emotional intensity. Taken together, these findings highlight a gap between stable personality traits and their manifestation in surface-level linguistic signals. While computational features effectively capture behavioral engagement in online incivility, they do not provide reliable proxies for underlying personality constructs within the present framework. Our results underscore the importance of grounding computational approaches in validated psychological measures and point to the need for richer, context-aware representations to better understand the relationship between personality and online behavior.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript examines associations between validated dark personality trait measures, self-reported experiences of online incivility, and a broad set of linguistic and behavioral features extracted from nearly 57K Reddit comments (2.2M tokens) by 114 MTurk participants whose accounts were securely linked via a custom web app. It reports consistent positive associations between dark traits and self-reported uncivil engagement, but no significant predictions of text-derived toxicity scores or linguistic markers (negativity, moral framing, emotional intensity) by any dark personality dimension. Self-reported incivility experiences, by contrast, show robust correlations with those same linguistic features. The authors conclude that computational features capture behavioral engagement but are unreliable proxies for underlying personality constructs.

Significance. If the dissociation holds after addressing proxy validation, the work offers a valuable empirical demonstration that self-report measures of dark traits align with reported online behavior while observable linguistic signals do not, highlighting limits of current text-based proxies in computational social science. The linkage of validated psychological scales to real Reddit activity on a dataset of this scale is a clear methodological strength and supports calls for richer, context-aware representations.

major comments (3)
  1. [Methods] Methods section: No details are provided on the specific toxicity classifier (model architecture, training corpus, calibration on Reddit data, or performance metrics such as precision/recall against human annotations for sarcasm and context). This is load-bearing for the central null result on dark traits, as substantial measurement error in the automated features would attenuate correlations with stable traits while permitting coarser self-report associations to remain detectable at N=114.
  2. [Results] Results section: The analyses involve a broad feature set and multiple personality and linguistic variables, yet no correction for multiple comparisons is mentioned. This affects interpretation of both the reported self-report correlations and the null personality-to-toxicity findings.
  3. [Discussion] Discussion section: The claim that 'computational features effectively capture behavioral engagement in online incivility' but 'do not provide reliable proxies for underlying personality constructs' requires explicit discussion of feature limitations (e.g., lack of thread context or sarcasm handling) to justify the dissociation as substantive rather than methodological.
minor comments (2)
  1. [Abstract] Abstract: The sample size (N=114) and comment volume should be stated with greater precision regarding inclusion criteria and any data filtering steps.
  2. [Methods] The manuscript would benefit from a table summarizing the exact linguistic and behavioral features used and their extraction methods.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight important areas for clarification and strengthening of the manuscript. We address each major comment point by point below, outlining the specific revisions we will implement.

read point-by-point responses

  1. Referee: [Methods] Methods section: No details are provided on the specific toxicity classifier (model architecture, training corpus, calibration on Reddit data, or performance metrics such as precision/recall against human annotations for sarcasm and context). This is load-bearing for the central null result on dark traits, as substantial measurement error in the automated features would attenuate correlations with stable traits while permitting coarser self-report associations to remain detectable at N=114.

    Authors: We agree that the Methods section requires additional detail on the toxicity classifier to allow proper evaluation of the null results. In the revised manuscript, we will expand this section to specify the exact model (including architecture and source), training corpus, any calibration or fine-tuning performed on Reddit-style data, and performance metrics such as precision, recall, and F1 scores, with explicit discussion of sarcasm and context handling. These additions will directly address concerns about measurement error. revision: yes

  2. Referee: [Results] Results section: The analyses involve a broad feature set and multiple personality and linguistic variables, yet no correction for multiple comparisons is mentioned. This affects interpretation of both the reported self-report correlations and the null personality-to-toxicity findings.

    Authors: The referee is correct that multiple-comparison correction is needed given the number of tests performed. We will revise the Results section to apply FDR correction (or Bonferroni, as appropriate) and report both uncorrected and corrected p-values. This will provide a more rigorous view of the self-report associations; the null findings for dark traits predicting toxicity features remain robust, as none approached significance even without correction. revision: yes

  3. Referee: [Discussion] Discussion section: The claim that 'computational features effectively capture behavioral engagement in online incivility' but 'do not provide reliable proxies for underlying personality constructs' requires explicit discussion of feature limitations (e.g., lack of thread context or sarcasm handling) to justify the dissociation as substantive rather than methodological.

    Authors: We appreciate this suggestion and will expand the Discussion to include a dedicated paragraph on the limitations of the extracted linguistic features. This will explicitly cover the absence of full thread context, challenges in sarcasm detection, reliance on surface-level markers, and other constraints. These additions will clarify that the observed dissociation reflects substantive differences between self-report and observable signals rather than solely methodological artifacts. revision: yes

Circularity Check

0 steps flagged

Empirical correlation study with no derivation chain or self-referential reductions

full rationale

The paper is a data-driven empirical analysis that collects self-report questionnaire data via MTurk, links it to real Reddit comments, extracts standard linguistic and behavioral features, and reports statistical associations. No equations, first-principles derivations, fitted parameters renamed as predictions, or uniqueness theorems appear in the abstract or described methods. All central claims rest on observable data and externally validated scales rather than any step that reduces to its own inputs by construction. Self-citations, if present, are not load-bearing for the reported associations. This is a standard non-circular empirical design.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the assumption that validated dark personality scales measure stable traits accurately and that the chosen linguistic features are faithful proxies for toxicity; no new entities or fitted parameters are introduced.

axioms (2)
  • domain assumption Validated self-report questionnaires accurately capture dark personality traits and experiences of online incivility.
    Invoked throughout the abstract when interpreting associations between questionnaire scores and observed behavior.
  • domain assumption Linguistic and behavioral features extracted from Reddit comments are valid indicators of toxicity.
    Central to the claim that no dark trait predicts text-derived toxicity.

pith-pipeline@v0.9.0 · 5579 in / 1267 out tokens · 22639 ms · 2026-05-16T22:59:17.743942+00:00 · methodology

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