Benedetta Tessa

Aldo Cerulli, Benedetta Tessa, Giuseppe La Selva et al.

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.

Amaury Trujillo, Benedetta Tessa, Stefano Cresci

The European Commission recently introduced new regulation to harmonize transparency reporting of large online platforms under the Digital Services Act (DSA). Here, we present the first systematic evaluation of transparency reporting data quality after this normative change, for the eight largest social media platforms in the European Union. In detail, we run a set of large-scale quantitative analyses on key reporting dimensions, followed by a structured comparative assessment across platforms and reporting mechanisms. Among our findings is that: (i) the analyzed platforms had varying degrees of compliance and data quality, but all exhibited issues on data formatting, timeliness, consistency, and completeness; (ii) some platforms employed differing reporting procedures across mechanisms, which caused them to submit contrasting information; (iii) despite the harmonization, a number of issues still prevent interoperability between reporting mechanisms; and (iv) many of the previously identified issues with transparency reporting are still unresolved. We conclude by discussing implications for transparency auditing and proposing key targeted improvements to strengthen the reliability and interoperability of DSA transparency reporting.

Benedetta Tessa, Gautam Kishore Shahi, Amaury Trujillo et al.

During major political events, social media platforms encounter increased systemic risks. However, it is still unclear if and how they adjust their moderation practices in response. The Digital Services Act Transparency Database provides-for the first time-an opportunity to systematically examine content moderation at scale, allowing researchers and policymakers to evaluate platforms' compliance and effectiveness, especially at high-stakes times. Here we analyze 1.58 billion self-reported moderation actions by the eight largest social media platforms in Europe over an eight-month period surrounding the 2024 European Parliament elections. We found that platforms did not exhibit meaningful signs of adaptation in moderation strategies as their self-reported enforcement patterns did not change significantly around the elections. This raises questions about whether platforms made any concrete adjustments, or whether the structure of the database may have masked them. On top of that, we reveal that initial concerns regarding platforms' transparency and accountability still persist one year after the launch of the Transparency Database. Our findings highlight the limits of current self-regulatory approaches and point to the need for stronger enforcement and better data access mechanisms to ensure that online platforms meet their responsibilities in protecting the democratic processes.

Benedetta Tessa, Alejandro Moreo, Stefano Cresci et al.

Accurately estimating how users respond to moderation interventions is paramount for developing effective and user-centred moderation strategies. However, this requires a clear understanding of which user characteristics are associated with different behavioural responses, which is the goal of this work. We investigate the informativeness of 753 socio-behavioural, linguistic, relational, and psychological features, in predicting the behavioural changes of 16.8K users affected by a major moderation intervention on Reddit. To reach this goal, we frame the problem in terms of "quantification", a task well-suited to estimating shifts in aggregate user behaviour. We then apply a greedy feature selection strategy with the double goal of (i) identifying the features that are most predictive of changes in user activity, toxicity, and participation diversity, and (ii) estimating their importance. Our results allow identifying a small set of features that are consistently informative across all tasks, and determining that many others are either task-specific or of limited utility altogether. We also find that predictive performance varies according to the task, with changes in activity and toxicity being easier to estimate than changes in diversity. Overall, our results pave the way for the development of accurate systems that predict user reactions to moderation interventions. Furthermore, our findings highlight the complexity of post-moderation user behaviour, and indicate that effective moderation should be tailored not only to user traits but also to the specific objective of the intervention.