arXiv subjects

Stefano Cresci

Publications and source records attributed to Stefano Cresci.

At least 37 records · Page 2Linked to original sources

arXiv2025

Improving Regulatory Oversight in Online Content Moderation

The European Union introduced the Digital Services Act (DSA) to address the risks associated with digital platforms and promote a safer online environment. However, despite the potential of components such as the Transparency Database, Transparency Reports, and Article 40 of the DSA to improve platform transparency, significant challenges remain. These include data inconsistencies and a lack of detailed information, which hinder transparency in content moderation practices. Additionally, the absence of standardized reporting structures makes cross-platform comparisons and broader analyses difficult. To address these issues, we propose two complementary processes: a Transparency Report Cross-Checking Process and a Verification Process. Their goal is to provide both internal and external validation by detecting possible inconsistencies between self-reported and actual platform data, assessing compliance levels, and ultimately enhancing transparency while improving the overall effectiveness of the DSA in ensuring accountability in content moderation. Additionally, these processes can benefit policymakers by providing more accurate data for decision-making, independent researchers with trustworthy analysis, and platforms by offering a method for self-assessment and improving compliance and reporting practices.

cs.CY↗

arXiv2025

A Year of the DSA Transparency Database: What it (Does Not) Reveal About Platform Moderation During the 2024 European Parliament Election

Social media platforms face heightened risks during major political events; yet, how platforms adapt their moderation practices in response remains unclear. The Digital Services Act Transparency Database offers an unprecedented opportunity to systematically study content moderation at scale, enabling researchers and policymakers to assess platforms' compliance and effectiveness. Herein, we analyze 1.58 billion self-reported moderation actions taken by eight large social media platforms during an extended period of eight months surrounding the 2024 European Parliament elections. Our findings reveal a lack of adaptation in moderation strategies, as platforms did not exhibit significant changes in their enforcement behaviors surrounding the elections. This raises concerns about whether platforms adapted their moderation practices at all, or if structural limitations of the database concealed possible adjustments. Moreover, we found that noted transparency and accountability issues persist nearly a year after initial concerns were raised. These results highlight the limitations of current self-regulatory approaches and underscore the need for stronger enforcement and data access mechanisms to ensure that online platforms uphold their responsibility in safeguarding democratic processes.

cs.CY↗

arXiv2025

Coordinated Inauthentic Behavior and Information Spreading on Twitter

We explore the effects of coordinated users (i.e., users characterized by an unexpected, suspicious, or exceptional similarity) in information spreading on Twitter by quantifying the efficacy of their tactics in deceiving feed algorithms to maximize information outreach. In particular, we investigate the behavior of coordinated accounts within a large set of retweet-based information cascades identifying key differences between coordinated and non-coordinated accounts in terms of position within the cascade, action delay and outreach. On average, coordinated accounts occupy higher positions of the information cascade (i.e., closer to the root), spread messages faster and involve a slightly higher number of users. When considering cascade metrics such as size, number of edges and height, we observe clear differences among information cascades that are associated to a systematically larger proportion of coordinated accounts, as confirmed by comparisons with statistical null models. To further characterize the activity of coordinated accounts we introduce two new measures capturing their infectivity within the information cascade (i.e., their ability to involve other users) and their interaction with non-coordinated accounts. Finally, we find that the interaction pattern between the two classes of users follows a saturation-like process. A larger-scale targeting of non-coordinated users does not require a larger amount of coordinated accounts after a threshold value approximately 50%, after which involving more coordinated accounts within a cascade yields a null marginal effect. Our results contribute to shed light on the role of coordinated accounts and their effect on information diffusion.

cs.SI↗

arXiv2025

Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs

Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic-Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10k event-related tweets, achieving $F1=0.66$. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain $F1 \leq 0.55$. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.

cs.CL↗

arXiv2025

The Great Ban: Efficacy and Unintended Consequences of a Massive Deplatforming Operation on Reddit

In the current landscape of online abuses and harms, effective content moderation is necessary to cultivate safe and inclusive online spaces. Yet, the effectiveness of many moderation interventions is still unclear. Here, we assess the effectiveness of The Great Ban, a massive deplatforming operation that affected nearly 2,000 communities on Reddit. By analyzing 16M comments posted by 17K users during 14 months, we provide nuanced results on the effects, both desired and otherwise, of the ban. Among our main findings is that 15.6% of the affected users left Reddit and that those who remained reduced their toxicity by 6.6% on average. The ban also caused 5% users to increase their toxicity by more than 70% of their pre-ban level. Overall, our multifaceted results provide new insights into the efficacy of deplatforming. As such, our findings can inform the development of future moderation interventions and the policing of online platforms.

cs.SI↗

arXiv2025

Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation

AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.

cs.HC↗

arXiv2025

The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media

Since September 2023, the Digital Services Act (DSA) obliges large online platforms to submit detailed data on each moderation action they take within the European Union (EU) to the DSA Transparency Database. From its inception, this centralized database has sparked scholarly interest as an unprecedented and potentially unique trove of data on real-world online moderation. Here, we thoroughly analyze all 353.12M records submitted by the eight largest social media platforms in the EU during the first 100 days of the database. Specifically, we conduct a platform-wise comparative study of their: volume of moderation actions, grounds for decision, types of applied restrictions, types of moderated content, timeliness in undertaking and submitting moderation actions, and use of automation. Furthermore, we systematically cross-check the contents of the database with the platforms' own transparency reports. Our analyses reveal that (i) the platforms adhered only in part to the philosophy and structure of the database, (ii) the structure of the database is partially inadequate for the platforms' reporting needs, (iii) the platforms exhibited substantial differences in their moderation actions, (iv) a remarkable fraction of the database data is inconsistent, (v) the platform X (formerly Twitter) presents the most inconsistencies. Our findings have far-reaching implications for policymakers and scholars across diverse disciplines. They offer guidance for future regulations that cater to the reporting needs of online platforms in general, but also highlight opportunities to improve and refine the database itself.

cs.SI↗

arXiv2024

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid sensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

cs.HC↗

arXiv2024

Toxic Bias: Perspective API Misreads German as More Toxic

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

cs.SI↗

arXiv2024

Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out the first dynamic analysis of coordinated behavior. To reach our goal we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. Thanks to our novel approach we find that: (i) coordinated communities feature variable degrees of temporal instability; (ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; (iii) some users exhibit distinct archetypal behaviors that have important practical implications; (iv) content and network characteristics contribute to explaining why users leave and join coordinated communities. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of coordinated communities, and on the patterns of online influence.

cs.SI↗

arXiv2024

Misinformation and Polarization around COVID-19 vaccines in France, Germany, and Italy

The kick-off of vaccination campaigns in Europe, starting in late December 2020, has been followed by the online spread of controversies and conspiracies surrounding vaccine validity and efficacy. We study Twitter discussions in three major European languages (Italian, German, and French) during the vaccination campaign. Moving beyond content analysis to explore the structural aspects of online discussions, our investigation includes an analysis of polarization and the potential formation of echo chambers, revealing nuanced behavioral and topical differences in user interactions across the analyzed countries. Notably, we identify strong anti- and pro-vaccine factions exhibiting heterogeneous temporal polarization patterns in different countries. Through a detailed examination of news-sharing sources, we uncover the widespread use of other media platforms like Telegram and YouTube for disseminating low-credibility information, indicating a concerning trend of diminishing news credibility over time. Our findings on Twitter discussions during the COVID-19 vaccination campaign in major European languages expose nuanced behavioral distinctions, revealing the profound impact of polarization and the emergence of distinct anti-vaccine and pro-vaccine advocates over time.

cs.SI↗

arXiv2023

Dynamics of toxic behavior in the Covid-19 vaccination debate

In this paper, we study the behavior of users on Online Social Networks in the context of Covid-19 vaccines in Italy. We identify two main polarized communities: Provax and Novax. We find that Novax users are more active, more clustered in the network, and share less reliable information compared to the Provax users. On average, Novax are more toxic than Provax. However, starting from June 2021, the Provax became more toxic than the Novax. We show that the change in trend is explained by the aggregation of some contagion effects and the change in the activity level within communities. In fact, we establish that Provax users who increase their intensity of activity after May 2021 are significantly more toxic than the other users, shifting the toxicity up within the Provax community. Our study suggests that users presenting a spiky activity pattern tend to be more toxic.

cs.SI↗

arXiv2023

User's Reaction Patterns in Online Social Network Communities

Several one-fits-all intervention policies were introduced by the Online Social Networks (OSNs) platforms to mitigate potential harms. Nevertheless, some studies showed the limited effectiveness of these approaches. An alternative to this would be a user-centered design of intervention policies. In this context, we study the susceptibility of users to undesired behavior in communities on OSNs. In particular, we explore their reaction to specific events. Our study shows that communities develop different undesired behavior patterns in reaction to specific events. These events can significantly alter the behavior of the community and invert the dynamics of behavior within the whole network. Our findings stress out the importance of understanding the reasons behind the changes in users' reactions and highlights the need of fine-tuning the research to the individual's level. It paves the way towards building better OSNs' intervention strategies centered on the user.

cs.SI↗

arXiv2023

Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack

The aftermath of the 2020 US Presidential Election witnessed an unprecedented attack on the democratic values of the country through the violent insurrection at Capitol Hill on January 6th, 2021. The attack was fueled by the proliferation of conspiracy theories and misleading claims about the integrity of the election pushed by political elites and fringe communities on social media. In this study, we explore the evolution of fringe content and conspiracy theories on Twitter in the seven months leading up to the Capitol attack. We examine the suspicious coordinated activity carried out by users sharing fringe content, finding evidence of common adversarial manipulation techniques ranging from targeted amplification to manufactured consensus. Further, we map out the temporal evolution of, and the relationship between, fringe and conspiracy theories, which eventually coalesced into the rhetoric of a stolen election, with the hashtag #stopthesteal, alongside QAnon-related narratives. Our findings further highlight how social media platforms offer fertile ground for the widespread proliferation of conspiracies during major societal events, which can potentially lead to offline coordinated actions and organized violence.

cs.SI↗

arXiv2023

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.

cs.SI↗

arXiv2023

One of Many: Assessing User-level Effects of Moderation Interventions on r/The_Donald

Evaluating the effects of moderation interventions is a task of paramount importance, as it allows assessing the success of content moderation processes. So far, intervention effects have been almost solely evaluated at the aggregated platform or community levels. Here, we carry out a multidimensional evaluation of the user-level effects of the sequence of moderation interventions that targeted r/The_Donald: a community of Donald Trump adherents on Reddit. We demonstrate that the interventions: 1) strongly reduced user activity; 2) slightly increased the diversity of the subreddits in which users participated; 3) slightly reduced user toxicity; and 4) gave way to the sharing of less factual and more politically biased news. Importantly, we also find that interventions having strong community level effects are associated to extreme and diversified user-level reactions. Our results highlight that community-level effects are not always representative of the underlying behavior of individuals or smaller user groups. We conclude by discussing the practical and ethical implications of our results. Overall, our findings can inform the development of targeted moderation interventions and provide useful guidance for policing online platforms.

cs.SI↗

arXiv2022

A Survey on Multimodal Disinformation Detection

Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation. While initially this was mostly about textual content, over time images and videos gained popularity, as they are much easier to consume, attract more attention, and spread further than text. As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content. In this study, we offer a survey on the state-of-the-art on multimodal disinformation detection covering various combinations of modalities: text, images, speech, video, social media network structure, and temporal information. Moreover, while some studies focused on factuality, others investigated how harmful the content is. While these two components in the definition of disinformation (i) factuality, and (ii) harmfulness, are equally important, they are typically studied in isolation. Thus, we argue for the need to tackle disinformation detection by taking into account multiple modalities as well as both factuality and harmfulness, in the same framework. Finally, we discuss current challenges and future research directions

cs.MM↗

arXiv2022

MulBot: Unsupervised Bot Detection Based on Multivariate Time Series

Online social networks are actively involved in the removal of malicious social bots due to their role in the spread of low quality information. However, most of the existing bot detectors are supervised classifiers incapable of capturing the evolving behavior of sophisticated bots. Here we propose MulBot, an unsupervised bot detector based on multivariate time series (MTS). For the first time, we exploit multidimensional temporal features extracted from user timelines. We manage the multidimensionality with an LSTM autoencoder, which projects the MTS in a suitable latent space. Then, we perform a clustering step on this encoded representation to identify dense groups of very similar users -- a known sign of automation. Finally, we perform a binary classification task achieving f1-score $= 0.99$, outperforming state-of-the-art methods (f1-score $\le 0.97$). Not only does MulBot achieve excellent results in the binary classification task, but we also demonstrate its strengths in a novel and practically-relevant task: detecting and separating different botnets. In this multi-class classification task we achieve f1-score $= 0.96$. We conclude by estimating the importance of the different features used in our model and by evaluating MulBot's capability to generalize to new unseen bots, thus proposing a solution to the generalization deficiencies of supervised bot detectors.

cs.SI↗