Pub Date : 2025-12-15DOI: 10.1016/j.osnem.2025.100344
Jacopo Nudo , Mario Edoardo Pandolfo , Edoardo Loru , Mattia Samory , Matteo Cinelli , Walter Quattrociocchi
We investigate how Large Language Models (LLMs) behave when simulating political discourse on social media. Leveraging 21 million interactions on X during the 2024 U.S. presidential election, we construct LLM agents based on 1186 real users, prompting them to reply to politically salient tweets under controlled conditions. Agents are initialized either with minimal ideological cues (Zero Shot) or recent tweet history (Few Shot), allowing one-to-one comparisons with human replies. We evaluate three model families — Gemini, Mistral, and DeepSeek — across linguistic style, ideological consistency, and toxicity. We find that richer contextualization improves internal consistency but also amplifies polarization, stylized signals, and harmful language. We observe an emergent distortion that we call “generation exaggeration”: a systematic amplification of salient traits beyond empirical baselines. Our analysis shows that LLMs do not emulate users, they reconstruct them. Their outputs, indeed, reflect internal optimization dynamics more than observed behavior, introducing structural biases that compromise their reliability as social proxies. This challenges their use in content moderation, deliberative simulations, and policy modeling.
{"title":"Generative exaggeration in LLM social agents: Consistency, bias, and toxicity","authors":"Jacopo Nudo , Mario Edoardo Pandolfo , Edoardo Loru , Mattia Samory , Matteo Cinelli , Walter Quattrociocchi","doi":"10.1016/j.osnem.2025.100344","DOIUrl":"10.1016/j.osnem.2025.100344","url":null,"abstract":"<div><div>We investigate how Large Language Models (LLMs) behave when simulating political discourse on social media. Leveraging 21 million interactions on X during the 2024 U.S. presidential election, we construct LLM agents based on 1186 real users, prompting them to reply to politically salient tweets under controlled conditions. Agents are initialized either with minimal ideological cues (Zero Shot) or recent tweet history (Few Shot), allowing one-to-one comparisons with human replies. We evaluate three model families — Gemini, Mistral, and DeepSeek — across linguistic style, ideological consistency, and toxicity. We find that richer contextualization improves internal consistency but also amplifies polarization, stylized signals, and harmful language. We observe an emergent distortion that we call “generation exaggeration”: a systematic amplification of salient traits beyond empirical baselines. Our analysis shows that LLMs do not emulate users, they reconstruct them. Their outputs, indeed, reflect internal optimization dynamics more than observed behavior, introducing structural biases that compromise their reliability as social proxies. This challenges their use in content moderation, deliberative simulations, and policy modeling.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"51 ","pages":"Article 100344"},"PeriodicalIF":2.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.osnem.2025.100342
Gianluca Nogara , Erfan Samieyan Sahneh , Matthew R. DeVerna , Nick Liu , Luca Luceri , Filippo Menczer , Francesco Pierri , Silvia Giordano
Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform’s public launch, an influx of new users — particularly those posting in English and Japanese — contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky’s moderation efforts have been effective.
{"title":"A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch","authors":"Gianluca Nogara , Erfan Samieyan Sahneh , Matthew R. DeVerna , Nick Liu , Luca Luceri , Filippo Menczer , Francesco Pierri , Silvia Giordano","doi":"10.1016/j.osnem.2025.100342","DOIUrl":"10.1016/j.osnem.2025.100342","url":null,"abstract":"<div><div>Bluesky is a decentralized, Twitter-like social media platform that has rapidly gained popularity. Following an invite-only phase, it officially opened to the public on February 6th, 2024, leading to a significant expansion of its user base. In this paper, we present a longitudinal analysis of user activity in the two months surrounding its public launch, examining how the platform evolved due to this rapid growth. Our analysis reveals that Bluesky exhibits an activity distribution comparable to more established social platforms, yet it features a higher volume of original content relative to reshared posts and maintains low toxicity levels. We further investigate the political leanings of its user base, misinformation dynamics, and engagement in harmful conversations. Our findings indicate that Bluesky users predominantly lean left politically and tend to share high-credibility sources. After the platform’s public launch, an influx of new users — particularly those posting in English and Japanese — contributed to a surge in activity. Among them, several accounts displayed suspicious behaviors, such as mass-following users and sharing content from low-credibility news sources. Some of these accounts have already been flagged as spam or suspended, suggesting that Bluesky’s moderation efforts have been effective.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"51 ","pages":"Article 100342"},"PeriodicalIF":2.9,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-21DOI: 10.1016/j.osnem.2025.100343
Youssef Gamha, Lotfi Ben Romdhane
Trust in online social networks (OSNs) is inherently dynamic, shaped by evolving user interactions, contextual shifts and behavioral changes. Traditional static trust models struggle to adapt to these fluid dynamics, limiting their applicability in real-time environments. This paper proposes DUTrust, a novel dynamic trust assessment framework that leverages Liquid Neural Networks (LNNs) to continuously update trust scores based on temporal, relational and context-sensitive factors. The DUTrust model integrates multiple facets of user behavior including user profile, interaction patterns, shared interests, network influence, and reciprocity into a unified model. This holistic data consolidation is a significant contribution, as it facilitates adaptive trust computation through LNNs’ real-time temporal reasoning. Experiments on real-world Twitter datasets demonstrate DUTrust’s effectiveness in predicting trustworthiness with high accuracy and adaptability to evolving user behavior.
{"title":"Dynamic user trust assessment in online social networks using liquid neural networks","authors":"Youssef Gamha, Lotfi Ben Romdhane","doi":"10.1016/j.osnem.2025.100343","DOIUrl":"10.1016/j.osnem.2025.100343","url":null,"abstract":"<div><div>Trust in online social networks (OSNs) is inherently dynamic, shaped by evolving user interactions, contextual shifts and behavioral changes. Traditional static trust models struggle to adapt to these fluid dynamics, limiting their applicability in real-time environments. This paper proposes DUTrust, a novel dynamic trust assessment framework that leverages Liquid Neural Networks (LNNs) to continuously update trust scores based on temporal, relational and context-sensitive factors. The DUTrust model integrates multiple facets of user behavior including user profile, interaction patterns, shared interests, network influence, and reciprocity into a unified model. This holistic data consolidation is a significant contribution, as it facilitates adaptive trust computation through LNNs’ real-time temporal reasoning. Experiments on real-world Twitter datasets demonstrate DUTrust’s effectiveness in predicting trustworthiness with high accuracy and adaptability to evolving user behavior.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"51 ","pages":"Article 100343"},"PeriodicalIF":2.9,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145555037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1016/j.osnem.2025.100341
Ahmad Zareie, Mehmet E. Bakir, Mark A. Greenwood, Kalina Bontcheva, Carolina Scarton
The proliferation of coordinated campaigns on Online Social Networks (OSNs) has raised increasing concerns over the last decade. These campaigns typically involve organised efforts by multiple accounts to manipulate public discourse or amplify particular narratives, and may include disinformation, astroturfing, or other influence operations. Therefore, identifying coordinated accounts and detecting the content they promote has become a critical challenge in OSN analysis. Existing methods for coordination detection focus mainly on the idea that accounts repeatedly sharing similar content are coordinated accounts. Since these methods ignore how this sharing behaviour differs from that of non-coordinated (regular) accounts, they may misidentify highly active accounts as coordinated accounts. To fill this gap, this paper proposes a novel method to detect coordination by looking for anomalies in accounts’ sharing behaviour. This method takes into account the extent to which the sharing behaviour of coordinated accounts diverges from that of regular accounts. Experimental results indicate that our approach is superior to the compared baselines for detecting coordination despite not requiring training or threshold optimisation.
{"title":"Identifying coordination in online social networks through anomalous sharing behaviour","authors":"Ahmad Zareie, Mehmet E. Bakir, Mark A. Greenwood, Kalina Bontcheva, Carolina Scarton","doi":"10.1016/j.osnem.2025.100341","DOIUrl":"10.1016/j.osnem.2025.100341","url":null,"abstract":"<div><div>The proliferation of coordinated campaigns on Online Social Networks (OSNs) has raised increasing concerns over the last decade. These campaigns typically involve organised efforts by multiple accounts to manipulate public discourse or amplify particular narratives, and may include disinformation, astroturfing, or other influence operations. Therefore, identifying coordinated accounts and detecting the content they promote has become a critical challenge in OSN analysis. Existing methods for coordination detection focus mainly on the idea that accounts repeatedly sharing similar content are coordinated accounts. Since these methods ignore how this sharing behaviour differs from that of non-coordinated (regular) accounts, they may misidentify highly active accounts as coordinated accounts. To fill this gap, this paper proposes a novel method to detect coordination by looking for anomalies in accounts’ sharing behaviour. This method takes into account the extent to which the sharing behaviour of coordinated accounts diverges from that of regular accounts. Experimental results indicate that our approach is superior to the compared baselines for detecting coordination despite not requiring training or threshold optimisation.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100341"},"PeriodicalIF":2.9,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-04DOI: 10.1016/j.osnem.2025.100340
Hichem Merini , Adil Imad Eddine Hosni , Kadda Beghdad Bey , Vincenzo Lomonaco , Marco Podda , Islem Baira
In today’s digital landscape, online social networks (OSNs) facilitate rapid information dissemination. However, they also serve as conduits for misinformation, leading to severe real-world consequences such as public panic, social unrest, and the erosion of institutional trust. Existing rumor influence minimization strategies predominantly rely on static models or specific diffusion mechanisms, restricting their ability to dynamically adapt to the evolving nature of misinformation. To address this gap, this paper proposes a novel misinformation influence mitigation framework that integrates Graph Neural Networks (GNNs) with continual learning and employs a Node Blocking strategy as its intervention approach. The framework comprises three key components: (1) a Dataset Generator, (2) a GNN Model Trainer, and (3) an Influential Node Identifier. Given the scarcity of real-world data on misinformation propagation, the first component simulates misinformation diffusion processes within social networks, leveraging the Human Individual and Social Behavior (HISB) model as a case study. The second component employs GNNs to learn from these synthetic datasets and predict the most influential nodes susceptible to misinformation. Subsequently, these nodes are strategically targeted and blocked to minimize further misinformation spread. Finally, the continual learning mechanism ensures the model dynamically adapts to evolving network structures and propagation patterns. Beyond evaluating the Human Individual and Social Behavior (HISB) propagation model, we empirically demonstrate that our framework is propagation-model agnostic by reproducing the pipeline under Independent Cascade and Linear Threshold with consistent gains over baselines. Finally, we introduce a truth-aware intervention rule that gates and weights actions by an external veracity score at detection time, selecting most influential nodes. This addition ensures interventions are enacted only when content is likely false, aligning the method with responsible deployment. Experimental evaluations conducted on multiple benchmark datasets demonstrate the superiority of the proposed node blocking framework over state-of-the-art methods. Our results indicate a statistically significant reduction in misinformation spread, with non-parametric statistical tests yielding -values below 0.001 (p0.001), confirming the robustness of our approach. This work presents a scalable and adaptable solution for misinformation containment, contributing to the development of more reliable and trustworthy online information ecosystems.
{"title":"Misinformation mitigation in online social networks using continual learning with graph neural networks","authors":"Hichem Merini , Adil Imad Eddine Hosni , Kadda Beghdad Bey , Vincenzo Lomonaco , Marco Podda , Islem Baira","doi":"10.1016/j.osnem.2025.100340","DOIUrl":"10.1016/j.osnem.2025.100340","url":null,"abstract":"<div><div>In today’s digital landscape, online social networks (OSNs) facilitate rapid information dissemination. However, they also serve as conduits for misinformation, leading to severe real-world consequences such as public panic, social unrest, and the erosion of institutional trust. Existing rumor influence minimization strategies predominantly rely on static models or specific diffusion mechanisms, restricting their ability to dynamically adapt to the evolving nature of misinformation. To address this gap, this paper proposes a novel misinformation influence mitigation framework that integrates Graph Neural Networks (GNNs) with continual learning and employs a Node Blocking strategy as its intervention approach. The framework comprises three key components: (1) a Dataset Generator, (2) a GNN Model Trainer, and (3) an Influential Node Identifier. Given the scarcity of real-world data on misinformation propagation, the first component simulates misinformation diffusion processes within social networks, leveraging the Human Individual and Social Behavior (HISB) model as a case study. The second component employs GNNs to learn from these synthetic datasets and predict the most influential nodes susceptible to misinformation. Subsequently, these nodes are strategically targeted and blocked to minimize further misinformation spread. Finally, the continual learning mechanism ensures the model dynamically adapts to evolving network structures and propagation patterns. Beyond evaluating the Human Individual and Social Behavior (HISB) propagation model, we empirically demonstrate that our framework is propagation-model agnostic by reproducing the pipeline under Independent Cascade and Linear Threshold with consistent gains over baselines. Finally, we introduce a truth-aware intervention rule that gates and weights actions by an external veracity score at detection time, selecting most influential nodes. This addition ensures interventions are enacted only when content is likely false, aligning the method with responsible deployment. Experimental evaluations conducted on multiple benchmark datasets demonstrate the superiority of the proposed node blocking framework over state-of-the-art methods. Our results indicate a statistically significant reduction in misinformation spread, with non-parametric statistical tests yielding <span><math><mi>p</mi></math></span>-values below 0.001 (p<span><math><mo><</mo></math></span>0.001), confirming the robustness of our approach. This work presents a scalable and adaptable solution for misinformation containment, contributing to the development of more reliable and trustworthy online information ecosystems.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100340"},"PeriodicalIF":2.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1016/j.osnem.2025.100339
Eliseo Bao , Anxo Perez , David Otero , Javier Parapar
Many individuals with mental health problems turn to the internet and social media for information and support. The text generated on these platforms serves as a valuable resource for identifying mental health risks, driving interdisciplinary research to develop models for mental health analysis and prediction. In this paper, we model depression-related language using relevance-based statistical language models to create lexicons that characterize linguistic patterns associated with depression. We also propose a ranking method that leverages these lexicons to prioritize users exhibiting stronger signs of depressive language on social media. Our models integrate clinical markers from established depression questionnaires, particularly the Beck Depression Inventory-II (BDI-II), enhancing explainability, generalization, and performance. Experiments across multiple social media datasets show that incorporating clinical knowledge improves user ranking and generalizes effectively across platforms. Additionally, we refine existing depression lexicons by applying weights estimated from our models, achieving better performance in generating depression-related queries. A comparative analysis of our models highlights differences in language use between control users and those with depression, aligning with prior psycholinguistic findings. This work advances the understanding of depression-related language through statistical modeling, paving the way for scalable social media interventions to identify at-risk individuals.
{"title":"How does depression talk on social media? Modeling depression language with relevance-based statistical language models","authors":"Eliseo Bao , Anxo Perez , David Otero , Javier Parapar","doi":"10.1016/j.osnem.2025.100339","DOIUrl":"10.1016/j.osnem.2025.100339","url":null,"abstract":"<div><div>Many individuals with mental health problems turn to the internet and social media for information and support. The text generated on these platforms serves as a valuable resource for identifying mental health risks, driving interdisciplinary research to develop models for mental health analysis and prediction. In this paper, we model depression-related language using relevance-based statistical language models to create lexicons that characterize linguistic patterns associated with depression. We also propose a ranking method that leverages these lexicons to prioritize users exhibiting stronger signs of depressive language on social media. Our models integrate clinical markers from established depression questionnaires, particularly the Beck Depression Inventory-II (BDI-II), enhancing explainability, generalization, and performance. Experiments across multiple social media datasets show that incorporating clinical knowledge improves user ranking and generalizes effectively across platforms. Additionally, we refine existing depression lexicons by applying weights estimated from our models, achieving better performance in generating depression-related queries. A comparative analysis of our models highlights differences in language use between control users and those with depression, aligning with prior psycholinguistic findings. This work advances the understanding of depression-related language through statistical modeling, paving the way for scalable social media interventions to identify at-risk individuals.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100339"},"PeriodicalIF":2.9,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-14DOI: 10.1016/j.osnem.2025.100338
Mario Ezra Aragón , Adrián Pastor López-Monroy , Manuel Montes-y-Gómez , David E. Losada
With their growing popularity, social media platforms have become valuable tools for researchers and health professionals, offering new opportunities to identify linguistic patterns associated with mental health. In this study, we analyze depression-related symptoms using user-generated posts on social media and the Beck Depression Inventory (BDI). Using posts from individuals who have self-reported a depression diagnosis, we train and evaluate sentence classification models to assess their ability to detect BDI symptoms. Specifically, we conduct binary classification experiments to identify the presence of depression-related symptoms and additional tests to categorize sentences into specific BDI symptom types. We also perform a comprehensive symptom-level analysis to examine how depressive symptoms are expressed linguistically, linking social media data with a clinically validated framework. In addition, we analyze symptom distributions between users with and without depression and across platforms, providing insight into how symptoms manifest in diverse online contexts. Furthermore, we incorporate a data augmentation strategy that leverages Large Language Models to generate clinically grounded synthetic examples and evaluate their effectiveness against human-generated data. Our findings indicate that users with depression exhibit a significantly higher prevalence of certain BDI symptoms – particularly Suicidal Thoughts, Crying, Self-Dislike, and Changes in Sleeping Pattern – while control users predominantly express milder categories such as Sadness or Pessimism. Synthetic data improves the detection of underrepresented symptoms and enhances model robustness, although human-generated data better captures subtle linguistic nuances. Specialized models outperform general ones, but specific symptom categories remain challenging, underscoring the need for more interpretable and clinically grounded detection frameworks.
{"title":"Online expressions, offline struggles: Using social media to identify depression-related symptoms","authors":"Mario Ezra Aragón , Adrián Pastor López-Monroy , Manuel Montes-y-Gómez , David E. Losada","doi":"10.1016/j.osnem.2025.100338","DOIUrl":"10.1016/j.osnem.2025.100338","url":null,"abstract":"<div><div>With their growing popularity, social media platforms have become valuable tools for researchers and health professionals, offering new opportunities to identify linguistic patterns associated with mental health. In this study, we analyze depression-related symptoms using user-generated posts on social media and the Beck Depression Inventory (BDI). Using posts from individuals who have self-reported a depression diagnosis, we train and evaluate sentence classification models to assess their ability to detect BDI symptoms. Specifically, we conduct binary classification experiments to identify the presence of depression-related symptoms and additional tests to categorize sentences into specific BDI symptom types. We also perform a comprehensive symptom-level analysis to examine how depressive symptoms are expressed linguistically, linking social media data with a clinically validated framework. In addition, we analyze symptom distributions between users with and without depression and across platforms, providing insight into how symptoms manifest in diverse online contexts. Furthermore, we incorporate a data augmentation strategy that leverages Large Language Models to generate clinically grounded synthetic examples and evaluate their effectiveness against human-generated data. Our findings indicate that users with depression exhibit a significantly higher prevalence of certain BDI symptoms – particularly Suicidal Thoughts, Crying, Self-Dislike, and Changes in Sleeping Pattern – while control users predominantly express milder categories such as Sadness or Pessimism. Synthetic data improves the detection of underrepresented symptoms and enhances model robustness, although human-generated data better captures subtle linguistic nuances. Specialized models outperform general ones, but specific symptom categories remain challenging, underscoring the need for more interpretable and clinically grounded detection frameworks.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100338"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-11DOI: 10.1016/j.osnem.2025.100337
Terence Zhang , Aniket Mahanti , Ranesh Naha
The rise of centralised social networks has consolidated power among a few major technology companies, raising critical concerns about privacy, censorship, and transparency. In response, decentralised alternatives, including Web3 platforms like Decentralised Social (DeSo) and Fediverse platforms such as Mastodon, have gained increasing attention. While prior research has explored individual aspects of decentralised networks, comparisons between Fediverse and Web3 platforms remain limited, and the unique dynamics of Web3 networks like DeSo are not well understood. This study provides the first in-depth study of DeSo, characterising user behaviour, discourse, and economic activities, and compares these with Mastodon and memo.cash. We collected over 3.1M posts from 13K users on DeSo and Mastodon, along with 11M DeSo on-chain transactions via public APIs. Our analysis reveals that while DeSo and Mastodon share similarities in passive content engagement, they differ in their use of URLs, hashtags, and community focus. DeSo is primarily oriented around Decentralised Finance (DeFi) topics, whereas Mastodon hosts diverse discussions with an emphasis on news and politics. Despite DeSo’s decentralised social graph, its transaction graph remains centralised, underscoring the need for further decentralisation in Web3 platforms. Additionally, while wealth inequality exists on DeSo, low transaction fees promote user participation irrespective of financial status. These findings provide new insights into the evolving landscape of decentralised social networks and highlight critical areas for future research and platform development.
{"title":"Web3 vs Fediverse: A comparative analysis of DeSo and Mastodon as decentralised social media ecosystems","authors":"Terence Zhang , Aniket Mahanti , Ranesh Naha","doi":"10.1016/j.osnem.2025.100337","DOIUrl":"10.1016/j.osnem.2025.100337","url":null,"abstract":"<div><div>The rise of centralised social networks has consolidated power among a few major technology companies, raising critical concerns about privacy, censorship, and transparency. In response, decentralised alternatives, including Web3 platforms like Decentralised Social (DeSo) and Fediverse platforms such as Mastodon, have gained increasing attention. While prior research has explored individual aspects of decentralised networks, comparisons between Fediverse and Web3 platforms remain limited, and the unique dynamics of Web3 networks like DeSo are not well understood. This study provides the first in-depth study of DeSo, characterising user behaviour, discourse, and economic activities, and compares these with Mastodon and <span>memo.cash</span>. We collected over 3.1M posts from 13K users on DeSo and Mastodon, along with 11M DeSo on-chain transactions via public APIs. Our analysis reveals that while DeSo and Mastodon share similarities in passive content engagement, they differ in their use of URLs, hashtags, and community focus. DeSo is primarily oriented around Decentralised Finance (DeFi) topics, whereas Mastodon hosts diverse discussions with an emphasis on news and politics. Despite DeSo’s decentralised social graph, its transaction graph remains centralised, underscoring the need for further decentralisation in Web3 platforms. Additionally, while wealth inequality exists on DeSo, low transaction fees promote user participation irrespective of financial status. These findings provide new insights into the evolving landscape of decentralised social networks and highlight critical areas for future research and platform development.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100337"},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-03DOI: 10.1016/j.osnem.2025.100336
Enrico Verdolotti , Luca Luceri , Silvia Giordano
The spread of misinformation on social networks poses a significant challenge to online communities and society at large. Not all users contribute equally to this phenomenon: a small number of highly effective individuals can exert outsized influence, amplifying false narratives and contributing to significant societal harm. This paper seeks to mitigate the spread of misinformation by enabling proactive interventions, identifying and ranking users according to key behavioral indicators associated with harmful content dissemination. We examine three user archetypes — amplifiers, super-spreaders, and coordinated accounts — each characterized by distinct behavioral patterns in the dissemination of misinformation. These are not mutually exclusive, and individual users may exhibit characteristics of multiple archetypes. We develop and evaluate several user ranking models, each aligned with a specific archetype, and find that super-spreader traits consistently dominate the top ranks among the most influential misinformation spreaders. As we move down the ranking, however, the interplay of multiple archetypes becomes more prominent. Additionally, we demonstrate the critical role of temporal dynamics in predictive performance, and introduce methods that reduce data requirements by minimizing the observation window needed for accurate forecasting. Finally, we demonstrate the utility and benefits of explainable AI (XAI) techniques, integrating multiple archetypal traits into a unified model to enhance interpretability and offer deeper insight into the key factors driving misinformation propagation. Our findings provide actionable tools for identifying potentially harmful users and guiding content moderation strategies, enabling platforms to monitor accounts of concern more effectively.
{"title":"Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior","authors":"Enrico Verdolotti , Luca Luceri , Silvia Giordano","doi":"10.1016/j.osnem.2025.100336","DOIUrl":"10.1016/j.osnem.2025.100336","url":null,"abstract":"<div><div>The spread of misinformation on social networks poses a significant challenge to online communities and society at large. Not all users contribute equally to this phenomenon: a small number of highly effective individuals can exert outsized influence, amplifying false narratives and contributing to significant societal harm. This paper seeks to mitigate the spread of misinformation by enabling proactive interventions, identifying and ranking users according to key behavioral indicators associated with harmful content dissemination. We examine three user archetypes — <em>amplifiers</em>, <em>super-spreaders</em>, and <em>coordinated accounts</em> — each characterized by distinct behavioral patterns in the dissemination of misinformation. These are not mutually exclusive, and individual users may exhibit characteristics of multiple archetypes. We develop and evaluate several user ranking models, each aligned with a specific archetype, and find that <em>super-spreader</em> traits consistently dominate the top ranks among the most influential misinformation spreaders. As we move down the ranking, however, the interplay of multiple archetypes becomes more prominent. Additionally, we demonstrate the critical role of temporal dynamics in predictive performance, and introduce methods that reduce data requirements by minimizing the observation window needed for accurate forecasting. Finally, we demonstrate the utility and benefits of explainable AI (XAI) techniques, integrating multiple archetypal traits into a unified model to enhance interpretability and offer deeper insight into the key factors driving misinformation propagation. Our findings provide actionable tools for identifying potentially harmful users and guiding content moderation strategies, enabling platforms to monitor accounts of concern more effectively.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100336"},"PeriodicalIF":2.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moderating harmful content, such as misogynistic language, is essential to ensure safety and well-being in online spaces. To this end, text classification models have been used to detect toxic content, especially in communities that are known to promote violence and radicalization in the real world, such as the incel movement. However, these models remain vulnerable to targeted data poisoning attacks. In this work, we present a realistic targeted poisoning strategy in which an adversary aims at misclassifying specific misogynistic comments in order to evade detection. While prior approaches craft poisoned samples with explicit trigger phrases, our method relies exclusively on existing training data. In particular, we repurpose the concept of opponents, training points that negatively influence the prediction of a target test point, to identify poisoned points to be added to the training set, either in their original form or in a paraphrased variant. The effectiveness of the attack is then measured on several aspects: success rate, number of poisoned samples required, and preservation of the overall model performance. Our results on two different datasets show that only a small fraction of malicious inputs are possibly sufficient to undermine classification of a target sample, while leaving the model performance on non-target points virtually unaffected, revealing the stealthy nature of the attack. Finally, we show that the attack can be transferred across different models, thus highlighting its practical relevance in real-world scenarios. Overall, our work raises awareness on the vulnerability of powerful machine learning models to data poisoning attacks, and will possibly encourage the development of efficient defense and mitigation techniques to strengthen the security of automated moderation systems.
{"title":"The perils of stealthy data poisoning attacks in misogynistic content moderation","authors":"Syrine Enneifer, Federica Baccini, Federico Siciliano, Irene Amerini, Fabrizio Silvestri","doi":"10.1016/j.osnem.2025.100334","DOIUrl":"10.1016/j.osnem.2025.100334","url":null,"abstract":"<div><div>Moderating harmful content, such as misogynistic language, is essential to ensure safety and well-being in online spaces. To this end, text classification models have been used to detect toxic content, especially in communities that are known to promote violence and radicalization in the real world, such as the <em>incel</em> movement. However, these models remain vulnerable to targeted data poisoning attacks. In this work, we present a realistic targeted poisoning strategy in which an adversary aims at misclassifying specific misogynistic comments in order to evade detection. While prior approaches craft poisoned samples with explicit trigger phrases, our method relies exclusively on existing training data. In particular, we repurpose the concept of <em>opponents</em>, training points that negatively influence the prediction of a target test point, to identify poisoned points to be added to the training set, either in their original form or in a paraphrased variant. The effectiveness of the attack is then measured on several aspects: success rate, number of poisoned samples required, and preservation of the overall model performance. Our results on two different datasets show that only a small fraction of malicious inputs are possibly sufficient to undermine classification of a target sample, while leaving the model performance on non-target points virtually unaffected, revealing the stealthy nature of the attack. Finally, we show that the attack can be transferred across different models, thus highlighting its practical relevance in real-world scenarios. Overall, our work raises awareness on the vulnerability of powerful machine learning models to data poisoning attacks, and will possibly encourage the development of efficient defense and mitigation techniques to strengthen the security of automated moderation systems.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100334"},"PeriodicalIF":2.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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