Online Social Networks and Media最新文献

{"title":"Online expressions, offline struggles: Using social media to identify depression-related symptoms","authors":"Mario Ezra Aragón ,&nbsp;Adrián Pastor López-Monroy ,&nbsp;Manuel Montes-y-Gómez ,&nbsp;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":"2025-12-01","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}

{"title":"Misinformation mitigation in online social networks using continual learning with graph neural networks","authors":"Hichem Merini ,&nbsp;Adil Imad Eddine Hosni ,&nbsp;Kadda Beghdad Bey ,&nbsp;Vincenzo Lomonaco ,&nbsp;Marco Podda ,&nbsp;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>&lt;</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":"2025-12-01","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}

{"title":"Intelligent nudging for truth: Mitigating rumor and misinformation in social networks with behavioral strategies","authors":"Indu V. ,&nbsp;Sabu M. Thampi","doi":"10.1016/j.osnem.2025.100333","DOIUrl":"10.1016/j.osnem.2025.100333","url":null,"abstract":"<div><div>Social networks play a crucial role in disseminating information during emergencies and natural disasters, but they also facilitate the spread of rumors and misinformation, which can have adverse effects on society. Numerous false messages related to the COVID-19 pandemic circulated on social networks, causing unnecessary fear and anxiety, and leading to various mental health issues. Despite strict measures by social network providers and government authorities to curb fake news, many users continue to fall victim to misinformation. This highlights the need for novel approaches that incorporate user participation in mitigating rumors on social networks. Since users are the primary consumers and spreaders of information, their involvement is essential in maintaining information hygiene. We propose a novel approach based on nudging theory to motivate users to post or share only verified information on their social network profiles, thereby positively influencing their information-sharing behavior. Our approach utilizes three nudging strategies: Confront nudge, Reinforcement nudge, and Social Influence nudge. We have developed a Chrome browser plug-in for Twitter that prompts users to verify the authenticity of tweets and rate them before sharing. Additionally, user profiles receive a rating based on the average ratings of their posted tweets. The effectiveness of this mechanism was tested in a field study involving 125 Twitter users over one month. The results suggest that the proposed approach is a promising solution for limiting the propagation of rumors on social networks.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"49 ","pages":"Article 100333"},"PeriodicalIF":2.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889996","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}

{"title":"WhatsApp tiplines and multilingual claims in the 2021 Indian assembly elections","authors":"Gautam Kishore Shahi ,&nbsp;Scott A. Hale","doi":"10.1016/j.osnem.2025.100323","DOIUrl":"10.1016/j.osnem.2025.100323","url":null,"abstract":"<div><div>WhatsApp tiplines, first launched in 2019 to combat misinformation, enable users to interact with fact-checkers to verify misleading content. This study analyzes 580 unique claims (tips) from 451 users, covering both high-resource languages (English, Hindi) and a low-resource language (Telugu) during the 2021 Indian assembly elections using a mixed-method approach. We categorize the claims into three categories, election, COVID-19, and others, and observe variations across languages. We compare content similarity through frequent word analysis and clustering of neural sentence embeddings. We also investigate user overlap across languages and fact-checking organizations. We measure the average time required to debunk claims and inform tipline users. Results reveal similarities in claims across languages, with some users submitting tips in multiple languages to the same fact-checkers. Fact-checkers generally require a couple of days to debunk a new claim and share the results with users. Notably, no user submits claims to multiple fact-checking organizations, indicating that each organization maintains a unique audience. We provide practical recommendations for using tiplines during elections with ethical consideration of user information.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"49 ","pages":"Article 100323"},"PeriodicalIF":2.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852544","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}

{"title":"SPREADSHOT: Analysis of fake news spreading through topic modeling and bipartite weighted graphs","authors":"Carmela Bernardo,&nbsp;Marta Catillo,&nbsp;Antonio Pecchia,&nbsp;Francesco Vasca,&nbsp;Umberto Villano","doi":"10.1016/j.osnem.2025.100324","DOIUrl":"10.1016/j.osnem.2025.100324","url":null,"abstract":"<div><div>Spreading of fake news is one of the primary drivers of misinformation in social networks. Graph-based approaches that analyze fake news dissemination are mostly dedicated to fake news detection and consider homogeneous tree-based networks obtained by following the diffusion of single messages through users, thus lacking the ability to identify implicit patterns among spreaders and topics. Alternatively, heterogeneous graphs have been proposed, although the detection remains their main goal and the use of graph centralities is rather limited. In this paper, bipartite weighted graphs are used to analyze fake news and spreaders by utilizing topic modeling and a combination of network centrality measures. The proposed architecture, called SPREADSHOT, leverages a topic modeling technique to identify key topics or subjects within a collection of fake news articles published by spreaders, thus generating a bipartite weighted graph. By projecting the graph model to the space of spreaders, one can identify the strengths of links between them in terms of fakeness correlation on common topics. Moreover, the closeness and betweennes centralities highlight spreaders who represent key enablers in the dissemination of fakeness on different topics. The projection of the bipartite graph to the space of topics allows one to identify topics which are more prone to misinformation. By collecting specific network measures, a synthetic fakeness networking index is defined which characterizes the behaviors and roles of spreaders and topics in the fakeness dissemination. The effectiveness of the proposed technique is demonstrated through tests on the LIAR dataset.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"49 ","pages":"Article 100324"},"PeriodicalIF":2.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758057","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}

{"title":"Detecting mental disorder on social media: A ChatGPT-augmented explainable approach","authors":"Loris Belcastro,&nbsp;Riccardo Cantini,&nbsp;Fabrizio Marozzo,&nbsp;Domenico Talia,&nbsp;Paolo Trunfio","doi":"10.1016/j.osnem.2025.100321","DOIUrl":"10.1016/j.osnem.2025.100321","url":null,"abstract":"<div><div>In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572466","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}

{"title":"Assessing the potential of generative agents in crowdsourced fact-checking","authors":"Luigia Costabile ,&nbsp;Gian Marco Orlando ,&nbsp;Valerio La Gatta ,&nbsp;Vincenzo Moscato","doi":"10.1016/j.osnem.2025.100326","DOIUrl":"10.1016/j.osnem.2025.100326","url":null,"abstract":"<div><div>The growing spread of online misinformation has created an urgent need for scalable, reliable fact-checking solutions. Crowdsourced fact-checking—where non-experts evaluate claim veracity—offers a cost-effective alternative to expert verification, despite concerns about variability in quality and bias. Encouraged by promising results in certain contexts, major platforms such as X (formerly Twitter), Facebook, and Instagram have begun shifting from centralized moderation to decentralized, crowd-based approaches.</div><div>In parallel, advances in Large Language Models (LLMs) have shown strong performance across core fact-checking tasks, including claim detection and evidence evaluation. However, their potential role in crowdsourced workflows remains unexplored. This paper investigates whether LLM-powered generative agents—autonomous entities that emulate human behavior and decision-making—can meaningfully contribute to fact-checking tasks traditionally reserved for human crowds.</div><div>Using the protocol of La Barbera et al. (2024), we simulate crowds of generative agents with diverse demographic and ideological profiles. Agents retrieve evidence, assess claims along multiple quality dimensions, and issue final veracity judgments. Our results show that agent crowds outperform human crowds in truthfulness classification, exhibit higher internal consistency, and show reduced susceptibility to social and cognitive biases. Compared to humans, agents rely more systematically on informative criteria such as <em>Accuracy</em>, <em>Precision</em>, and <em>Informativeness</em>, suggesting a more structured decision-making process. Overall, our findings highlight the potential of generative agents as scalable, consistent, and less biased contributors to crowd-based fact-checking systems.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100326"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713785","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}

{"title":"Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance","authors":"Lucio La Cava,&nbsp;Andrea Tagarelli","doi":"10.1016/j.osnem.2025.100319","DOIUrl":"10.1016/j.osnem.2025.100319","url":null,"abstract":"<div><div>Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content and a limited number of moderators. Recent advancements in Natural Language Understanding demonstrated by Large Language Models unlock new opportunities for automated content compliance verification. This work evaluates six AI-agents built on Open-LLMs for automated rule compliance checking in Decentralized Social Networks, a challenging environment due to heterogeneous community scopes and rules. By analyzing over 50,000 posts from hundreds of Mastodon servers, we find that AI-agents effectively detect non-compliant content, grasp linguistic subtleties, and adapt to diverse community contexts. Most agents also show high inter-rater reliability and consistency in score justification and suggestions for compliance. Human-based evaluation with domain experts confirmed the agents’ reliability and usefulness, rendering them promising tools for semi-automated or human-in-the-loop content moderation systems.</div><div><em>Warning: This manuscript may contain sensitive content as it quotes harmful/hateful social media posts.</em></div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472285","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}

{"title":"Investigating the heterogeneous effects of a massive content moderation intervention via Difference-in-Differences","authors":"Lorenzo Cima ,&nbsp;Benedetta Tessa ,&nbsp;Amaury Trujillo ,&nbsp;Stefano Cresci ,&nbsp;Marco Avvenuti","doi":"10.1016/j.osnem.2025.100320","DOIUrl":"10.1016/j.osnem.2025.100320","url":null,"abstract":"<div><div>In today’s online environments, users encounter harm and abuse on a daily basis. Therefore, content moderation is crucial to ensure their safety and well-being. However, the effectiveness of many moderation interventions is still uncertain. Here, we apply a causal inference approach to shed light on the effectiveness of The Great Ban, a massive social media deplatforming intervention on Reddit. We analyze 53M comments shared by nearly 34K users, providing in-depth results on both the intended and unintended consequences of the ban. Our causal analyses reveal that 15.6% of the moderated users abandoned the platform while the remaining ones decreased their overall toxicity by 4.1%. Nonetheless, a small subset of users exhibited marked increases in both the intensity and volume of toxic behavior, particularly among those whose activity levels changed after the intervention. However, these reactions were not accompanied by greater activity or engagement, suggesting that even the most toxic users maintained a limited overall impact. Our findings bring to light new insights on the effectiveness of deplatforming moderation interventions. Furthermore, they also contribute to informing future content moderation strategies and regulations.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517875","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}

{"title":"Computational analysis of Information Disorder in Cognitive Warfare","authors":"Angelo Gaeta ,&nbsp;Vincenzo Loia ,&nbsp;Angelo Lorusso ,&nbsp;Francesco Orciuoli ,&nbsp;Antonella Pascuzzo","doi":"10.1016/j.osnem.2025.100322","DOIUrl":"10.1016/j.osnem.2025.100322","url":null,"abstract":"<div><div>Cognitive Warfare represents the modern evolution of traditional conflict, where the human mind emerges as the primary battleground, and information serves as a weapon to influence people’s thoughts, perceptions, and behaviors. Adopting the Information Disorder perspective, this work meticulously explores the phenomena associated with Cognitive Warfare, particularly as they spread across online social networks and media, to better understand their textual nature. In particular, the work focuses on specific cognitive weapons predominantly used by malicious actors in this context, such as the dissemination of misleading political news, junk science, and conspiracy theories. Therefore, the paper proposes an approach to identify, extract, and assess text-based features able to characterize the forms of Information Disorder involved in Cognitive Warfare. The proposed approach starts with a literature review and ends by assessing the identified and selected features through comprehensive experimentation based on a well-known dataset and conducted through the application of machine learning methods. In particular, by applying the Rough Set Theory and explainable AI it is found that features belonging to readability, psychological, and linguistic categories demonstrate a significant contribution in classifying the aforementioned forms of disorder. The obtained results are highly valuable as they can be leveraged to analyze critical aspects of Information Disorder, such as identifying the intent behind manipulated content and its targeted audience.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679439","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}