Online Social Networks and Media最新文献

DisT5: A Text-to-Text transformer model for disaster events DisT5：灾难事件的文本到文本转换器模型

IF 2.9

Online Social Networks and Media Pub Date : 2026-03-01 Epub Date: 2026-02-22 DOI: 10.1016/j.osnem.2026.100347

Piyush Kumar Garg , Srishti Gupta , Syed Ali Abbas , Roshni Chakraborty , Sourav Kumar Dandapat

{"title":"DisT5: A Text-to-Text transformer model for disaster events","authors":"Piyush Kumar Garg , Srishti Gupta , Syed Ali Abbas , Roshni Chakraborty , Sourav Kumar Dandapat","doi":"10.1016/j.osnem.2026.100347","DOIUrl":"10.1016/j.osnem.2026.100347","url":null,"abstract":"<div><div>Transformer-based language models have become essential components in constructing pipelines for various NLP tasks. Although various pre-trained transformer models have been developed for different tasks, they still fail to achieve the desired performance in the disaster domain. Consequently, a lack of pre-trained models specifically designed for this domain. Therefore, in this paper, we present DisT5, a pre-trained transformer model specific to the disaster domain. We have adopted a T5-style self-supervised pre-training approach. We further pre-trained the T5-model on a large collection of textual data covering diverse disaster events, both natural and man-made. We benchmark DisT5 on three downstream tasks: (1) Tweet category classification, (2) Key-phrase identification, and (3) Abstractive summarization. We validate the performance of DisT5 for the aforementioned tasks through comprehensive experiments. Our experimental results demonstrate that additional pre-training improves the performance of the DisT5 model in all the aforementioned tasks. We achieved up to 70.35% accuracy improvement in tweet classification, 87.65% IOU (F1-score) improvement in key-phrase identification, and up to 76.47% and 72.72% ROUGE-N F1-score improvement in summarization without and with fine-tuning, respectively. The pre-trained model will be made available at <span><span>https://huggingface.co/Piyyussh/DisT5</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"52 ","pages":"Article 100347"},"PeriodicalIF":2.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147411321","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}

引用次数: 0

Dynamic user trust assessment in online social networks using liquid neural networks 基于液体神经网络的在线社交网络动态用户信任评估

IF 2.9

Online Social Networks and Media Pub Date : 2026-01-01 Epub Date: 2025-11-21 DOI: 10.1016/j.osnem.2025.100343

Youssef Gamha, Lotfi Ben Romdhane

引用次数: 0

Generative exaggeration in LLM social agents: Consistency, bias, and toxicity 法学硕士社会代理人的生成夸张：一致性、偏见和毒性

IF 2.9

Online Social Networks and Media Pub Date : 2026-01-01 Epub Date: 2025-12-15 DOI: 10.1016/j.osnem.2025.100344

Jacopo Nudo , Mario Edoardo Pandolfo , Edoardo Loru , Mattia Samory , Matteo Cinelli , Walter Quattrociocchi

{"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":"2026-01-01","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}

引用次数: 0

A longitudinal analysis of misinformation, polarization and toxicity on Bluesky after its public launch 蓝天上市后的误传、极化和毒性纵向分析

IF 2.9

Online Social Networks and Media Pub Date : 2026-01-01 Epub Date: 2025-11-27 DOI: 10.1016/j.osnem.2025.100342

Gianluca Nogara , Erfan Samieyan Sahneh , Matthew R. DeVerna , Nick Liu , Luca Luceri , Filippo Menczer , Francesco Pierri , Silvia Giordano

{"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":"2026-01-01","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}

引用次数: 0

Predicting, evaluating, and explaining top misinformation spreaders via archetypal user behavior 通过原型用户行为预测、评估和解释最主要的错误信息传播者

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-10-03 DOI: 10.1016/j.osnem.2025.100336

Enrico Verdolotti , Luca Luceri , Silvia Giordano

{"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":"2025-12-01","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}

引用次数: 0

The perils of stealthy data poisoning attacks in misogynistic content moderation 在歧视女性的内容审核中，秘密数据中毒攻击的危险

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-09-23 DOI: 10.1016/j.osnem.2025.100334

Syrine Enneifer, Federica Baccini, Federico Siciliano, Irene Amerini, Fabrizio Silvestri

{"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":"2025-12-01","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}

引用次数: 0

How does depression talk on social media? Modeling depression language with relevance-based statistical language models 抑郁症是如何在社交媒体上传播的？用基于相关性的统计语言模型建模抑郁语言

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-10-22 DOI: 10.1016/j.osnem.2025.100339

Eliseo Bao , Anxo Perez , David Otero , Javier Parapar

{"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":"2025-12-01","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}

引用次数: 0

IMMENSE: Inductive Multi-perspective User Classification in Social Networks 社交网络中的归纳多视角用户分类

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-09-12 DOI: 10.1016/j.osnem.2025.100335

Francesco Benedetti , Antonio Pellicani , Gianvito Pio , Michelangelo Ceci

{"title":"IMMENSE: Inductive Multi-perspective User Classification in Social Networks","authors":"Francesco Benedetti , Antonio Pellicani , Gianvito Pio , Michelangelo Ceci","doi":"10.1016/j.osnem.2025.100335","DOIUrl":"10.1016/j.osnem.2025.100335","url":null,"abstract":"<div><div>Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content. Young users, in particular, are vulnerable to exposure to such content, which can have harmful psychological and social repercussions. Given the massive scale of today’s social networks, in terms of both published content and number of users, there is an urgent need for effective systems to aid Law Enforcement Agencies (LEAs) in identifying and addressing users that disseminate malicious content. In this work we introduce IMMENSE, a machine learning-based method for detecting malicious social network users. Our approach adopts a hybrid classification strategy that integrates three perspectives: the semantics of the users’ published content, their social relationships and their spatial information. Such contextual perspectives potentially enhance classification performance beyond text-only analysis. Importantly, IMMENSE employs an inductive learning approach, enabling it to classify previously unseen users or entire new networks without the need for costly and time-consuming model retraining procedures. Experiments carried out on a real-world Twitter/X dataset showed the superiority of IMMENSE against five state of the art competitors, confirming the benefits of its hybrid approach for effective deployment in social network monitoring systems.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"50 ","pages":"Article 100335"},"PeriodicalIF":2.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050487","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}

引用次数: 0

Web3 vs Fediverse: A comparative analysis of DeSo and Mastodon as decentralised social media ecosystems Web3 vs Fediverse: DeSo和乳齿象作为分散的社会媒体生态系统的比较分析

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-10-11 DOI: 10.1016/j.osnem.2025.100337

Terence Zhang , Aniket Mahanti , Ranesh Naha

{"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":"2025-12-01","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}

引用次数: 0

Identifying coordination in online social networks through anomalous sharing behaviour 通过异常分享行为识别在线社交网络中的协调

IF 2.9

Online Social Networks and Media Pub Date : 2025-12-01 Epub Date: 2025-11-14 DOI: 10.1016/j.osnem.2025.100341

Ahmad Zareie, Mehmet E. Bakir, Mark A. Greenwood, Kalina Bontcheva, Carolina Scarton

{"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":"2025-12-01","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}

引用次数: 0