Online Social Networks and Media最新文献

{"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":"2025-07-01","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":"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":"2025-06-24","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":"BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning","authors":"Mohammad Majid Akhtar ,&nbsp;Navid Shadman Bhuiyan ,&nbsp;Rahat Masood ,&nbsp;Muhammad Ikram ,&nbsp;Salil S. Kanhere","doi":"10.1016/j.osnem.2025.100318","DOIUrl":"10.1016/j.osnem.2025.100318","url":null,"abstract":"<div><div>The detection of automated accounts, also known as “social bots”, has been an important concern for online social networks (OSNs). While several methods have been proposed for detecting social bots, significant research gaps remain. First, current models exhibit limitations in detecting sophisticated bots that aim to mimic genuine OSN users. Second, these methods often rely on simplistic profile features, which are susceptible to adversarial manipulation. In addition, these models lack generalizability, resulting in subpar performance when trained on one dataset and tested on another.</div><div>To address these challenges, we propose a framework for social <strong>Bot</strong> detection with <strong>S</strong>elf-<strong>S</strong>upervised <strong>C</strong>ontrastive <strong>L</strong>earning (BotSSCL). Our framework leverages contrastive learning to distinguish between social bots and humans in the embedding space to improve linear separability. The high-level representations derived by BotSSCL enhance its resilience to variations in data distribution and ensure generalizability. We evaluate BotSSCL’s robustness against adversarial attempts to manipulate bot accounts to evade detection. Experiments on two datasets featuring sophisticated bots demonstrate that BotSSCL outperforms other supervised, unsupervised, and self-supervised baseline methods. We achieve <span><math><mrow><mo>≈</mo><mn>6</mn><mtext>%</mtext></mrow></math></span> and <span><math><mrow><mo>≈</mo><mn>8</mn><mtext>%</mtext></mrow></math></span> higher (F1) performance than SOTA on both datasets. In addition, BotSSCL also achieves 67% F1 when trained on one dataset and tested with another, demonstrating its generalizability under cross-botnet evaluation. Lastly, under adversarial evasion attack, BotSSCL shows increased complexity for the adversary and only allows 4% success to the adversary in evading detection. The code is available at <span><span>https://github.com/code4thispaper/BotSSCL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"48 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203492","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":"‘Toxic’ memes: A survey of computational perspectives on the detection and explanation of meme toxicities","authors":"Delfina S. Martinez Pandiani ,&nbsp;Erik Tjong Kim Sang ,&nbsp;Davide Ceolin","doi":"10.1016/j.osnem.2025.100317","DOIUrl":"10.1016/j.osnem.2025.100317","url":null,"abstract":"<div><div>Internet memes are multimodal, highly shareable cultural units that condense complex messages into compact forms of communication, making them a powerful vehicle for information spread. Increasingly, they are used to propagate hateful, extremist, or otherwise ‘toxic’ narratives, symbols, and messages. Research on computational methods for meme toxicity analysis has expanded significantly over the past five years. However, existing surveys cover only studies published until 2022, resulting in inconsistent terminology and overlooked trends. This survey bridges that gap by systematically reviewing content-based computational approaches to toxic meme analysis, incorporating key developments up to early 2024. Using the PRISMA methodology, we extend the scope of prior analyses, resulting in a threefold increase in the number of reviewed works. This study makes four key contributions. First, we expand the coverage of computational research on toxic memes, reviewing 158 content-based studies, including 119 newly analyzed papers, and identifying over 30 datasets while examining their labeling methodologies. Second, we address the lack of clear definitions of meme toxicity in computational research by introducing a new taxonomy that categorizes different toxicity types, providing a more structured foundation for future studies. Third, we observe that existing content-based studies implicitly focus on three key dimensions of meme toxicity—target, intent, and conveyance tactics. We formalize this perspective by introducing a structured framework that models how these dimensions are computationally analyzed across studies. Finally, we examine emerging trends and challenges, including advancements in cross-modal reasoning, the integration of expert and cultural knowledge, the increasing demand for automatic toxicity explanations, the challenges of handling meme toxicity in low-resource languages, and the rising role of generative AI in both analyzing and generating ‘toxic’ memes.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"47 ","pages":"Article 100317"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167494","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":"Message order matters: A robust author profiling approach for social media platforms","authors":"Mehmet Deniz Türkmen ,&nbsp;Mucahid Kutlu","doi":"10.1016/j.osnem.2025.100316","DOIUrl":"10.1016/j.osnem.2025.100316","url":null,"abstract":"<div><div>As the order of sentences can impact the meaning of texts, transformer models and recurrent neural networks (RNN) also consider the order of the tokens. However, this feature can negatively affect the classification of social media accounts, as users might share messages on entirely different topics in consecutive order. In this study, we explore how to enhance the performance of models that take into account word order for various author profiling tasks on social media. We first draw attention to the transformer models’ input limit and propose a message selection method that also reduces noise caused by irrelevant messages. In addition, we show that arbitrarily concatenating messages can be problematic. Therefore, we propose creating multiple variants of data by shuffling messages, classifying each variant separately, and then aggregating the predictions. In our comprehensive experiments, we focus on age, gender, occupation, and bot detection tasks. We show that the proposed content selection and shuffling-based methods lead to slight improvements in the transformer model’s performance for age and gender detection tasks. However, our approach yields noticeable performance increases for BiLSTM model. Additionally, we observe that the shuffling method serves as an effective means to augment training data, further enhancing models’ performance. Moreover, our shuffling-based approach enhances the models’ resistance to adversarial attacks in gender and occupation detection tasks without compromising their performance in age detection.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"47 ","pages":"Article 100316"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144114716","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":"A first principles approach to trust-based recommendation systems in social networks","authors":"Paras Stefanopoulos ,&nbsp;Sourin Chatterjee ,&nbsp;Ahad N. Zehmakan","doi":"10.1016/j.osnem.2025.100315","DOIUrl":"10.1016/j.osnem.2025.100315","url":null,"abstract":"<div><div>This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information types in a collaborative filtering approach. The trust graph-based approaches were found to be more robust to network adversarial attacks due to hard-to-manipulate trust structures. Intra-item information, although sub-optimal in isolation, enhances the consistency of predictions and lower-end performance when fused with other information forms. Additionally, the Weighted Average framework is introduced, enabling the construction of recommendation systems around any user-to-user similarity metric.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"47 ","pages":"Article 100315"},"PeriodicalIF":0.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070285","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":"Is it fake or not? A comprehensive approach for multimodal fake news detection","authors":"Davide Antonio Mura ,&nbsp;Marco Usai ,&nbsp;Andrea Loddo,&nbsp;Manuela Sanguinetti,&nbsp;Luca Zedda,&nbsp;Cecilia Di Ruberto,&nbsp;Maurizio Atzori","doi":"10.1016/j.osnem.2025.100314","DOIUrl":"10.1016/j.osnem.2025.100314","url":null,"abstract":"<div><div>In recent years, the proliferation of fake news has posed significant challenges to information integrity and public trust, paving the way for the development of artificial intelligence-based models that can analyze information and determine its veracity. This study comprehensively evaluates the Themis architecture in the context of fake news detection on two distinct public datasets: Fakeddit and ReCoVery. To enhance model performance, we systematically investigate various customizations of Themis, including the integration of Low-Rank Adaptation, diverse data augmentation techniques, and multiple configurations, employing the TinyLlama Large Language Model and CLIP ViT image encoders while tuning key parameters to optimize results. Our findings reveal that while the standard Themis model performed adequately, significant improvements were observed by incorporating LoRA and specific data augmentation strategies, particularly in the ReCoVery dataset. Comparisons with existing literature indicate that Themis achieves competitive performance, especially in the ReCoVery dataset, where it outperforms existing solutions.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"47 ","pages":"Article 100314"},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070284","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":"Political communication and conspiracy theory sharing on twitter","authors":"Imane Khaouja ,&nbsp;Daniel Toribio-Flórez ,&nbsp;Ricky Green ,&nbsp;Cassidy Rowden ,&nbsp;Chee Siang Ang ,&nbsp;Karen M. Douglas","doi":"10.1016/j.osnem.2025.100313","DOIUrl":"10.1016/j.osnem.2025.100313","url":null,"abstract":"<div><div>Social media has become an influential channel for political communication, offering broad reach while enabling the proliferation of misinformation and conspiracy theories. These unchecked conspiracy narratives may allow manipulation by malign actors, posing dangers to democratic processes. Despite their intuitive appeal, little research has examined the strategic usage and timing of conspiracy theories in politicians’ social media communication compared to the spread of misinformation and fake news.</div><div>This study provides an empirical analysis of how members of the U.S. Congress spread conspiracy theories on Twitter. Leveraging the Twitter Historical API, we collected a corpus of tweets from members of the US Congress between January 2012 and December 2022. We developed a classifier to identify conspiracy theory content within this political discourse. We also analyzed the linguistic characteristics, topics and distribution of conspiracy tweets. To assess classifier performance, we created ground truth data through human annotation in which experts labeled a sample of 2500 politicians’ tweets.</div><div>Our findings shed light on several aspects, including the influence of prevailing political power dynamics on the propagation of conspiracy theories and higher user engagement. Moreover, we identified specific psycho-linguistic attributes within the tweets, characterized by the use of words related to power and causation, and outgroup language. Our results provide valuable insights into the motivations compelling influential figures to engage in the dissemination of conspiracy narratives in political discourse.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"47 ","pages":"Article 100313"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928802","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":"Advancing depression detection on social media platforms through fine-tuned large language models","authors":"Shahid Munir Shah ,&nbsp;Syeda Anshrah Gillani ,&nbsp;Mirza Samad Ahmed Baig ,&nbsp;Muhammad Aamer Saleem ,&nbsp;Muhammad Hamzah Siddiqui","doi":"10.1016/j.osnem.2025.100311","DOIUrl":"10.1016/j.osnem.2025.100311","url":null,"abstract":"<div><div>This study investigates the use of Large Language Models (LLMs) for improved depression detection from users’ social media data. Through the use of fine-tuned GPT-3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of 96.4%. The comparative analysis of the obtained results with the relevant studies in the literature and the base models shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state-of-the-art systems and the base models. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, along with the dataset description and comparative summary of the results, indicating the important implications of the obtained results for the early diagnosis of depression on various social media platforms.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683376","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":"Community detection in Multimedia Social Networks using an attributed graph model","authors":"Giancarlo Sperlì","doi":"10.1016/j.osnem.2025.100312","DOIUrl":"10.1016/j.osnem.2025.100312","url":null,"abstract":"<div><div>In this paper, we design a novel data model for a Multimedia Social Network, that has been modeled as an attribute graph for integrating semantic analysis of multimedia content published by users. It combines features inferred from object detection, image classification, and hashtag analysis in a unified model to characterize a user from different points of view. On top of this model, community detection algorithms have been applied to unveil users’ communities. Hence, we design a framework integrating multimedia features with different community detection approaches (topological, deep learning, representation learning, and game theory-based) to improve detection effectiveness. The proposed framework has been evaluated on a real-world dataset, composed of 4.5 million profiles publishing more than 42 million posts and 1.2 million images, to investigate the impact of different features on both graph-building and community detection tasks. The main findings of the proposed analysis show how combining different sets of features inferred from multimedia content allows to achieve the highest modularity score w.r.t. other configurations although it requires a higher running time for building the underlined network. Specifically, representation and game theory-based algorithms achieve the highest results in terms of Modularity measure by exploiting the semantic and contextual information integrated into the proposed model.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100312"},"PeriodicalIF":0.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}