Online Social Networks and Media最新文献

‘Toxic’ memes: A survey of computational perspectives on the detection and explanation of meme toxicities “有毒”模因：对模因毒性检测和解释的计算视角的调查

Online Social Networks and Media Pub Date : 2025-05-30 DOI: 10.1016/j.osnem.2025.100317

Delfina S. Martinez Pandiani , Erik Tjong Kim Sang , Davide Ceolin

{"title":"‘Toxic’ memes: A survey of computational perspectives on the detection and explanation of meme toxicities","authors":"Delfina S. Martinez Pandiani , Erik Tjong Kim Sang , 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}

引用次数: 0

Message order matters: A robust author profiling approach for social media platforms 消息顺序很重要：针对社交媒体平台的健壮的作者分析方法

Online Social Networks and Media Pub Date : 2025-05-22 DOI: 10.1016/j.osnem.2025.100316

Mehmet Deniz Türkmen , Mucahid Kutlu

{"title":"Message order matters: A robust author profiling approach for social media platforms","authors":"Mehmet Deniz Türkmen , 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}

引用次数: 0

A first principles approach to trust-based recommendation systems in social networks 社交网络中基于信任的推荐系统的第一原则方法

Online Social Networks and Media Pub Date : 2025-05-17 DOI: 10.1016/j.osnem.2025.100315

Paras Stefanopoulos , Sourin Chatterjee , Ahad N. Zehmakan

引用次数: 0

Is it fake or not? A comprehensive approach for multimodal fake news detection 它是假的还是假的？一种多模式假新闻检测的综合方法

Online Social Networks and Media Pub Date : 2025-05-16 DOI: 10.1016/j.osnem.2025.100314

Davide Antonio Mura , Marco Usai , Andrea Loddo, Manuela Sanguinetti, Luca Zedda, Cecilia Di Ruberto, Maurizio Atzori

{"title":"Is it fake or not? A comprehensive approach for multimodal fake news detection","authors":"Davide Antonio Mura , Marco Usai , Andrea Loddo, Manuela Sanguinetti, Luca Zedda, Cecilia Di Ruberto, 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}

引用次数: 0

Political communication and conspiracy theory sharing on twitter 推特上的政治交流和阴谋论分享

Online Social Networks and Media Pub Date : 2025-05-10 DOI: 10.1016/j.osnem.2025.100313

Imane Khaouja , Daniel Toribio-Flórez , Ricky Green , Cassidy Rowden , Chee Siang Ang , Karen M. Douglas

{"title":"Political communication and conspiracy theory sharing on twitter","authors":"Imane Khaouja , Daniel Toribio-Flórez , Ricky Green , Cassidy Rowden , Chee Siang Ang , 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}

引用次数: 0

Advancing depression detection on social media platforms through fine-tuned large language models 通过微调的大型语言模型，在社交媒体平台上推进抑郁症检测

Online Social Networks and Media Pub Date : 2025-03-21 DOI: 10.1016/j.osnem.2025.100311

Shahid Munir Shah , Syeda Anshrah Gillani , Mirza Samad Ahmed Baig , Muhammad Aamer Saleem , Muhammad Hamzah Siddiqui

{"title":"Advancing depression detection on social media platforms through fine-tuned large language models","authors":"Shahid Munir Shah , Syeda Anshrah Gillani , Mirza Samad Ahmed Baig , Muhammad Aamer Saleem , 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}

引用次数: 0

Community detection in Multimedia Social Networks using an attributed graph model 基于属性图模型的多媒体社交网络社区检测

Online Social Networks and Media Pub Date : 2025-03-20 DOI: 10.1016/j.osnem.2025.100312

Giancarlo Sperlì

{"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}

引用次数: 0

Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties 解释没有社会选择的同质性：及物性在同质关系形成中的作用

Online Social Networks and Media Pub Date : 2025-03-17 DOI: 10.1016/j.osnem.2025.100310

Alexander V. Gubanov , Vyacheslav L. Goiko , Ivan V. Kozitsin

{"title":"Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties","authors":"Alexander V. Gubanov , Vyacheslav L. Goiko , Ivan V. Kozitsin","doi":"10.1016/j.osnem.2025.100310","DOIUrl":"10.1016/j.osnem.2025.100310","url":null,"abstract":"<div><div>This paper investigates if an assortative social network may further amplify its assortativity following two distinct interventions: linking a random unconnected pair in the neighborhood of a vertex chosen by chance (N-protocol) or connecting a randomly selected open triplet (T-protocol). Under a series of assumptions, we derive a closed-form expression that links the expected change in assortativity caused by N-protocol and the current assortativity score. For the binary nodal characteristic or some other settings, this expression turns out to be a simple quadratic dependency, which indicates that networks with assortative mixing should become less assortative following N-protocol. Next, we provide a sufficient condition that N- and T-protocols will cause the same assortativity change. Using numerical experiments with synthetic and empirical networks with various assortativity rates, we demonstrate that our theoretical estimations tend to be quite accurate for topologies with moderate assortativity. However, for topologies with relatively high assortativity levels, the factual values of the assortativity shift tend to be greater than theoretical ones, but still negative. Empirical topologies are also characterized by relatively large divergences between N- and T-protocols, with the latter typically providing a higher assortativity decrease. We carefully explain these findings by analyzing local homophily patterns. We also consider a modified version of T-protocol that connects individuals having no less than a predefined number of common peers. For a corpus of empirical networks, we managed to characterize the threshold values of assortativity and the number of common friends above which the assortativity rate will increase following this intervention.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632120","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

Modeling and simulation of interventions’ effect on the spread of toxicity in social media 建模和模拟干预措施对社交媒体中毒性传播的影响

Online Social Networks and Media Pub Date : 2025-03-10 DOI: 10.1016/j.osnem.2025.100309

Emmanuel Addai , Nitin Agarwal , Niloofar Yousefi

{"title":"Modeling and simulation of interventions’ effect on the spread of toxicity in social media","authors":"Emmanuel Addai , Nitin Agarwal , Niloofar Yousefi","doi":"10.1016/j.osnem.2025.100309","DOIUrl":"10.1016/j.osnem.2025.100309","url":null,"abstract":"<div><div>The prevalence of toxicity on social media platforms constitutes a significant issue. Gaining insights into the factors that contribute to toxicity is essential for devising effective strategies to mitigate it. In this work, we extend and evaluate fractional control SEIQR (Susceptible, Exposed, Infected, Quarantined, Recovered) epidemiological modeling incorporating government interventions and awareness programs. The model incorporates different infected groups, moderate and high infected users, and is used to investigate the influence by each user on the overall spread of toxicity. We have evaluated the toxic post-free equilibrium point, the reproduction number <span><math><mrow><mo>(</mo><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>)</mo></mrow></math></span>, the existence-uniqueness, and the stability point. We performed the model sensitivity analysis using the Latin Hypercube Sampling-Partial Rank Correlation Coefficient (LHS-PRCC) method. For data fitting analysis, we examined data from COVID-19-related tweets. We examine the intricacies of the proposed numerical scheme, providing a robust framework for analyzing and comprehending online toxicity spread. Simulations were conducted to elucidate the effects of government interventions and public awareness programs on the prevalence and dynamics of online toxicity spread. The study’s primary accomplishment is the model’s reduction of the error rate to 0.0011. This is distinguished by the reduced need to remove users from the network. The model not only improves accuracy but also maintains a larger user base, indicating an efficient, user-centric strategy. The results suggest that both awareness programs and government interventions are crucial for managing and mitigating online toxicity spread. This study will significantly assist network providers and policymakers to identify the infected users, thereby reducing toxic conversations.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100309"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579436","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

An explainable ensemble model for revealing the level of depression in social media by considering personality traits and sentiment polarity pattern 考虑人格特质和情绪极性模式的社交媒体抑郁水平可解释的集合模型

Online Social Networks and Media Pub Date : 2025-03-08 DOI: 10.1016/j.osnem.2025.100307

Gede Aditra Pradnyana , Wiwik Anggraeni , Eko Mulyanto Yuniarno , Mauridhi Hery Purnomo

{"title":"An explainable ensemble model for revealing the level of depression in social media by considering personality traits and sentiment polarity pattern","authors":"Gede Aditra Pradnyana , Wiwik Anggraeni , Eko Mulyanto Yuniarno , Mauridhi Hery Purnomo","doi":"10.1016/j.osnem.2025.100307","DOIUrl":"10.1016/j.osnem.2025.100307","url":null,"abstract":"<div><div>Early detection of depression in mental health is crucial for better intervention. Social media has been extensively used to examine users’ behavior, motivating researchers to develop an automatic depression detection model. However, the accuracy and clarity of the reasons behind the detection results still need to be improved. Current research focuses primarily on syntactic and semantic information in user-posted texts, while other aspects of users’ psychological characteristics are often overlooked. Therefore, this study addresses the gap by proposing a novel model integrating personality traits and sentiment polarity patterns into an explainable ensemble model. Specifically, we developed two base learners for the averaged and meta-ensemble learning strategy. The first learner employed the Robustly Optimized BERT Pre-training Approach (RoBERTa). For the second learner, we combined the Random Forest and Bidirectional Long Short-Term Memory (RF-BiLSTM) methods to effectively handle the combination of personality traits and sequential information in sentiment polarity patterns. These additional features are obtained by performing domain adaptation for personality prediction and sentiment analysis using a lexicon-based model. Based on the experimental results, our ensemble model improved depression detection results by leveraging the strengths of each base learner. Our model advanced the state-of-the-art, outperforming existing models with an increase in accuracy and F1-score of 4.14% and 2.99%, respectively. The model successfully enhanced the interpretability of detection results, providing a more comprehensive understanding of the factors underlying depressive symptoms. This research highlights the potential of considering alternative additional features as a promising avenue for enhancing depression detection in social media.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100307"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579435","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