Online Social Networks and Media最新文献_第2页

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

Management of psychological emergency cases on social media: A hybrid approach combining knowledge graphs and graph neural networks 基于社交媒体的心理突发事件管理：知识图与图神经网络的混合方法

Online Social Networks and Media Pub Date : 2025-03-05 DOI: 10.1016/j.osnem.2025.100308

Mourad Ellouze , Sonda Rekik , Lamia Hadrich Belguith

{"title":"Management of psychological emergency cases on social media: A hybrid approach combining knowledge graphs and graph neural networks","authors":"Mourad Ellouze , Sonda Rekik , Lamia Hadrich Belguith","doi":"10.1016/j.osnem.2025.100308","DOIUrl":"10.1016/j.osnem.2025.100308","url":null,"abstract":"<div><div>The effects of psychological crises are evolving at an astounding rate nowadays, presenting a significant challenge for everyone involved in tracking these disorders. Therefore, we propose in this paper a hybrid approach based on linguistic processing and numerical techniques allowing to: (i) identify the presence of psychological emergencies among social network users by analyzing their textual production, (ii) determine the specific type of emergency case, (iii) elaborate a graph for each type of emergency, reflecting the different dimensions linked to the psychological emergency, allowing for a better diagnosis of the situation and providing an overall view of the crisis type, (iv) combine the separate graphs for each emergency to address the various semantic aspects. The work was accomplished using advanced language model techniques, knowledge graphs and neural network graphs. The combination of these techniques ensures that their advantages are leveraged while overcoming their limitations in terms of result generalization. The evaluation of different parts related to detecting the presence of psychological problems, predicting specific type of emergency cases, and detecting links between knowledge graphs was measured using the F-measure metric. The values derived from this measure, corresponding to the evaluation of these three tasks, are, respectively, 83%, 87% and 80%. For the evaluation of the elaboration of each graph related to specific type of emergency cases, this was accomplished using qualitative metric standards. The results obtained can be considered encouraging given the significant scale of our approach.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"46 ","pages":"Article 100308"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551494","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

DisTGranD: Granular event/sub-event classification for disaster response DisTGranD：灾难响应的细粒度事件/子事件分类

Online Social Networks and Media Pub Date : 2025-01-01 DOI: 10.1016/j.osnem.2024.100297

Ademola Adesokan , Sanjay Madria , Long Nguyen

{"title":"DisTGranD: Granular event/sub-event classification for disaster response","authors":"Ademola Adesokan , Sanjay Madria , Long Nguyen","doi":"10.1016/j.osnem.2024.100297","DOIUrl":"10.1016/j.osnem.2024.100297","url":null,"abstract":"<div><div>Efficient crisis management relies on prompt and precise analysis of disaster data from various sources, including social media. The advantage of fine-grained, annotated, class-labeled data is the provision of a diversified range of information compared to high-level label datasets. In this study, we introduce a dataset richly annotated at a low level to more accurately classify crisis-related communication. To this end, we first present DisTGranD, an extensively annotated dataset of over 47,600 tweets related to earthquakes and hurricanes. The dataset uses the Automatic Content Extraction (ACE) standard to provide detailed classification into dual-layer annotation for events and sub-events and identify critical triggers and supporting arguments. The inter-annotator evaluation of DisTGranD demonstrated high agreement among annotators, with Fleiss Kappa scores of 0.90 and 0.93 for event and sub-event types, respectively. Moreover, a transformer-based embedded phrase extraction method showed XLNet achieving an impressive 96% intra-label similarity score for event type and 97% for sub-event type. We further proposed a novel deep learning classification model, RoBiCCus, which achieved <span><math><mrow><mo>≥</mo><mn>90</mn><mtext>%</mtext></mrow></math></span> accuracy and F1-Score in the event and sub-event type classification tasks on our DisTGranD dataset and outperformed other models on publicly available disaster datasets. DisTGranD dataset represents a nuanced class-labeled framework for detecting and classifying disaster-related social media content, which can significantly aid decision-making in disaster response. This robust dataset enables deep-learning models to provide insightful, actionable data during crises. Our annotated dataset and code are publicly available on GitHub <span><span><sup>1</sup></span></span>.</div></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"45 ","pages":"Article 100297"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095218","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