Online Social Networks and Media最新文献

{"title":"Towards echo chamber assessment by employing aspect-based sentiment analysis and GDM consensus metrics","authors":"Miriam Amendola ,&nbsp;Danilo Cavaliere ,&nbsp;Carmen De Maio ,&nbsp;Giuseppe Fenza ,&nbsp;Vincenzo Loia","doi":"10.1016/j.osnem.2024.100276","DOIUrl":"https://doi.org/10.1016/j.osnem.2024.100276","url":null,"abstract":"<div><p>Echo chambers naturally occur on social networks, where individuals join groups to share and discuss their own interests driven by algorithms that steer their beliefs and behaviours based on their emotions, biases, and cognitive vulnerabilities. According to recent research on information manipulation and interference, echo chambers have become crucial weapons in the arsenal of Cognitive Warfare for amplifying the effect of psychological techniques aimed at altering information and narratives to influence public perception and shape opinions. The research is focusing on the definition of assessment methods for detecting emerging echo chambers and monitoring their evolution over time. In this sense, this work stresses the complementary role of the existing topology-based metrics and the semantics of the viewpoints underlying groups as well as their belonging users. Indeed, this paper proposes a metric based on consensus Group Decision-Making (GDM) that acquires community members’ opinions through Aspect-Based Sentiment Analysis (ABSA) and applies consensus metrics to determine the agreement within a single community and between distinct communities. The potential of the proposed metrics have been evaluated on two public datasets of tweets through comparisons with sentiment-aware opinions analysis and state-of-the-art metrics for polarization and echo chamber detection. The results reveal that topology-based metrics strictly depending on random walks over the individuals are not sufficient to fully depict the communities closeness on topics and their prevailing beliefs coming out from content analysis.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468696424000016/pdfft?md5=201f0c26cc0e647ab968aea16e27c59d&pid=1-s2.0-S2468696424000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139732561","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}

{"title":"Exploring unsupervised textual representations generated by neural language models in the context of automatic tweet stream summarization","authors":"Alexis Dusart,&nbsp;Karen Pinel-Sauvagnat,&nbsp;Gilles Hubert","doi":"10.1016/j.osnem.2023.100272","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100272","url":null,"abstract":"<div><p><span>Users are often overwhelmed by the amount of information generated on online social networks<span> and media (OSNEM), in particular Twitter, during particular events. Summarizing the information streams would help them be informed in a reasonable time. In parallel, recent state of the art in summarization has a special focus on deep neural models and pre-trained </span></span>language models.</p><p>In this context, we aim at (i) evaluating different pre-trained language model (PLM) to represent microblogs<span> (i.e., tweets), and (ii) to identify the most suitable ones in a summarization context, as well as (iii) to see how neural models can be used knowing the issue of input size limitation of such models. For this purpose, we divided the problem into 3 questions and made experiments on 3 different datasets. Using a simple greedy algorithm<span>, we first compared several pre-trained models for single tweet representation. We then evaluated the quality of the average representation of the stream and sought to use it as a starting point for a neural approach. First results show the interest of using USE and Sentence-BERT representations for tweet stream summarization, as well as the great potential of using the average representation of the stream.</span></span></p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987037","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":"What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter","authors":"Timo Spinde ,&nbsp;Elisabeth Richter ,&nbsp;Martin Wessel ,&nbsp;Juhi Kulshrestha ,&nbsp;Karsten Donnay","doi":"10.1016/j.osnem.2023.100264","DOIUrl":"10.1016/j.osnem.2023.100264","url":null,"abstract":"<div><p>News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage.</p><p>This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - <strong>B</strong>ias <strong>A</strong>nd <strong>T</strong>witter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles.</p><p>Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>01</mn></mrow></math></span>), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42750623","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":"Projection of Socio-Linguistic markers in a semantic context and its application to online social networks","authors":"Tomaso Erseghe ,&nbsp;Leonardo Badia ,&nbsp;Lejla Džanko ,&nbsp;Magdalena Formanowicz ,&nbsp;Jan Nikadon ,&nbsp;Caterina Suitner","doi":"10.1016/j.osnem.2023.100271","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100271","url":null,"abstract":"<div><p>Relevant socio-psychological processes can be detected in social networks thanks to an analysis of linguistic markers that sheds light on the characteristics and dynamics of the social discourse. Usually, linguistic markers comprise a list of words representative of a given construct; however, this approach does not account for contextual interdependencies of words, which can amplify or diminish the relevance of a particular word. In this paper, we present and leverage a scalable method called PageRank-like marker projection (PLMP) that addresses this problem. Its rationale, inspired by PageRank, is meant to fully exploit the interdependencies in a semantic network to project markers from a social discourse level (tweets) to its semantic elements (words). We show how PLMP is able to associate markers with specific words from their semantic context, which allows for an even richer interpretation of the online sentiment. We demonstrate the effectiveness of PLMP in practice by considering specific instances of social discourse on Twitter for three exemplary calls to collective action.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701587","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":"Reputation assessment and visitor arrival forecasts for data driven tourism attractions assessment","authors":"Enrico Collini,&nbsp;Paolo Nesi,&nbsp;Gianni Pantaleo","doi":"10.1016/j.osnem.2023.100274","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100274","url":null,"abstract":"<div><p>Tourism is vital for most historical and cultural cities. In the context of Smart Cities, there are numerous data sources in tourism domain that could be analyzed to monitor and forecast a range of different indicators related to touristic locations and attractions. In this paper, we propose a framework which exploits social media and big data to forecast both online reputation and touristic attraction presences. To this end, some techniques have been tested and proposed on the basis of machine learning, deep learning, causality assessment and explainable Artificial Intelligence, so as to provide evidence of the relevant variables for each prediction and estimation. An approach has been introduced to analyze the explainability of the proposed solutions, i.e., a multilingual sentiment analysis tool for social media data based on transformers to compare data sources as Trip Advisor and Twitter. Furthermore, causality analysis has been performed to evaluate the temporal impact of social media posts and other factors with respect to the number of presences. The work has been developed in the context of Herit-Data, a European Commission funded project on the exploitation of big data for tourism management and based on the Snap4City infrastructure and platform. Herit-Data has developed solutions for 6 major European touristic locations. In this paper, some of the solutions developed for Florence, Italy and Pont du Gard, France, are reported.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468696423000332/pdfft?md5=0686f0ed64956b2a291c790ccfa7844b&pid=1-s2.0-S2468696423000332-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92046150","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}

{"title":"A multi-layer trust framework for Self Sovereign Identity on blockchain","authors":"Andrea De Salve ,&nbsp;Damiano Di Francesco Maesa ,&nbsp;Paolo Mori ,&nbsp;Laura Ricci ,&nbsp;Alessandro Puccia","doi":"10.1016/j.osnem.2023.100265","DOIUrl":"10.1016/j.osnem.2023.100265","url":null,"abstract":"<div><p>The recent interest for decentralised systems and decentralisation of the control over users’ data brings a shift in the way identities and their information are managed. Self Sovereign Identity (SSI) has been proposed as the next generation paradigm for decentralised identity management. Research on SSI is getting more and more traction, focusing mainly on the management of users’ identifiers and on providing a standard way to express and verify credentials. Instead, this paper focuses on the understanding of the role of trust in SSI and it provides new insight into the trust relationships existing between the different SSI actors. Indeed, the analysis of such roles and the relationships existing between SSI actors reveals that the current paradigm suffers from trust issues between the verifier and the issuer of a verifiable credential.</p><p>In order to cope this problem, the paper proposes a new multi-layer framework that exploits trust relationships defined by the actors of the SSI standards (verifiers and issuers of verifiable credentials). An implementation of the framework through Solidity smart contracts has been proposed and deployed on both private and public blockchain networks in order to assess its capabilities. In addition, a dataset related to the spread of spam reviews has been exploited to test the benefits and performance of the proposed framework, demonstrating that it is able to improve the reliability of the SSI paradigm in real-world scenario.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48451552","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":"De-sounding echo chambers: Simulation-based analysis of polarization dynamics in social networks","authors":"Tim Donkers,&nbsp;Jürgen Ziegler","doi":"10.1016/j.osnem.2023.100275","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100275","url":null,"abstract":"<div><p>As online social networks have become dominant platforms for public discourse worldwide, there is growing anecdotal evidence of a concurrent rise in social antagonisms. Yet, while the increase in polarization is evident, the extent to which these digital communication ecosystems are driving this shift remains elusive. A dominant scholarly perspective suggests that digital social media lead to the compartmentalization of information channels, potentially culminating in the emergence of <em>echo chambers</em>. However, a growing body of empirical research suggests that the mechanisms influencing ideological demarcation are more complex than a complete communicative decoupling of user groups. This study introduces two intertwined principles that elucidate the dynamics of digital communication: First, socio-cognitive biases of social group formation enforce internal congruence of ideological communities by demarcation from outsiders. Second, algorithmic personalization of content contributes to ideological network formation by creating social redundancy, wherein the same individuals frequently interact in various roles, such as authors, recipients, or disseminators of messages, leading to a surplus of shared ideological fragments. Leveraging these insights, we pioneer a computational simulation model, integrating machine learning based on behavioral data and established recommendation technologies, to explore the evolution of social network structures in digital exchanges. Utilizing advanced methods in opinion dynamics, our model uniquely captures both the algorithmic delivery and the subsequent dissemination of messages by users. Our findings reveal that in ambiguous debate scenarios, the dual components of our model are essential to accurately capture the emergence of social polarization. Consequently, our model offers a forward-looking perspective on the evolution of network communication, facilitating nuanced comparisons with empirical graph benchmarks.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468696423000344/pdfft?md5=63d57f3bfbfbb90e78b38200b817651b&pid=1-s2.0-S2468696423000344-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138466569","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}

{"title":"The Least Cost Directed Perfect Awareness Problem: complexity, algorithms and computations","authors":"Felipe de C. Pereira,&nbsp;Pedro J. de Rezende","doi":"10.1016/j.osnem.2023.100255","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100255","url":null,"abstract":"<div><p>In this paper, we investigate the Least Cost Directed Perfect Awareness Problem (<span>LDPAP</span><span>), a combinatorial optimization problem that deals with the spread of information on social networks. The objective of </span><span>LDPAP</span> is to minimize the cost of recruiting individuals capable of starting a propagation of a given news so that it reaches everyone. By showing that <span>LDPAP</span> can be regarded as a generalization of the Perfect Awareness Problem, we establish that <span>LDPAP</span> is <span>NP</span>-hard and we then prove that it remains <span>NP</span><span>-hard even when restricted to directed acyclic graphs. Our contributions also include two integer programming<span> formulations, a heuristic based on the metaheuristic </span></span><span>GRASP</span> and a useful lower bound for the objective function. Lastly, we present extensive experiments comparing the efficiency and efficacy of our heuristic and mathematical models both on synthetic and on real-world datasets.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728247","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":"Multitask learning for recognizing stress and depression in social media","authors":"Loukas Ilias,&nbsp;Dimitris Askounis","doi":"10.1016/j.osnem.2023.100270","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100270","url":null,"abstract":"<div><p>Stress and depression are prevalent nowadays across people of all ages due to the quick paces of life. People use social media to express their feelings. Thus, social media constitute a valuable form of information for the early recognition of stress and depression. Although many research works have been introduced targeting the early recognition of stress and depression, there are still limitations. There have been proposed multi-task learning settings, which use depression and emotion (or figurative language) as the primary and auxiliary tasks respectively. However, although stress is inextricably linked with depression, researchers face these two tasks as two separate tasks. To address these limitations, we present the first study, which exploits two different datasets collected under different conditions, and introduce two multitask learning frameworks, which use depression and stress as the main and auxiliary tasks respectively. Specifically, we use a depression dataset and a stressful dataset including stressful posts from ten subreddits of five domains. In terms of the first approach, each post passes through a shared BERT<span> layer, which is updated by both tasks. Next, two separate BERT encoder layers are exploited, which are updated by each task separately. Regarding the second approach, it consists of shared and task-specific layers weighted by attention fusion networks. We conduct a series of experiments and compare our approaches with existing research initiatives, single-task learning, and transfer learning. Experiments show multiple advantages of our approaches over state-of-the-art ones.</span></p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728431","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":"Using social-media-network ties for predicting intended protest participation in Russia","authors":"Elizaveta Kopacheva ,&nbsp;Masoud Fatemi ,&nbsp;Kostiantyn Kucher","doi":"10.1016/j.osnem.2023.100273","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100273","url":null,"abstract":"<div><p>Previous research has highlighted the importance of network structures in information diffusion on social media. In this study, we explore the role of an individual’s social network structure in predicting publicly announced intention of protest participation. Using the case of ecological protests in Russia and applying machine learning to publicly-available VKontakte data, we classify users into protesters and non-protesters. We have found that personal social networks have a high predictive power allowing user classification with an accuracy of 81%. Meanwhile, using all public VKontakte data, including memberships in activist groups and friendship ties to protesters, we were able to classify users into protesters and non-protesters with a higher accuracy of 96%. Our study contributes to the political-participation literature by demonstrating the importance of personal social networks in predicting protest participation. Our results suggest that in some cases, the likelihood of participating in protests can be significantly influenced by elements of a personal-network structure, inter alia, network density and size. Further explanatory research should be done to explore the mechanisms underlying these relationships.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468696423000320/pdfft?md5=0b82a674e27381ee51954b364a215f03&pid=1-s2.0-S2468696423000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92046151","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}