Online Social Networks and Media最新文献

{"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":"37 ","pages":"Article 100255"},"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":"37 ","pages":"Article 100270"},"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":"37 ","pages":"Article 100273"},"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}

{"title":"Parasocial diffusion: K-pop fandoms help drive COVID-19 public health messaging on social media","authors":"Ho-Chun Herbert Chang ,&nbsp;Becky Pham ,&nbsp;Emilio Ferrara","doi":"10.1016/j.osnem.2023.100267","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100267","url":null,"abstract":"<div><p>We examine an unexpected but significant source of positive public health messaging during the COVID-19 pandemic—K-pop fandoms. Leveraging more than 7 million tweets related to mask-wearing and K-pop between March 2020 and December 2021, we analyzed the online spread of the hashtag #WearAMask and vaccine-related tweets amid anti-mask sentiments and public health misinformation. Analyses reveal the South Korean boyband BTS as one of the most significant driver of health discourse. Tweets from health agencies and prominent figures that mentioned K-pop generate 111 times more online responses compared to tweets that did not. These tweets also elicited strong responses from South America, Southeast Asia, and interior States—areas often neglected by mainstream social media campaigns. Network and temporal analysis show increased use from right-leaning elites over time. Mechanistically, strong-levels of parasocial engagement and connectedness allow sustained activism in the community. Our results suggest that public health institutions may leverage pre-existing audience markets to synergistically diffuse and target under-served communities both domestically and globally, especially during health crises.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"37 ","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701459","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":"Should we agree to disagree about Twitter’s bot problem?","authors":"Onur Varol","doi":"10.1016/j.osnem.2023.100263","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100263","url":null,"abstract":"<div><p>Bots, simply defined as accounts controlled by automation, can be used as a weapon for online manipulation and pose a threat to the health of platforms. Researchers have studied online platforms to detect, estimate, and characterize bot accounts. Concerns about the prevalence of bots were raised following Elon Musk’s bid to acquire Twitter. In this work, we want to stress that crucial questions need to be answered in order to make a proper estimation and compare different methodologies and definitions based on behaviors and activities; otherwise the real questions concerning the health of online platforms will be confounded by disagreements about definitions and models. We argue how assumptions on bot-likely behavior, the detection approach, and the population inspected can affect the estimation of the percentage of bots on Twitter. Finally, we emphasize the responsibility of platforms to be vigilant, transparent, and unbiased in dealing with threats that may affect their users.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"37 ","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728252","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":"The Least Cost Directed Perfect Awareness Problem: complexity, algorithms and computations","authors":"Felipe de C. Pereira, P. D. de Rezende","doi":"10.1016/j.osnem.2023.100255","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100255","url":null,"abstract":"","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54996800","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":"Modeling communication asymmetry and content personalization in online social networks","authors":"Franco Galante ,&nbsp;Luca Vassio ,&nbsp;Michele Garetto ,&nbsp;Emilio Leonardi","doi":"10.1016/j.osnem.2023.100269","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100269","url":null,"abstract":"<div><p><span>The increasing popularity of online social networks (OSNs) attracted growing interest in modeling social interactions. On online social platforms, a few individuals, commonly referred to as </span><em>influencers</em><span>, produce the majority of content consumed by users and hegemonize the landscape of the social debate. However, classical opinion models do not capture this communication asymmetry. We develop an opinion model inspired by observations on social media platforms<span> with two main objectives: first, to describe this inherent communication asymmetry in OSNs, and second, to model the effects of content personalization. We derive a Fokker–Planck equation for the temporal evolution of users’ opinion distribution and analytically characterize the stationary system behavior. Analytical results, confirmed by Monte-Carlo simulations, show how strict forms of content personalization tend to radicalize user opinion, leading to the emergence of </span></span><em>echo chambers</em>, and favor <em>structurally advantaged</em><span> influencers. As an example application, we apply our model to Facebook data during the Italian government crisis in 2019. Our work provides a flexible framework to evaluate the impact of content personalization on the opinion formation process, focusing on the interaction between influential individuals and regular users. This framework is interesting in the context of marketing and advertising, misinformation spreading, politics and activism.</span></p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"37 ","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728432","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":"Relationship privacy preservation in photo sharing","authors":"Jialin Liu,&nbsp;Lin Li,&nbsp;Na Li","doi":"10.1016/j.osnem.2023.100268","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100268","url":null,"abstract":"<div><p>In recent years, Online Social Networks<span> (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem to handle the trade-off between preserving relationship privacy and maintaining the photo utility. To cope with the problem, we developed Greedy and Linear Programming based face blocking technologies. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.</span></p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"37 ","pages":"Article 100268"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728492","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":"Characterizing growth in decentralized socio-economic networks through triadic closure-related network motifs","authors":"Cheick Tidiane Ba,&nbsp;Matteo Zignani,&nbsp;Sabrina Gaito","doi":"10.1016/j.osnem.2023.100266","DOIUrl":"10.1016/j.osnem.2023.100266","url":null,"abstract":"<div><p>The emergence of the Web3 paradigm has led to more and more systems built on blockchain technology and relying on cryptocurrency tokens – both fungible and non-fungible – to sustain themselves and generate profit. The growth and success of these platforms are strongly dependent on the growth and evolution of the trade relationships among users. In this context, it is of paramount importance to understand the mechanism behind the evolution and growth dynamics of these economic ties: however, in these systems the trade relationships are strictly intertwined with social dynamics, posing significant challenges in the analysis. One of the most important mechanisms behind the evolution of social networks is the triadic closure principle: given the strict link between social and economic spheres, the mechanism emerges as a potential candidate among mechanisms in literature. Therefore in this work, we extend the existing methodology for triadic closure studies and adapt it to directed networks. We performed an analysis centered around 3-node subgraphs known as “triads” and statistically significant triads referred to as “triadic motifs”, both from a static and temporal perspective. The methodology was applied to various decentralized socio-economic networks with distinct levels of social components. These networks include currency transfers from the blockchain-based online social media platform Steemit, trade relationships among NFT sellers and buyers on the Ethereum blockchain, and a blockchain-based currency designed for humanitarian aid called Sarafu. Our measurements show how triadic closure is relevant during the evolution of these platforms and, for a few aspects, more impactful than centralized online social networks, where triadic closure is also incentivized by recommendation systems. Moreover, we are able to highlight both similarities and differences across networks with different levels of social components, both from a static and temporal standpoint. Overall our work presents strong evidence that triadic closure is an important evolutionary mechanism in decentralized socio-economic networks. Our findings provide a stepping stone in the study of decentralized socio-economic networks. Understanding the evolution of other decentralized networks, not following the same Web3 paradigm or with different social components will provide valuable insight into the understanding of dynamics in decentralized systems and potentially improve their design process.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"37 ","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43869986","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":"Beyond fear and anger: A global analysis of emotional response to Covid-19 news on Twitter","authors":"Francisco Bráulio Oliveira ,&nbsp;Davoud Mougouei ,&nbsp;Amanul Haque ,&nbsp;Jaime Simão Sichman ,&nbsp;Hoa Khanh Dam ,&nbsp;Simon Evans ,&nbsp;Aditya Ghose ,&nbsp;Munindar P. Singh","doi":"10.1016/j.osnem.2023.100253","DOIUrl":"https://doi.org/10.1016/j.osnem.2023.100253","url":null,"abstract":"<div><p>The media has been used to disseminate public information amid the Covid-19 pandemic. However, Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to Covid-19 news, we studied user comments on news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employed a deep-learning-based model to identify the basic human emotions defined by Ekman in comments related to Covid-19 news. Additionally, we implemented Latent Dirichlet Allocation (LDA) to identify the news topics. Our analysis found that while nearly half of the user comments showed no significant emotions, negative emotions were more common. Anger was the most prevalent emotion, particularly in the media and comments regarding political responses and governmental actions in the United States. On the other hand, joy was mainly linked to media outlets from the Philippines and news about vaccination. Over time, anger consistently remained the most prevalent emotion, with fear being most prevalent at the start of the pandemic but decreasing over time, occasionally spiking with news on Covid-19 variants, cases, and deaths. Emotions also varied across media outlets, with Fox News being associated with the highest level of disgust, the second-highest level of anger, and the lowest level of fear. Sadness was highest at Citizen TV, SABC, and Nation Africa, all three African media outlets. Additionally, fear was most evident in the comments on news from The Times of India.</p></div>","PeriodicalId":52228,"journal":{"name":"Online Social Networks and Media","volume":"36 ","pages":"Article 100253"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49888615","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}