ACM transactions on social computing最新文献

{"title":"Modeling the Diffusion of Fake and Real News through the Lens of the Diffusion of Innovations Theory","authors":"Abishai Joy, R. Pathak, Anu Shrestha, F. Spezzano, Donald Winiecki","doi":"10.1145/3674882","DOIUrl":"https://doi.org/10.1145/3674882","url":null,"abstract":"These days, people have increasingly used social media as a go-to resource for any information need and daily news diet. In the past decade, the news ecosystem and information flow have been dramatically transformed by the popularity of such platforms. Social media users can, in fact, easily access nearly any kind of information and then spread it nearly without friction through activities like tweets/retweets in Twitter (now X) and similar means on other social media. This seemingly innocuous activity of spreading information has a collective consequence of making social media users responsible for radical changes in the way news is distributed, including both authentic and fake news. Moreover, malicious individuals have been implicated in capitalizing on the ease of introducing and spreading information in these platforms to create misinformation, spread it to a wider audience, and subsequently influence public opinion on important topics through information diffusion. Therefore, understanding the factors that motivate a user’s decision to share is of paramount importance in understanding the information diffusion phenomenon in social media.\u0000 In this paper, we propose an approach based on the Diffusion of Innovation theory to model, characterize, and compare real and fake news sharing in social media with a focus on different levels of influencing factors including innovation, communication channels, and social system. We apply that approach to identify factors related to the spread of fake news as they relate to users, the structure of news items themselves, and the networks through which news is circulated. We address the problem of predicting real and fake news sharing as a classification task and demonstrate the potentials of the proposed features by achieving an AUROC of around 0.97 and an average precision ranging from 0.88 to 0.95, consistently outperforming baseline models with a higher margin (at least 13% of average precision). In addition, we also found out that empirically identifiable characteristics of news items themselves and users who share news are the strongest element allowing accurate prediction of real and fake news sharing, followed by network-based features. Moreover, our proposed approach can be effectively used to model news diffusion as a multi-step propagation process.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"124 27","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819920","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":"Longitudinal Loyalty: Understanding The Barriers To Running Longitudinal Studies On Crowdsourcing Platforms","authors":"Michael Soprano, Kevin Roitero, U. Gadiraju, Eddy Maddalena, Gianluca Demartini","doi":"10.1145/3674884","DOIUrl":"https://doi.org/10.1145/3674884","url":null,"abstract":"\u0000 Crowdsourcing tasks have been widely used to collect a large number of human labels at scale. While some of these tasks are deployed by requesters and performed only once by crowd workers, others require the same worker to perform the same task or a variant of it more than once, thus participating in a so-called\u0000 longitudinal study\u0000 . Despite the prevalence of longitudinal studies in crowdsourcing, there is a limited understanding of factors that influence worker participation in them across different crowdsourcing marketplaces. We present results from a large-scale survey of 300 workers on 3 different micro-task crowdsourcing platforms: Amazon Mechanical Turk, Prolific and Toloka. The aim is to understand how longitudinal studies are performed using crowdsourcing. We collect answers about 547 experiences and we analyze them both quantitatively and qualitatively. We synthesize 17 take-home messages about longitudinal studies together with 8 recommendations for task requesters and 5 best practices for crowdsourcing platforms to adequately conduct and support such kinds of studies. We release the survey and the data at: https://osf.io/h4du9/.\u0000","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"138 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656012","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":"User Engagement Triggers in Social Media Discourse on Biodiversity Conservation","authors":"Nina Dethlefs, H. Cuayáhuitl","doi":"10.1145/3662685","DOIUrl":"https://doi.org/10.1145/3662685","url":null,"abstract":"\u0000 Studies in digital conservation have increasingly used social media in recent years as a source of data to understand the interactions between humans and nature, model and monitor biodiversity, and analyse online discourse about the conservation of species. Current approaches to digital conservation are for the most part purely frequentist, i.e. focused on easily trackable and quantifiable features, or purely qualitative, which allows a deeper level of interpretation, but is less scalable. Our approach aims to evaluate the applicability of recent advances in deep learning in combination with semi-automatic analysis. We present a multimodal neural learning framework that experiments with different combinations of linguistic and visual features and metadata of tweets to predict user engagement from a function of\u0000 likes\u0000 and\u0000 retweets\u0000 . Experimental results show that text is the single most effective modality for prediction when a large amount of training data is available. For smaller datasets, drawing information from multiple modalities can boost performance. Notably, we find a negative effect of large pre-trained language models when dealing with substantially unbalanced datasets. A qualitative analysis into the triggers of user engagement with tweets reveals that it emerges from a combination of online discourse topic and sentiment, and is often amplified by user activity, e.g. when content originates from an influencer account. We find clear evidence of existing sub-communities around specific topics, including\u0000 animal photography and sightings\u0000 ,\u0000 illegal wildlife trade and trophy hunting\u0000 ,\u0000 deforestation and destruction of nature\u0000 and\u0000 climate change and action\u0000 in a broader sense.\u0000","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141663910","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":"Social Media, Sentiments and Political Discourse ? An Exploratory Study of the 2021 Canadian Federal Election","authors":"Hiba Mohammad Noor, Ozgur Turetken, Mehmet Akgul","doi":"10.1145/3665450","DOIUrl":"https://doi.org/10.1145/3665450","url":null,"abstract":"Social media are widely used for online political discourse. Opinions shared on social media have different sentiments associated with them. Given the very high adoption rates of Twitter (now X) among adults, those who share their opinions on Twitter (X) not only represent a sizable segment of the society, but also influence (through emotion contagion) an even larger segment who are passive (non-contributing) users of the platform. Further, the discourse that is initiated on Twitter (X) typically spreads to other more traditional media. As a result, Twitter (X) is influential, which makes it useful to understand the factors related to the sentiments expressed in tweets. Such understanding can help policymakers to take actions that align with public needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments associated with political discourse on Twitter (X). We also explore virality, i.e., how much a message (the tweet) spreads, and the relationship between sentiments and virality. Finally, we explore whether the clustering of tweets among sentiment and virality groups can improve the potential of social media content for predicting election results. Sentiment Analysis of 764,000 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. We found some keywords predominantly present within a positive or negative sentiment that are suggestive of entities or ideas to invest in or mitigate by political decision makers. We were also able to find partial evidence for “negativity bias” by detecting a negative relationship between sentiment (positivity) and virality (number of retweets). Finally, we demonstrated that high positivity on the political discourse does not reflect election outcomes and examining Twitter (X) content in more neutral groups can improve predictive power. Our findings have implications for political decision makers and social media analytics researchers.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141685928","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":"WallStreetBets: Assessing the Collective Intelligence of Reddit for Investment Advice","authors":"Tolga Buz, Gerard de Melo","doi":"10.1145/3660760","DOIUrl":"https://doi.org/10.1145/3660760","url":null,"abstract":"The WallStreetBets (WSB) community on Reddit gained prominence for its role in the GameStop saga and the resulting meme stock phenomenon. Concurrently, this has boosted the popularity of finance-related communities on Reddit, with the top five totalling more than 25 million subscribers at the time of writing. However, little is known about the reliability of the advice disseminated in these communities, which is a relevant research question within the field of social computing. In this study, we examine the collective intelligence of WSB, the largest and most active subreddit focused on the stock market, and assess its potential as a democratizing force in enabling access to financial knowledge. First, we establish that WSB meets several criteria to be considered a collectively intelligent crowd. Then, we test our hypothesis quantitatively by analyzing Reddit posts and financial data from a 28-month period to evaluate how successful an investor relying on WSB recommendations could have been. We define a portfolio of WSB’s most discussed stocks which shows significant growth, outperforming the S&P 500 index over the reviewed time frame. We further find that following buy signals at the time they are posted on WSB leads to positive outcomes over the long run, and that the GameStop hype merely amplified previously existing characteristics. The WSB portfolio underperforms the broader market during downturns, but recovers more quickly and achieves higher profits afterwards. The results of our work can be generalized to comparable finance-related communities, indicating that their original purpose of leisurely entertainment has already been extended towards tangible real-world value.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141688190","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":"Combating Islamophobia: Compromise, Community, and Harmony in Mitigating Harmful Online Content","authors":"M. Rifat, Ashratuz Zavin Asha, Shivesh Jadon, Xinyi Yan, Shion Guha, Syed Ishtiaque Ahmed","doi":"10.1145/3641510","DOIUrl":"https://doi.org/10.1145/3641510","url":null,"abstract":"Despite significant advances in content moderation within HCI, social computing scholarship in this area remains constrained by secular values and Western interpretations of justice. As a result, current literature often overlooks religious and spiritual sensibilities, as well as communal peacebuilding efforts even when the harms originate from and strongly connected to faith sensitivities, such as Islamophobia. This paper presents findings from a design and evaluation study on the reporting and moderation of Islamophobic posts on Twitter (currently known as “X”). By utilizing HCI theories and readily available NLP techniques, we developed an online tool for reporting and moderating Islamophobic tweets. We subsequently conducted usability studies, contextual inquiries, and interviews with 32 participants to assess the tool’s effectiveness in addressing Islamophobic content. Our study revealed that factors such as faith-related knowledge practices, fact-checking, communal leadership, social harmony, and the cultural-religious value of “compromise” significantly influence reactions to Islamophobic posts online. Expanding on these findings and drawing from the literature on conflict resolution in theology, legal studies, and justification., we explore how “Sulha,” a community-driven process for mitigating conflict and restoring communal peace, can cater to faith-based sensibilities in reporting and moderating Islamophobic content. Therefore, this paper complements existing content moderation literature with recommendation of adapting faith-sensitivities for in the design of tools and policies to mitigate Islamophobia and similar faith-related online harms.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"177 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140437667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Analysis of Problems in Open Collaborative Data Engineering","authors":"Philip Heltweg, Dirk Riehle","doi":"10.1145/3629040","DOIUrl":"https://doi.org/10.1145/3629040","url":null,"abstract":"Collaborative workflows are common in open-source software development. They reduce individual costs and improve the quality of work results. Open data shares many characteristics with open-source software as it can be used, modified, and redistributed by anyone, for free. However, in contrast to open-source software engineering, collaborative data engineering on open data lacks a shared understanding of processes, methods, and tools. This article presents a systematic literature review of collaboration processes, methods, and tools in data engineering as performed by open data users. An additional interview study with practitioners confirms and enhances the findings and strengthens the resulting insights. We find an ecosystem with heterogeneous participants and no standardized processes, methods, and tools. Participants face a variety of technical and social challenges during their work. Our work provides a structured overview of collaboration systems in open collaborative data engineering, enabling further research. Additionally, we contribute preliminary guidelines for successful open collaborative data engineering projects and recommendations to increase its adoption for open data ecosystems.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"23 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136233243","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":"Online Self-Disclosure, Social Support, and User Engagement During the COVID-19 Pandemic","authors":"Jooyoung Lee, Sarah Rajtmajer, Eesha Srivatsavaya, Shomir Wilson","doi":"10.1145/3617654","DOIUrl":"https://doi.org/10.1145/3617654","url":null,"abstract":"We investigate relationships between online self-disclosure and received social support and user engagement during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users’ comments. We then analyze the effect of personal information sharing and posts’ topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: 1) users were more likely to share their age, education, and location information when seeking both informational and emotional support, as opposed to pursuing either one; 2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; 3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers’ self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; 4) post characteristics affecting audience response differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social support and user engagement.","PeriodicalId":486759,"journal":{"name":"ACM transactions on social computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014153","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}