EPJ Data Science最新文献_第6页

Account credibility inference based on news-sharing networks 基于新闻共享网络的账户可信度推断

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-31 DOI: 10.1140/epjds/s13688-024-00450-9

Bao Tran Truong, Oliver Melbourne Allen, Filippo Menczer

{"title":"Account credibility inference based on news-sharing networks","authors":"Bao Tran Truong, Oliver Melbourne Allen, Filippo Menczer","doi":"10.1140/epjds/s13688-024-00450-9","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00450-9","url":null,"abstract":"The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information diffusion patterns, in particular leveraging two networks: the reshare network, capturing an account’s trust in other accounts, and the bipartite account-source network, capturing an account’s trust in media sources. We extend network centrality measures and graph embedding techniques, systematically comparing these algorithms on data from diverse contexts and social media platforms. We demonstrate that both kinds of trust networks provide useful signals for estimating account credibility. Some of the proposed methods yield high accuracy, providing promising solutions to promote the dissemination of reliable information in online communities. Two kinds of homophily emerge from our results: accounts tend to have similar credibility if they reshare each other’s content or share content from similar sources. Our methodology invites further investigation into the relationship between accounts and news sources to better characterize misinformation spreaders.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"23 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139645231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Which sport is becoming more predictable? A cross-discipline analysis of predictability in team sports 哪种运动的可预测性更高？团队运动可预测性的跨学科分析

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-29 DOI: 10.1140/epjds/s13688-024-00448-3

Michele Coscia

{"title":"Which sport is becoming more predictable? A cross-discipline analysis of predictability in team sports","authors":"Michele Coscia","doi":"10.1140/epjds/s13688-024-00448-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00448-3","url":null,"abstract":"Professional sports are a cultural activity beloved by many, and a global hundred-billion-dollar industry. In this paper, we investigate the trends of match outcome predictability, assuming that the public is more interested in an event if there is some uncertainty about who will win. We reproduce previous methodology focused on soccer and we expand it by analyzing more than 300,000 matches in the 1996-2023 period from nine disciplines, to identify which disciplines are getting more/less predictable over time. We investigate the home advantage effect, since it can affect outcome predictability and it has been impacted by the COVID-19 pandemic. Going beyond previous work, we estimate which sport management model – between the egalitarian one popular in North America and the rich-get-richer used in Europe – leads to more uncertain outcomes. Our results show that there is no generalized trend in predictability across sport disciplines, that home advantage has been decreasing independently from the pandemic, and that sports managed with the egalitarian North American approach tend to be less predictable. We base our result on a predictive model that ranks team by analyzing the directed network of who-beats-whom, where the most central teams in the network are expected to be the best performing ones. Our results are robust to the measure we use for the prediction.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"43 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139587346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling teams performance using deep representational learning on graphs 利用图形上的深度表征学习为团队绩效建模

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-19 DOI: 10.1140/epjds/s13688-023-00442-1

Francesco Carli, Pietro Foini, Nicolò Gozzi, Nicola Perra, Rossano Schifanella

{"title":"Modeling teams performance using deep representational learning on graphs","authors":"Francesco Carli, Pietro Foini, Nicolò Gozzi, Nicola Perra, Rossano Schifanella","doi":"10.1140/epjds/s13688-023-00442-1","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00442-1","url":null,"abstract":"Most human activities require collaborations within and across formal or informal teams. Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team’s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams’ success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. We test model performance on various domains, outperforming most classical and neural baselines. Moreover, we include synthetic datasets designed to validate how the model disentangles the intended properties on which our model vastly outperforms baselines.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"29 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139508999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of home detection algorithms using smartphone GPS data 使用智能手机 GPS 数据的住宅检测算法比较

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-16 DOI: 10.1140/epjds/s13688-023-00447-w

Rajat Verma, Shagun Mittal, Zengxiang Lei, Xiaowei Chen, Satish V. Ukkusuri

{"title":"Comparison of home detection algorithms using smartphone GPS data","authors":"Rajat Verma, Shagun Mittal, Zengxiang Lei, Xiaowei Chen, Satish V. Ukkusuri","doi":"10.1140/epjds/s13688-023-00447-w","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00447-w","url":null,"abstract":"Estimation of people’s home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this study, we review existing HDAs and examine five HDAs using eight high-quality mobile phone geolocation datasets. These include four commonly used HDAs as well as an HDA proposed in this work. To make quantitative comparisons, we propose three novel metrics to assess the quality of detected home locations and test them on eight datasets across four U.S. cities. We find that all three metrics show a consistent rank of HDAs’ performances, with the proposed HDA outperforming the others. We infer that the temporal and spatial continuity of the geolocation data points matters more than the overall size of the data for accurate home detection. We also find that HDAs with high (and similar) performance metrics tend to create results with better consistency and closer to common expectations. Further, the performance deteriorates with decreasing data quality of the devices, though the patterns of relative performance persist. Finally, we show how the differences in home detection can lead to substantial differences in subsequent inferences using two case studies—(i) hurricane evacuation estimation, and (ii) correlation of mobility patterns with socioeconomic status. Our work contributes to improving the transparency of large-scale human mobility assessment applications.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"35 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139474602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What relational event models can reveal: Commentary on Thomas Grund’s “Dynamics of Denunciation: The Limits of a Scandal” 关系事件模型能揭示什么？对托马斯-格伦德《谴责的动力》的评论：丑闻的局限性

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-11 DOI: 10.1140/epjds/s13688-023-00432-3

{"title":"What relational event models can reveal: Commentary on Thomas Grund’s “Dynamics of Denunciation: The Limits of a Scandal”","authors":"","doi":"10.1140/epjds/s13688-023-00432-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00432-3","url":null,"abstract":"<h3>Abstract</h3> This article provides a commentary on Thomas Grund’s International Conference on Computational Social Science 2021 keynote “Dynamics of Denunciation: The Limits of a Scandal”. The keynote presents results from research investigating the relational dynamics underpinning the denunciations provided in testimonies relating to a Canadian political scandal. Grund uses relational event models to test hypotheses about the social mechanisms driving the denunciations. Although denunciation should depend only on who is guilty and not on who has said what up to that point, Grund’s study finds evidence in support of a number of relational mechanisms influencing the denunciation process. Grund argues that the apparent influence of past denunciations on testimonies reveals the limits of the inquiry process itself and what it can reveal about a scandal. This article reviews Grund’s talk and puts the work in a broader context of using approaches rooted in event history modelling and social network theory to illuminate the processes defining social interaction data. It highlights ways in which the keynote can inform the development of computational social science approaches to analysing such data, and argues that the value of such an analysis has implications for scholarship beyond the social sciences.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"86 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the duration of face-to-face contacts 关于面对面接触的持续时间

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-10 DOI: 10.1140/epjds/s13688-023-00444-z

{"title":"On the duration of face-to-face contacts","authors":"","doi":"10.1140/epjds/s13688-023-00444-z","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00444-z","url":null,"abstract":"<h3>Abstract</h3> The analysis of social networks, in particular those describing face-to-face interactions between individuals, is complex due to the intertwining of the topological and temporal aspects. We revisit here both, using public data recorded by the sociopatterns wearable sensors in some very different sociological environments, putting particular emphasis on the contact duration timelines. As well known, the distribution of the contact duration for all the interactions within a group is broad, with tails that resemble each other, but not precisely, in different contexts. By separating each interacting pair, we find that the fluctuations of the contact duration around the mean-interaction time follow however a very similar pattern. This common robust behavior is observed on 7 different datasets. It suggests that, although the set of persons we interact with and the mean-time spent together, depend strongly on the environment, our tendency to allocate more or less time than usual with a given individual is invariant, i.e. governed by some rules that lie outside the social context. Additional data reveal the same fluctuations in a baboon population. This new metric, which we call the relation “contrast”, can be used to build and test agent-based models, or as an input for describing long duration contacts in epidemiological studies.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"94 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139422522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational social science with confidence 自信的计算社会科学

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-10 DOI: 10.1140/epjds/s13688-023-00435-0

Carolina E. S. Mattsson

引用次数: 0

Public perception of generative AI on Twitter: an empirical study based on occupation and usage 推特上生成式人工智能的公众认知：基于职业和使用情况的实证研究

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-08 DOI: 10.1140/epjds/s13688-023-00445-y

Kunihiro Miyazaki, Taichi Murayama, Takayuki Uchiba, Jisun An, Haewoon Kwak

{"title":"Public perception of generative AI on Twitter: an empirical study based on occupation and usage","authors":"Kunihiro Miyazaki, Taichi Murayama, Takayuki Uchiba, Jisun An, Haewoon Kwak","doi":"10.1140/epjds/s13688-023-00445-y","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00445-y","url":null,"abstract":"The emergence of generative AI has sparked substantial discussions, with the potential to have profound impacts on society in all aspects. As emerging technologies continue to advance, it is imperative to facilitate their proper integration into society, managing expectations and fear. This paper investigates users’ perceptions of generative AI using 3M posts on Twitter from January 2019 to March 2023, especially focusing on their occupation and usage. We find that people across various occupations, not just IT-related ones, show a strong interest in generative AI. The sentiment toward generative AI is generally positive, and remarkably, their sentiments are positively correlated with their exposure to AI. Among occupations, illustrators show exceptionally negative sentiment mainly due to concerns about the unethical usage of artworks in constructing AI. People use ChatGPT in diverse ways, and notably the casual usage in which they “play with” ChatGPT tends to be associated with positive sentiments. These findings would offer valuable lessons for policymaking on the emergence of new technology and also empirical insights for the considerations of future human-AI symbiosis.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"24 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139398459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter 俄罗斯入侵乌克兰使推特上的芬兰北约讨论出现选择性失衡

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-03 DOI: 10.1140/epjds/s13688-023-00441-2

Yan Xia, Antti Gronow, Arttu Malkamäki, Tuomas Ylä-Anttila, Barbara Keller, Mikko Kivelä

{"title":"The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter","authors":"Yan Xia, Antti Gronow, Arttu Malkamäki, Tuomas Ylä-Anttila, Barbara Keller, Mikko Kivelä","doi":"10.1140/epjds/s13688-023-00441-2","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00441-2","url":null,"abstract":"It is often thought that an external threat increases the internal cohesion of a nation, and thus decreases polarization. We examine this proposition by analyzing NATO discussion dynamics on Finnish social media following the Russian invasion of Ukraine in February 2022. In Finland, public opinion on joining the North Atlantic Treaty Organization (NATO) had long been polarized along the left-right partisan axis, but the invasion led to a rapid convergence of opinion toward joining NATO. We investigate whether and how this depolarization took place among polarized actors on Finnish Twitter. By analyzing retweet patterns, we find three separate user groups before the invasion: a pro-NATO, a left-wing anti-NATO, and a conspiracy-charged anti-NATO group. After the invasion, the left-wing anti-NATO group members broke out of their retweeting bubble and connected with the pro-NATO group despite their difference in partisanship, while the conspiracy-charged anti-NATO group mostly remained a separate cluster. Our content analysis reveals that the left-wing anti-NATO group and the pro-NATO group were bridged by a shared condemnation of Russia’s actions and shared democratic norms, while the other anti-NATO group, mainly built around conspiracy theories and disinformation, consistently demonstrated a clear anti-NATO attitude. We show that an external threat can bridge partisan divides in issues linked to the threat, but bubbles upheld by conspiracy theories and disinformation may persist even under dramatic external threats.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"179 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139093092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Studying social networks in the age of computational social science 在计算社会科学时代研究社交网络

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-19 DOI: 10.1140/epjds/s13688-023-00436-z

Xinwei Xu

引用次数: 0