EPJ Data Science最新文献_第6页

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

Deflating the Chinese balloon: types of Twitter bots in US-China balloon incident 让中国气球 "泄气"：中美气球事件中的推特机器人类型

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-19 DOI: 10.1140/epjds/s13688-023-00440-3

Lynnette Hui Xian Ng, Kathleen M. Carley

{"title":"Deflating the Chinese balloon: types of Twitter bots in US-China balloon incident","authors":"Lynnette Hui Xian Ng, Kathleen M. Carley","doi":"10.1140/epjds/s13688-023-00440-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00440-3","url":null,"abstract":"As digitalization increases, countries employ digital diplomacy, harnessing digital resources to project their desired image. Digital diplomacy also encompasses the interactivity of digital platforms, providing a trove of public opinion that diplomatic agents can collect. Social media bots actively participate in political events through influencing political communication and purporting coordinated narratives to influence human behavior. This article provides a methodology towards identifying three types of bots: General Bots, News Bots and Bridging Bots, then further identify these classes of bots on Twitter during a diplomatic incident involving the United States and China. In the balloon incident that occurred in early 2023, where a balloon believed to have originated from China is spotted across the US airspace. Both countries have differing opinions on the function and eventual handling of the balloon. Using a series of computational methods, this article examines the impact of bots on the topics disseminated, the influence and the use of information maneuvers of bots within the social communication network. Among others, our results observe that all three types of bots are present across the two countries; bots geotagged to the US are generally concerned with the balloon location while those geotagged to China discussed topics related to escalating tensions; and perform different extent of positive narrative and network information maneuvers. The broader implications of our work towards policy making is the systematic identification of the type of bot users and their properties across country lines, enabling the evaluation of how automated agents are being deployed to disseminate narratives and the nature of narratives propagated, and therefore reflects the image that the country is being projected as on social media; as well as the perception of political issues by social media users.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138745658","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

Untangling pair synergy in the evolution of collaborative scientific impact 在合作产生科学影响的演变过程中解开配对协同作用的谜团

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-19 DOI: 10.1140/epjds/s13688-023-00439-w

Gangmin Son, Jinhyuk Yun, Hawoong Jeong

{"title":"Untangling pair synergy in the evolution of collaborative scientific impact","authors":"Gangmin Son, Jinhyuk Yun, Hawoong Jeong","doi":"10.1140/epjds/s13688-023-00439-w","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00439-w","url":null,"abstract":"Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"56 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138745846","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

Human-network regions as effective geographic units for disease mitigation 将人类网络区域作为有效缓解疾病的地理单元

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-18 DOI: 10.1140/epjds/s13688-023-00426-1

Clio Andris, Caglar Koylu, Mason A. Porter

{"title":"Human-network regions as effective geographic units for disease mitigation","authors":"Clio Andris, Caglar Koylu, Mason A. Porter","doi":"10.1140/epjds/s13688-023-00426-1","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00426-1","url":null,"abstract":"Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"36 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138717253","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

UTDRM: unsupervised method for training debunked-narrative retrieval models UTDRM：训练揭穿性叙事检索模型的无监督方法

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-13 DOI: 10.1140/epjds/s13688-023-00437-y

Iknoor Singh, Carolina Scarton, Kalina Bontcheva

引用次数: 0

Multiple gravity laws for human mobility within cities 城市内人类流动的多重重力定律

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-12-11 DOI: 10.1140/epjds/s13688-023-00438-x

Oh-Hyun Kwon, Inho Hong, Woo-Sung Jung, Hang-Hyun Jo

引用次数: 0