EPJ Data Science最新文献_第3页

Online advertisement in a pink-colored market 粉色市场中的在线广告

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-05-08 DOI: 10.1140/epjds/s13688-024-00473-2

Amir Mehrjoo, Rubén Cuevas, Ángel Cuevas

{"title":"Online advertisement in a pink-colored market","authors":"Amir Mehrjoo, Rubén Cuevas, Ángel Cuevas","doi":"10.1140/epjds/s13688-024-00473-2","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00473-2","url":null,"abstract":"It is surprising that women are often charged more for products and services marketed explicitly to them. This phenomenon, known as the pink tax, is a major issue that questions women’s buying power. Nevertheless, it is not just limited to physical products – even online advertising can be subject to this type of gender-price discrimination. That is where our research comes in. We have developed a new methodology to measure what we call the digital marketing pink tax – the additional expense of delivering advertisements to female audiences. Analyzing data from Facebook advertising platforms across 187 countries and 40 territories shows this issue is systematic. Particularly, the digital marketing pink tax is prevalent in 79% of audiences across the world and 98% of audiences in highly developed countries. Therefore, advertisers incur a median cost of 30% more to display advertisements to women than men. In contrast, advertisers have to pay less digital marketing pink tax in less-developed countries (5%). Our research indicates that countries in the Middle East and Africa with a low Human Development Index (HDI) do not experience this phenomenon. Our comprehensive investigation of 24 industries reveals that advertisers must pay up to 64% of the digital marketing pink tax to target women in some industries. Our findings also suggest a connection between the digital marketing pink tax and the consumer pink tax – the extra charge placed on products marketed to women. Overall, our research sheds light on an important issue affecting women worldwide. Raising awareness of the digital marketing pink tax and advocating for better regulation.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"59 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140925708","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

Who makes open source code? The hybridisation of commercial and open source practices 谁在编写开放源代码？商业和开源实践的混合

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-05-06 DOI: 10.1140/epjds/s13688-024-00475-0

Peter Mehler, Eva Iris Otto, Anna Sapienza

{"title":"Who makes open source code? The hybridisation of commercial and open source practices","authors":"Peter Mehler, Eva Iris Otto, Anna Sapienza","doi":"10.1140/epjds/s13688-024-00475-0","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00475-0","url":null,"abstract":"While Free and Open Source (F/OSS) coding has traditionally been described as a separate commons linked to values of openness and sharing, recent research suggests an increasing integration of private corporations into F/OSS practices, blurring the boundaries between F/OSS and commodified coding. However, there is a dearth of empirical, and especially quantitative studies exploring this phenomenon. To address this gap, we model the power dynamics and infrastructural aspects of software production within GitHub, a central hub for F/OSS development, using a large-scale, directed network. Using various network statistics, we detect the ecosystem’s most impactful actors and find a nuanced picture of the influence of individuals, open source organizations, and private corporations in F/OSS practices. We find that the majority of public repositories on GitHub depend on a small core of specialized repositories and users. In accordance with expectations, individuals and open source organizations are more prevalent in this core of elite GitHub users, however, we also find a significant amount of private organizations with an indirect, yet consistent influence within GitHub. In addition, we find that directly influential individuals tend to facilitate sponsorship methods more often than indirectly or non-influential individuals. Our research highlights a hybridization of F/OSS and sheds light on the complex interplay between influence, power, and code production in the multi-language dependency ecosystem of GitHub.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"61 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140883269","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

Segmentation using large language models: A new typology of American neighborhoods 使用大型语言模型进行分类：美国社区的新类型

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-22 DOI: 10.1140/epjds/s13688-024-00466-1

Alex D. Singleton, Seth Spielman

引用次数: 0

Early career wins and tournament prestige characterize tennis players’ trajectories 职业生涯早期的胜利和赛事声望是网球运动员发展轨迹的特征

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-19 DOI: 10.1140/epjds/s13688-024-00472-3

Chiara Zappalà, Sandro Sousa, Tiago Cunha, Alessandro Pluchino, Andrea Rapisarda, Roberta Sinatra

引用次数: 0

Multifaceted online coordinated behavior in the 2020 US presidential election 2020 年美国总统大选中的多方面在线协调行为

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-19 DOI: 10.1140/epjds/s13688-024-00467-0

Serena Tardelli, Leonardo Nizzoli, Marco Avvenuti, Stefano Cresci, Maurizio Tesconi

{"title":"Multifaceted online coordinated behavior in the 2020 US presidential election","authors":"Serena Tardelli, Leonardo Nizzoli, Marco Avvenuti, Stefano Cresci, Maurizio Tesconi","doi":"10.1140/epjds/s13688-024-00467-0","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00467-0","url":null,"abstract":"Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet, the ways in which coordinated behavior surfaces during major online political debates is still largely unclear. This study sheds light on coordinated behaviors that took place on Twitter (now X) during the 2020 US Presidential Election. Utilizing state-of-the-art network science methods, we detect and characterize the coordinated communities that participated in the debate. Our approach goes beyond previous analyses by proposing a multifaceted characterization of the coordinated communities that allows obtaining nuanced results. In particular, we uncover three main categories of coordinated users: (i) moderate groups genuinely interested in the electoral debate, (ii) conspiratorial groups that spread false information and divisive narratives, and (iii) foreign influence networks that either sought to tamper with the debate or that exploited it to publicize their own agendas. We also reveal a large use of automation by far-right foreign influence and conspiratorial communities. Conversely, left-leaning supporters were overall less coordinated and engaged primarily in harmless, factual communication. Our results also showed that Twitter was effective at thwarting the activity of some coordinated groups, while it failed on some other equally suspicious ones. Overall, this study advances the understanding of online human interactions and contributes new knowledge to mitigate cyber social threats.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"48 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623004","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

Developing a hierarchical model for unraveling conspiracy theories 建立揭示阴谋论的分层模型

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-16 DOI: 10.1140/epjds/s13688-024-00470-5

Mohsen Ghasemizade, Jeremiah Onaolapo

{"title":"Developing a hierarchical model for unraveling conspiracy theories","authors":"Mohsen Ghasemizade, Jeremiah Onaolapo","doi":"10.1140/epjds/s13688-024-00470-5","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00470-5","url":null,"abstract":"A conspiracy theory (CT) suggests covert groups or powerful individuals secretly manipulate events. Not knowing about existing conspiracy theories could make one more likely to believe them, so this work aims to compile a list of CTs shaped as a tree that is as comprehensive as possible. We began with a manually curated ‘tree’ of CTs from academic papers and Wikipedia. Next, we examined 1769 CT-related articles from four fact-checking websites, focusing on their core content, and used a technique called Keyphrase Extraction to label the documents. This process yielded 769 identified conspiracies, each assigned a label and a family name. The second goal of this project was to detect whether an article is a conspiracy theory, so we built a binary classifier with our labeled dataset. This model uses a transformer-based machine learning technique and is pre-trained on a large corpus called RoBERTa, resulting in an F1 score of 87%. This model helps to identify potential conspiracy theories in new articles. We used a combination of clustering (HDBSCAN) and a dimension reduction technique (UMAP) to assign a label from the tree to these new articles detected as conspiracy theories. We then labeled these groups accordingly to help us match them to the tree. These can lead us to detect new conspiracy theories and expand the tree using computational methods. We successfully generated a tree of conspiracy theories and built a pipeline to detect and categorize conspiracy theories within any text corpora. This pipeline gives us valuable insights through any databases formatted as text.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140610910","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

Scaling law of real traffic jams under varying travel demand 不同出行需求下实际交通拥堵的缩放规律

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-11 DOI: 10.1140/epjds/s13688-024-00471-4

Rui Chen, Yuming Lin, Huan Yan, Jiazhen Liu, Yu Liu, Yong Li

{"title":"Scaling law of real traffic jams under varying travel demand","authors":"Rui Chen, Yuming Lin, Huan Yan, Jiazhen Liu, Yu Liu, Yong Li","doi":"10.1140/epjds/s13688-024-00471-4","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00471-4","url":null,"abstract":"The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"38 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563982","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

Suspended accounts align with the Internet Research Agency misinformation campaign to influence the 2016 US election 被暂停的账户与 "互联网研究机构 "影响 2016 年美国大选的虚假信息活动一致

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-10 DOI: 10.1140/epjds/s13688-024-00464-3

Matteo Serafino, Zhenkun Zhou, José S. Andrade, Alexandre Bovet, Hernán A. Makse

引用次数: 0

Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks 揭开沉默的大多数的面纱：利用协同过滤和图卷积网络对社交媒体上的被动用户进行立场检测和特征描述

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-04 DOI: 10.1140/epjds/s13688-024-00469-y

{"title":"Unveiling the silent majority: stance detection and characterization of passive users on social media using collaborative filtering and graph convolutional networks","authors":"","doi":"10.1140/epjds/s13688-024-00469-y","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00469-y","url":null,"abstract":"<h3>Abstract</h3> Social Media (SM) has become a popular medium for individuals to share their opinions on various topics, including politics, social issues, and daily affairs. During controversial events such as political elections, active users often proclaim their stance and try to persuade others to support them. However, disparities in participation levels can lead to misperceptions and cause analysts to misjudge the support for each side. For example, current models usually rely on content production and overlook a vast majority of civically engaged users who passively consume information. These “silent users” can significantly impact the democratic process despite being less vocal. Accounting for the stances of this silent majority is critical to improving our reliance on SM to understand and measure social phenomena. Thus, this study proposes and evaluates a new approach for silent users’ stance prediction based on collaborative filtering and Graph Convolutional Networks, which exploits multiple relationships between users and topics. Furthermore, our method allows us to describe users with different stances and online behaviors. We demonstrate its validity using real-world datasets from two related political events. Specifically, we examine user attitudes leading to the Chilean constitutional referendums in 2020 and 2022 through extensive Twitter datasets. In both datasets, our model outperforms the baselines by over 9% at the edge- and the user level. Thus, our method offers an improvement in effectively quantifying the support and creating a multidimensional understanding of social discussions on SM platforms, especially during polarizing events.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"32 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563977","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

Science as exploration in a knowledge landscape: tracing hotspots or seeking opportunity? 科学是知识景观中的探索：追踪热点还是寻找机会？

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-04-02 DOI: 10.1140/epjds/s13688-024-00468-z

{"title":"Science as exploration in a knowledge landscape: tracing hotspots or seeking opportunity?","authors":"","doi":"10.1140/epjds/s13688-024-00468-z","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00468-z","url":null,"abstract":"<h3>Abstract</h3> The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in Geographic Information System (GIS) techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists’ topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"2013 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564049","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