EPJ Data Science最新文献_第9页

Quantifying participation biases on social media 量化社交媒体上的参与偏见

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-07-28 DOI: 10.1140/epjds/s13688-023-00405-6

Neeti Pokhriyal, B. Valentino, Soroush Vosoughi

引用次数: 1

Leveraging augmentation techniques for tasks with unbalancedness within the financial domain: a two-level ensemble approach 利用增广技术处理金融领域中不平衡的任务：一种两级集成方法

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-07-10 DOI: 10.1140/epjds/s13688-023-00402-9

Golshid Ranjbaran, Diego Reforgiato Recupero, Gianfranco Lombardo, S. Consoli

引用次数: 0

Towards hypergraph cognitive networks as feature-rich models of knowledge 将超图认知网络作为特征丰富的知识模型

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-04-13 DOI: 10.1140/epjds/s13688-023-00409-2

Salvatore Citraro, S. Deyne, Massimo Stella, Giulio Rossetti

引用次数: 0

Mapping language literacy at scale: a case study on Facebook 大规模绘制语言素养地图:Facebook的案例研究

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-03-21 DOI: 10.1140/epjds/s13688-023-00388-4

Yu-Ru Lin, Shaomei Wu, Winter A. Mason

引用次数: 0

A computational analysis of accessibility, readability, and explainability of figures in open access publications 开放获取出版物中数字的可访问性、可读性和可解释性的计算分析

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-03-02 DOI: 10.1140/epjds/s13688-023-00380-y

Han Zhuang, Tzu-Yang Huang, Daniel Ernesto Acuna

引用次数: 0

Socially disruptive periods and topics from information-theoretical analysis of judicial decisions 从司法判决的信息理论分析社会分裂时期和主题

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-02-03 DOI: 10.1140/epjds/s13688-022-00376-0

Lluc Font-Pomarol, Angelo Piga, R. M. Garcia-Teruel, Sergio Nasarre-Aznar, M. Sales-Pardo, R. Guimerà

引用次数: 1

Design and analysis of tweet-based election models for the 2021 Mexican legislative election 2021年墨西哥立法选举基于推特的选举模型设计与分析

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-01-02 DOI: 10.1140/epjds/s13688-023-00401-w

Alejandro Vigna-G'omez, Javier Murillo, Manelik Ramirez, A. Borbolla, Ian M'arquez, Prasun K. Ray

引用次数: 0

The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 pandemic in Colombia, Ecuador, and El Salvador. 哥伦比亚、厄瓜多尔和萨尔瓦多在新冠肺炎大流行期间采用非药物干预措施和数字基础设施的作用。

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-01-01 Epub Date: 2023-06-06 DOI: 10.1140/epjds/s13688-023-00395-5

Nicolò Gozzi, Niccolò Comini, Nicola Perra

{"title":"The adoption of non-pharmaceutical interventions and the role of digital infrastructure during the COVID-19 pandemic in Colombia, Ecuador, and El Salvador.","authors":"Nicolò Gozzi, Niccolò Comini, Nicola Perra","doi":"10.1140/epjds/s13688-023-00395-5","DOIUrl":"10.1140/epjds/s13688-023-00395-5","url":null,"abstract":"Adherence to the non-pharmaceutical interventions (NPIs) put in place to mitigate the spreading of infectious diseases is a multifaceted problem. Several factors, including socio-demographic and socio-economic attributes, can influence the perceived susceptibility and risk which are known to affect behavior. Furthermore, the adoption of NPIs is dependent upon the barriers, real or perceived, associated with their implementation. Here, we study the determinants of NPIs adherence during the first wave of the COVID-19 Pandemic in Colombia, Ecuador, and El Salvador. Analyses are performed at the level of municipalities and include socio-economic, socio-demographic, and epidemiological indicators. Furthermore, by leveraging a unique dataset comprising tens of millions of internet Speedtest® measurements from Ookla®, we investigate the quality of the digital infrastructure as a possible barrier to adoption. We use mobility changes provided by Meta as a proxy of adherence to NPIs and find a significant correlation between mobility drops and digital infrastructure quality. The relationship remains significant after controlling for several factors. This finding suggests that municipalities with better internet connectivity were able to afford higher mobility reductions. We also find that mobility reductions were more pronounced in larger, denser, and wealthier municipalities.Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00395-5.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"18"},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10243255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9612333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

LEIA: Linguistic Embeddings for the Identification of Affect. 情感识别的语言嵌入。

IF 3 2区计算机科学

EPJ Data Science Pub Date : 2023-01-01 Epub Date: 2023-11-16 DOI: 10.1140/epjds/s13688-023-00427-0

Segun Taofeek Aroyehun, Lukas Malik, Hannah Metzler, Nikolas Haimerl, Anna Di Natale, David Garcia

{"title":"LEIA: Linguistic Embeddings for the Identification of Affect.","authors":"Segun Taofeek Aroyehun, Lukas Malik, Hannah Metzler, Nikolas Haimerl, Anna Di Natale, David Garcia","doi":"10.1140/epjds/s13688-023-00427-0","DOIUrl":"10.1140/epjds/s13688-023-00427-0","url":null,"abstract":"The wealth of text data generated by social media has enabled new kinds of analysis of emotions with language models. These models are often trained on small and costly datasets of text annotations produced by readers who guess the emotions expressed by others in social media posts. This affects the quality of emotion identification methods due to training data size limitations and noise in the production of labels used in model development. We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear. LEIA is based on a word masking method that enhances the learning of emotion words during model pre-training. LEIA achieves macro-F1 values of approximately 73 on three in-domain test datasets, outperforming other supervised and unsupervised methods in a strong benchmark that shows that LEIA generalizes across posts, users, and time periods. We further perform an out-of-domain evaluation on five different datasets of social media and other sources, showing LEIA's robust performance across media, data collection methods, and annotation schemes. Our results show that LEIA generalizes its classification of anger, happiness, and sadness beyond the domain it was trained on. LEIA can be applied in future research to provide better identification of emotions in text from the perspective of the writer.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"52"},"PeriodicalIF":3.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138458730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors. 震惊、应对和恢复力:智能手机应用程序的使用揭示了Covid-19对人类行为的封锁效应。

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2023-01-01 DOI: 10.1140/epjds/s13688-023-00391-9

Xiao Fan Liu, Zhen-Zhen Wang, Xiao-Ke Xu, Ye Wu, Zhidan Zhao, Huarong Deng, Ping Wang, Naipeng Chao, Yi-Hui C Huang

{"title":"The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors.","authors":"Xiao Fan Liu, Zhen-Zhen Wang, Xiao-Ke Xu, Ye Wu, Zhidan Zhao, Huarong Deng, Ping Wang, Naipeng Chao, Yi-Hui C Huang","doi":"10.1140/epjds/s13688-023-00391-9","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00391-9","url":null,"abstract":"Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming \"digital residents.\" This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors.Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"12 1","pages":"17"},"PeriodicalIF":3.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9947205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0