Journal of Computational Social Science最新文献_第10页

Deep diving into the S&P Europe 350 index network and its reaction to COVID-19. 深入了解标准普尔欧洲350指数网络及其对COVID-19的反应。

IF 3.2

Journal of Computational Social Science Pub Date : 2022-01-01 Epub Date: 2022-06-28 DOI: 10.1007/s42001-022-00172-w

Ariana Paola Cortés Ángel, Mustafa Hakan Eratalay

{"title":"Deep diving into the S&P Europe 350 index network and its reaction to COVID-19.","authors":"Ariana Paola Cortés Ángel, Mustafa Hakan Eratalay","doi":"10.1007/s42001-022-00172-w","DOIUrl":"https://doi.org/10.1007/s42001-022-00172-w","url":null,"abstract":"In this paper, we analyse the dynamic partial correlation network of the constituent stocks of S&P Europe 350. We focus on global parameters such as radius, which is rarely used in financial networks literature, and also the diameter and distance parameters. The first two parameters are useful for deducing the force that economic instability should exert to trigger a cascade effect on the network. With these global parameters, we hone the boundaries of the strength that a shock should exert to trigger a cascade effect. In addition, we analysed the homophilic profiles, which is quite new in financial networks literature. We found highly homophilic relationships among companies, considering firms by country and industry. We also calculate the local parameters such as degree, closeness, betweenness, eigenvector, and harmonic centralities to gauge the importance of the companies regarding different aspects, such as the strength of the relationships with their neighbourhood and their location in the network. Finally, we analysed a network substructure by introducing the skeleton concept of a dynamic network. This subnetwork allowed us to study the stability of relations among constituents and detect a significant increase in these stable connections during the Covid-19 pandemic.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"1343-1408"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40564507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Characterizing the roles of bots on Twitter during the COVID-19 infodemic. 在COVID-19信息大流行期间，Twitter上机器人的角色特征

IF 3.2

Journal of Computational Social Science Pub Date : 2022-01-01 Epub Date: 2021-08-30 DOI: 10.1007/s42001-021-00139-3

Wentao Xu, Kazutoshi Sasahara

{"title":"Characterizing the roles of bots on Twitter during the COVID-19 infodemic.","authors":"Wentao Xu, Kazutoshi Sasahara","doi":"10.1007/s42001-021-00139-3","DOIUrl":"https://doi.org/10.1007/s42001-021-00139-3","url":null,"abstract":"An infodemic is an emerging phenomenon caused by an overabundance of information online. This proliferation of information makes it difficult for the public to distinguish trustworthy news and credible information from untrustworthy sites and non-credible sources. The perils of an infodemic debuted with the outbreak of the COVID-19 pandemic and bots (i.e., automated accounts controlled by a set of algorithms) that are suspected of spreading the infodemic. Although previous research has revealed that bots played a central role in spreading misinformation during major political events, how bots behavior during the infodemic is unclear. In this paper, we examined the roles of bots in the case of the COVID-19 infodemic and the diffusion of non-credible information such as \"5G\" and \"Bill Gates\" conspiracy theories and content related to \"Trump\" and \"WHO\" by analyzing retweet networks and retweeted items. We show the segregated topology of their retweet networks, which indicates that right-wing self-media accounts and conspiracy theorists may lead to this opinion cleavage, while malicious bots might favor amplification of the diffusion of non-credible information. Although the basic influence of information diffusion could be larger in human users than bots, the effects of bots are non-negligible under an infodemic situation.Supplementary information: The online version contains supplementary material available at 10.1007/s42001-021-00139-3.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"591-609"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39386626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic. 与COVID-19大流行相关的推特帖子的高分辨率时间和地理空间内容分析。

IF 3.2

Journal of Computational Social Science Pub Date : 2022-01-01 Epub Date: 2021-10-20 DOI: 10.1007/s42001-021-00150-8

Charalampos Ntompras, George Drosatos, Eleni Kaldoudi

{"title":"A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.","authors":"Charalampos Ntompras, George Drosatos, Eleni Kaldoudi","doi":"10.1007/s42001-021-00150-8","DOIUrl":"https://doi.org/10.1007/s42001-021-00150-8","url":null,"abstract":"The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"687-729"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39557960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Comparative analysis of social bots and humans during the COVID-19 pandemic. COVID-19大流行期间社交机器人和人类的比较分析。

IF 3.2

Journal of Computational Social Science Pub Date : 2022-01-01 Epub Date: 2022-06-30 DOI: 10.1007/s42001-022-00173-9

Ho-Chun Herbert Chang, Emilio Ferrara

引用次数: 12

How he won: Using machine learning to understand Trump’s 2016 victory 他是如何获胜的:利用机器学习来理解特朗普2016年的胜利

IF 3.2

Journal of Computational Social Science Pub Date : 2021-12-28 DOI: 10.1007/s42001-021-00147-3

Zhaochen He, J. Camobreco, K. Perkins

引用次数: 0

OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment OCR与Tesseract、Amazon text和Google Document AI:一个基准实验

IF 3.2

Journal of Computational Social Science Pub Date : 2021-11-22 DOI: 10.1007/s42001-021-00149-1

Thomas Hegghammer

引用次数: 9

Determining political interests of issue-motivated groups on social media: joint topic models for issues, sentiment and stance 确定社交媒体上议题驱动群体的政治利益:议题、情绪和立场的联合话题模型

IF 3.2

Journal of Computational Social Science Pub Date : 2021-11-12 DOI: 10.1007/s42001-021-00146-4

Sandeepa Kannangara, W. Wobcke

引用次数: 1

What race and gender stand for: using Markov blankets to identify constitutive and mediating relationships 种族和性别代表什么:使用马尔可夫毯子来识别构成和中介关系

IF 3.2

Journal of Computational Social Science Pub Date : 2021-10-30 DOI: 10.1007/s42001-021-00152-6

Rafael Quintana

引用次数: 6

Text-based automatic personality prediction: a bibliographic review 基于文本的自动人格预测:参考文献综述

IF 3.2

Journal of Computational Social Science Pub Date : 2021-10-04 DOI: 10.1007/s42001-022-00178-4

Ali Reza Feizi Derakhshi, M. Feizi-Derakhshi, Majid Ramezani, N. Nikzad-Khasmakhi, M. Asgari-Chenaghlu, Taymaz Akan, Mehrdad Ranjbar-Khadivi, Elnaz Zafarni-Moattar, Zoleikha Jahanbakhsh-Naghadeh

引用次数: 7

Geographies of Twitter debates 推特辩论的地理分布

IF 3.2

Journal of Computational Social Science Pub Date : 2021-09-12 DOI: 10.1007/s42001-021-00143-7

E. Gobbo, Lara Fontanella, Sara Fontanella, A. Sarra

引用次数: 2