Journal of Computational Social Science最新文献_第2页

A fuzzy set extension of Schelling’s spatial segregation model 谢林空间隔离模型的模糊集扩展

IF 3.2

Journal of Computational Social Science Pub Date : 2023-11-20 DOI: 10.1007/s42001-023-00234-7

Atsushi Ishida

引用次数: 0

Integrating the gender dimension to disclose the degree of businesses’ articulation of innovation 整合性别维度，披露企业的创新衔接程度

IF 3.2

Journal of Computational Social Science Pub Date : 2023-11-19 DOI: 10.1007/s42001-023-00230-x

Giacomo di Tollo, Joseph Andria, S. Tanev, Sara Ghilardi

引用次数: 0

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective 有影响力的传播者对印度市场和少数外国市场不同部门的影响研究:复杂网络视角

Journal of Computational Social Science Pub Date : 2023-11-14 DOI: 10.1007/s42001-023-00229-4

Anwesha Sengupta, Shashankaditya Upadhyay, Indranil Mukherjee, Prasanta K. Panigrahi

引用次数: 0

Predictive insights: leveraging Twitter sentiments and machine learning for environmental, social and governance controversy prediction 预测洞察:利用Twitter情绪和机器学习进行环境、社会和治理争议预测

Journal of Computational Social Science Pub Date : 2023-10-28 DOI: 10.1007/s42001-023-00228-5

Yasemin Lheureux

引用次数: 0

The variant of efforts avoiding strain: successful correction of a scientific discourse related to COVID-19 努力避免紧张的变体:成功纠正与COVID-19相关的科学论述

Journal of Computational Social Science Pub Date : 2023-10-26 DOI: 10.1007/s42001-023-00223-w

Dongwoo Lim, Fujio Toriumi, Mitsuo Yoshida, Mikihito Tanaka, Kunhao Yang

{"title":"The variant of efforts avoiding strain: successful correction of a scientific discourse related to COVID-19","authors":"Dongwoo Lim, Fujio Toriumi, Mitsuo Yoshida, Mikihito Tanaka, Kunhao Yang","doi":"10.1007/s42001-023-00223-w","DOIUrl":"https://doi.org/10.1007/s42001-023-00223-w","url":null,"abstract":"Abstract This study focuses on how scientifically accurate information is disseminated through social media, and how misinformation can be corrected. We have identified examples on Twitter where scientific terms that have been widely misused have been rectified and replaced by scientifically accurate terms through the interaction of users. The results show that the percentage of accurate terms (“variant” or “COVID-19 variant”) being used instead of the inaccurate terms (“strain”) on Twitter has already increased since the end of December 2020. This was about a month before the release of an official statement by the Japanese Association for Infectious Diseases regarding the accurate terminology, and the use of terms on social media was faster than it was in television. Some Twitter users who quickly started using the accurate term were more likely to retweet messages sent by leading influencers on Twitter, rather than messages sent by traditional media or portal sites. However, a few Twitter users continued to use wrong terms even after March 2021, even though the use of the accurate terms was widespread. This study empirically verified that self-correction occurs even on Twitter, and also suggested that influencers with expertise can influence the direction of public opinion on social media.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A high-dimensional approach to measuring online polarization 测量在线极化的高维方法

Journal of Computational Social Science Pub Date : 2023-10-25 DOI: 10.1007/s42001-023-00227-6

Samantha C. Phillips, Joshua Uyheng, Kathleen M. Carley

{"title":"A high-dimensional approach to measuring online polarization","authors":"Samantha C. Phillips, Joshua Uyheng, Kathleen M. Carley","doi":"10.1007/s42001-023-00227-6","DOIUrl":"https://doi.org/10.1007/s42001-023-00227-6","url":null,"abstract":"Abstract Polarization, ideological and psychological distancing between groups, can cause dire societal fragmentation. Of chief concern is the role of social media in enhancing polarization through mechanisms like facilitating selective exposure to information. Researchers using user-generated content to measure polarization typically focus on direct communication, suggesting echo chamber-like communities indicate the most polarization. However, this operationalization does not account for other dimensions of intergroup conflict that have been associated with polarization. We address this limitation by introducing a high-dimensional network framework to evaluate polarization based on three dimensions: social, knowledge, and knowledge source. Following an extensive review of the psychological and social mechanisms of polarization, we specify five sufficient conditions for polarization to occur that can be evaluated using our approach. We analyze six existing network-based polarization metrics in our high-dimensional network framework through a virtual experiment and apply our proposed methodology to discussions around COVID-19 vaccines on Twitter. This work has implications for detecting polarization on social media using user-generated content, quantifying the effects of offline divides or de-polarization efforts online, and comparing community dynamics across contexts.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"46 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134971813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transfer learning for hate speech detection in social media 基于迁移学习的社交媒体仇恨言论检测

Journal of Computational Social Science Pub Date : 2023-10-17 DOI: 10.1007/s42001-023-00224-9

Marian-Andrei Rizoiu, Tianyu Wang, Gabriela Ferraro, Hanna Suominen

{"title":"Transfer learning for hate speech detection in social media","authors":"Marian-Andrei Rizoiu, Tianyu Wang, Gabriela Ferraro, Hanna Suominen","doi":"10.1007/s42001-023-00224-9","DOIUrl":"https://doi.org/10.1007/s42001-023-00224-9","url":null,"abstract":"Abstract Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information increase exposure to harmful content, such as cyber-bullying and cyber-hatred. Models based on machine learning and natural language offer a way to make online platforms safer by identifying hate speech in web text autonomously. However, the main difficulty is annotating a sufficiently large number of examples to train these models. This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech. We build an interpretable two-dimensional visualization tool of the constructed hate speech representation—dubbed the Map of Hate—in which multiple datasets can be projected and comparatively analyzed. The hateful content is annotated differently across the two datasets (racist and sexist in one dataset, hateful and offensive in another). However, the common representation successfully projects the harmless class of both datasets into the same space and can be used to uncover labeling errors (false positives). We also show that the joint representation boosts prediction performances when only a limited amount of supervision is available. These methods and insights hold the potential for safer social media and reduce the need to expose human moderators and annotators to distressing online messaging.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Modeling economic migration on a global scale 在全球范围内模拟经济移民

Journal of Computational Social Science Pub Date : 2023-10-09 DOI: 10.1007/s42001-023-00226-7

Eva Dziadula, John O’Hare, Carl Colglazier, Marie C. Clay, Paul Brenner

引用次数: 0

Bridging the offline and online: 20 years of offline meeting data of the German-language Wikipedia 架起线下和线上的桥梁:德语维基百科20年的线下会议数据

Journal of Computational Social Science Pub Date : 2023-09-26 DOI: 10.1007/s42001-023-00225-8

Nicole Schwitter

{"title":"Bridging the offline and online: 20 years of offline meeting data of the German-language Wikipedia","authors":"Nicole Schwitter","doi":"10.1007/s42001-023-00225-8","DOIUrl":"https://doi.org/10.1007/s42001-023-00225-8","url":null,"abstract":"Abstract Wikipedia is one of the most visited websites worldwide. Thousands of volunteers are contributing to it daily, making it an example of how productive non-market collaboration on a very wide scale is not only viable but also sustainable. Wikipedia’s freely available data on the online actions conducted make it a popular source of data, particularly for computer scientists and computational social scientists. This data brief will present the dewiki meetup dataset which covers the offline component of the German-language version of the online encyclopaedia Wikipedia: informal offline gatherings between Wikipedia contributors. These gatherings are organised online and information about who is attending them, where they take place and what has happened at these meetings is shared publicly. The dewiki meetup dataset covers almost 20 years of offline activity of the German-language Wikipedia, containing 4418 meetups that have been organised with information on attendees, apologies, date and place of meeting, and minutes recorded. It is a valuable source of data for social science research: it captures the development of the offline network over time of one of the largest and most sustainable online public goods and communities. The data can easily be merged with online activity data on Wikipedia which allows us to bridge the gap between offline and online behaviour.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134961127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

It’s my turn: empirical evidence of upstream indirect reciprocity in society through a quasi-experimental approach 轮到我了:通过准实验方法，社会上游间接互惠的经验证据

IF 3.2

Journal of Computational Social Science Pub Date : 2023-09-07 DOI: 10.1007/s42001-023-00221-y

Shinya Obayashi, Misato Inaba, Tetsushi Ohdaira, T. Kiyonari

引用次数: 0