Pub Date : 2023-12-09DOI: 10.1007/s42001-023-00236-5
Betul Erkantarci, Gokhan Bakal
{"title":"An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms","authors":"Betul Erkantarci, Gokhan Bakal","doi":"10.1007/s42001-023-00236-5","DOIUrl":"https://doi.org/10.1007/s42001-023-00236-5","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138585535","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}
Pub Date : 2023-11-30DOI: 10.1007/s42001-023-00235-6
Valentina Rizzoli
{"title":"The risk co-de model: detecting psychosocial processes of risk perception in natural language through machine learning","authors":"Valentina Rizzoli","doi":"10.1007/s42001-023-00235-6","DOIUrl":"https://doi.org/10.1007/s42001-023-00235-6","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139204082","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}
{"title":"Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis","authors":"Raghul Gandhi Venkatesan, Dhivya Karmegam, Bagavandas Mappillairaju","doi":"10.1007/s42001-023-00231-w","DOIUrl":"https://doi.org/10.1007/s42001-023-00231-w","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139209369","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}
Pub Date : 2023-11-21DOI: 10.1007/s42001-023-00233-8
Xinyan Zhao, Chau-Wai Wong
{"title":"Automated measures of sentiment via transformer- and lexicon-based sentiment analysis (TLSA)","authors":"Xinyan Zhao, Chau-Wai Wong","doi":"10.1007/s42001-023-00233-8","DOIUrl":"https://doi.org/10.1007/s42001-023-00233-8","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251770","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}
Pub Date : 2023-11-20DOI: 10.1007/s42001-023-00234-7
Atsushi Ishida
{"title":"A fuzzy set extension of Schelling’s spatial segregation model","authors":"Atsushi Ishida","doi":"10.1007/s42001-023-00234-7","DOIUrl":"https://doi.org/10.1007/s42001-023-00234-7","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259627","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}
Pub Date : 2023-11-19DOI: 10.1007/s42001-023-00230-x
Giacomo di Tollo, Joseph Andria, S. Tanev, Sara Ghilardi
{"title":"Integrating the gender dimension to disclose the degree of businesses’ articulation of innovation","authors":"Giacomo di Tollo, Joseph Andria, S. Tanev, Sara Ghilardi","doi":"10.1007/s42001-023-00230-x","DOIUrl":"https://doi.org/10.1007/s42001-023-00230-x","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139260094","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}
Pub Date : 2023-11-14DOI: 10.1007/s42001-023-00229-4
Anwesha Sengupta, Shashankaditya Upadhyay, Indranil Mukherjee, Prasanta K. Panigrahi
{"title":"A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective","authors":"Anwesha Sengupta, Shashankaditya Upadhyay, Indranil Mukherjee, Prasanta K. Panigrahi","doi":"10.1007/s42001-023-00229-4","DOIUrl":"https://doi.org/10.1007/s42001-023-00229-4","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991829","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}
Pub Date : 2023-10-28DOI: 10.1007/s42001-023-00228-5
Yasemin Lheureux
{"title":"Predictive insights: leveraging Twitter sentiments and machine learning for environmental, social and governance controversy prediction","authors":"Yasemin Lheureux","doi":"10.1007/s42001-023-00228-5","DOIUrl":"https://doi.org/10.1007/s42001-023-00228-5","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136160463","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}
Pub Date : 2023-10-26DOI: 10.1007/s42001-023-00223-w
Dongwoo Lim, Fujio Toriumi, Mitsuo Yoshida, Mikihito Tanaka, Kunhao Yang
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-10-25DOI: 10.1007/s42001-023-00227-6
Samantha C. Phillips, Joshua Uyheng, Kathleen M. Carley
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.
{"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":null,"pages":null},"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}
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