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":"164 5","pages":""},"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":"11 2","pages":""},"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":"3 1","pages":""},"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":"1 3","pages":"0"},"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":"13 1","pages":"0"},"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":"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}
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":"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}
Pub Date : 2023-10-17DOI: 10.1007/s42001-023-00224-9
Marian-Andrei Rizoiu, Tianyu Wang, Gabriela Ferraro, Hanna Suominen
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.
{"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}
Pub Date : 2023-10-09DOI: 10.1007/s42001-023-00226-7
Eva Dziadula, John O’Hare, Carl Colglazier, Marie C. Clay, Paul Brenner
{"title":"Modeling economic migration on a global scale","authors":"Eva Dziadula, John O’Hare, Carl Colglazier, Marie C. Clay, Paul Brenner","doi":"10.1007/s42001-023-00226-7","DOIUrl":"https://doi.org/10.1007/s42001-023-00226-7","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135045730","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 : 2023-09-26DOI: 10.1007/s42001-023-00225-8
Nicole Schwitter
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.
{"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}
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