Pub Date : 2023-07-07DOI: 10.1007/s42001-023-00217-8
Daryl R. DeFord, Elliot Kimsey, R. Zerr
{"title":"Multi-balanced redistricting","authors":"Daryl R. DeFord, Elliot Kimsey, R. Zerr","doi":"10.1007/s42001-023-00217-8","DOIUrl":"https://doi.org/10.1007/s42001-023-00217-8","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"85 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76805653","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 : 2023-06-30DOI: 10.1007/s42001-023-00216-9
Thomas Feliciani, J. Tolsma, A. Flache
{"title":"Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation","authors":"Thomas Feliciani, J. Tolsma, A. Flache","doi":"10.1007/s42001-023-00216-9","DOIUrl":"https://doi.org/10.1007/s42001-023-00216-9","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"9 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79762786","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 : 2023-06-27DOI: 10.1007/s42001-023-00213-y
Jason A. Williams, Ahmed Aleroud, Danielle Zimmerman
{"title":"Detecting science-based health disinformation: a stylometric machine learning approach","authors":"Jason A. Williams, Ahmed Aleroud, Danielle Zimmerman","doi":"10.1007/s42001-023-00213-y","DOIUrl":"https://doi.org/10.1007/s42001-023-00213-y","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"110 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79048414","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 : 2023-06-21DOI: 10.1007/s42001-023-00212-z
Domonkos Sik, Márton Rakovics, J. Buda, R. Németh
{"title":"The impact of depression forums on illness narratives: a comprehensive NLP analysis of socialization in e-mental health communities","authors":"Domonkos Sik, Márton Rakovics, J. Buda, R. Németh","doi":"10.1007/s42001-023-00212-z","DOIUrl":"https://doi.org/10.1007/s42001-023-00212-z","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"70 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90245467","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 : 2023-06-08DOI: 10.1007/s42001-023-00209-8
Danaja Maldeniya, M. de Choudhury, David Garcia, Daniel M. Romero
{"title":"Pulling through together: social media response trajectories in disaster-stricken communities","authors":"Danaja Maldeniya, M. de Choudhury, David Garcia, Daniel M. Romero","doi":"10.1007/s42001-023-00209-8","DOIUrl":"https://doi.org/10.1007/s42001-023-00209-8","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"81 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73543420","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 : 2023-06-08DOI: 10.1007/s42001-023-00211-0
Christelle Khalaf, G. Michaud, G. J. Jolley
{"title":"Predicting declining and growing occupations using supervised machine learning","authors":"Christelle Khalaf, G. Michaud, G. J. Jolley","doi":"10.1007/s42001-023-00211-0","DOIUrl":"https://doi.org/10.1007/s42001-023-00211-0","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"90 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76679764","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 : 2023-05-15DOI: 10.1007/s42001-023-00206-x
Louis Magowan
The COVID-19 pandemic meant that, in 2020, students in England were unable to sit their examinations and instead received predicted grades, or "centre assessment grades" (CAGs), from their teachers to allow them to progress. Using the Grading and Admissions Data for England (GRADE) dataset for students from 2018 to 2020, this study treats the use of CAGs as a natural experiment for causally understanding how teacher judgements of academic ability may be biased according to the demographic and socio-economic characteristics of their students. A variety of machine learning models were trained on the 2018-19 data and then used to generate predictions for what the 2020 students were likely to have received had their examinations taken place as usual. The differences between these predictions and the CAGs that students received were calculated and then averaged across students' different characteristics, revealing what the treatment effects of the use of CAGs were likely to have been for different types of students. No evidence of absolute negative bias against students of any demographic or socio-economic characteristic was found, with all groups of students having received higher CAGs than the grades they were likely to have received had they sat their examinations. Some evidence for relative bias was found, with consistent, but insubstantial differences being observed in the treatment effects of certain groups. However, when higher-order interactions of student characteristics were considered, these differences became more substantial. Intersectional perspectives which emphasise interactions and sub-group differences should be used more widely within quantitative educational equalities research.
{"title":"Centre assessment grades in 2020: a natural experiment for investigating bias in teacher judgements.","authors":"Louis Magowan","doi":"10.1007/s42001-023-00206-x","DOIUrl":"10.1007/s42001-023-00206-x","url":null,"abstract":"<p><p>The COVID-19 pandemic meant that, in 2020, students in England were unable to sit their examinations and instead received predicted grades, or \"centre assessment grades\" (CAGs), from their teachers to allow them to progress. Using the Grading and Admissions Data for England (GRADE) dataset for students from 2018 to 2020, this study treats the use of CAGs as a natural experiment for causally understanding how teacher judgements of academic ability may be biased according to the demographic and socio-economic characteristics of their students. A variety of machine learning models were trained on the 2018-19 data and then used to generate predictions for what the 2020 students were likely to have received had their examinations taken place as usual. The differences between these predictions and the CAGs that students received were calculated and then averaged across students' different characteristics, revealing what the treatment effects of the use of CAGs were likely to have been for different types of students. No evidence of absolute negative bias against students of any demographic or socio-economic characteristic was found, with all groups of students having received higher CAGs than the grades they were likely to have received had they sat their examinations. Some evidence for relative bias was found, with consistent, but insubstantial differences being observed in the treatment effects of certain groups. However, when higher-order interactions of student characteristics were considered, these differences became more substantial. Intersectional perspectives which emphasise interactions and sub-group differences should be used more widely within quantitative educational equalities research.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"1-45"},"PeriodicalIF":3.2,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9709243","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}
Pub Date : 2023-05-05DOI: 10.1007/s42001-023-00199-7
Manfred Stede, Yannic Bracke, Luka Borec, Neele Charlotte Kinkel, Maria Skeppstedt
{"title":"Framing climate change in Nature and Science editorials: applications of supervised and unsupervised text categorization","authors":"Manfred Stede, Yannic Bracke, Luka Borec, Neele Charlotte Kinkel, Maria Skeppstedt","doi":"10.1007/s42001-023-00199-7","DOIUrl":"https://doi.org/10.1007/s42001-023-00199-7","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"12 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79435014","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 : 2023-05-03DOI: 10.1007/s42001-023-00208-9
Aleksandra Urman, Mykola Makhortykh
In this article, we conduct a comparative analysis of web search behaviors in Switzerland and Germany. For this aim, we rely on a combination of web tracking data and survey data collected over a period of 2 months from users in Germany (n = 558) and Switzerland (n = 563). We find that web search accounts for 13% of all desktop browsing, with the share being higher in Switzerland than in Germany. In over 50% of cases users clicked on the first search result, with over 97% of all clicks being made on the first page of search outputs. Most users rely on Google when conducting searches, with some differences observed in users' preferences for other engines across demographic groups. Further, we observe differences in the temporal patterns of web search use between women and men, marking the necessity of disaggregating data by gender in observational studies regarding online information seeking behaviors. Our findings highlight the contextual differences in web search behavior across countries and demographic groups that should be taken into account when examining search behavior and the potential effects of web search result quality on societies and individuals.
{"title":"You are how (and where) you search? Comparative analysis of web search behavior using web tracking data.","authors":"Aleksandra Urman, Mykola Makhortykh","doi":"10.1007/s42001-023-00208-9","DOIUrl":"10.1007/s42001-023-00208-9","url":null,"abstract":"<p><p>In this article, we conduct a comparative analysis of web search behaviors in Switzerland and Germany. For this aim, we rely on a combination of web tracking data and survey data collected over a period of 2 months from users in Germany (<i>n</i> = 558) and Switzerland (<i>n</i> = 563). We find that web search accounts for 13% of all desktop browsing, with the share being higher in Switzerland than in Germany. In over 50% of cases users clicked on the first search result, with over 97% of all clicks being made on the first page of search outputs. Most users rely on Google when conducting searches, with some differences observed in users' preferences for other engines across demographic groups. Further, we observe differences in the temporal patterns of web search use between women and men, marking the necessity of disaggregating data by gender in observational studies regarding online information seeking behaviors. Our findings highlight the contextual differences in web search behavior across countries and demographic groups that should be taken into account when examining search behavior and the potential effects of web search result quality on societies and individuals.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"1-16"},"PeriodicalIF":3.2,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10155157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717505","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}
Pub Date : 2023-04-11DOI: 10.1007/s42001-023-00204-z
Hyunsun Kim-Hahm
{"title":"Computational approach to studying media coverage of organizations","authors":"Hyunsun Kim-Hahm","doi":"10.1007/s42001-023-00204-z","DOIUrl":"https://doi.org/10.1007/s42001-023-00204-z","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"55 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88396211","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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