Journal of Computational Social Science最新文献_第9页

Detecting science-based health disinformation: a stylometric machine learning approach 检测基于科学的健康虚假信息:一种风格测量机器学习方法

IF 3.2

Journal of Computational Social Science Pub Date : 2023-06-27 DOI: 10.1007/s42001-023-00213-y

Jason A. Williams, Ahmed Aleroud, Danielle Zimmerman

引用次数: 0

The impact of depression forums on illness narratives: a comprehensive NLP analysis of socialization in e-mental health communities 抑郁症论坛对疾病叙述的影响:电子心理健康社区社会化的综合NLP分析

IF 3.2

Journal of Computational Social Science Pub Date : 2023-06-21 DOI: 10.1007/s42001-023-00212-z

Domonkos Sik, Márton Rakovics, J. Buda, R. Németh

引用次数: 0

Pulling through together: social media response trajectories in disaster-stricken communities 齐心协力:受灾社区的社会媒体反应轨迹

IF 3.2

Journal of Computational Social Science Pub Date : 2023-06-08 DOI: 10.1007/s42001-023-00209-8

Danaja Maldeniya, M. de Choudhury, David Garcia, Daniel M. Romero

引用次数: 1

Predicting declining and growing occupations using supervised machine learning 使用监督式机器学习预测衰退和增长的职业

IF 3.2

Journal of Computational Social Science Pub Date : 2023-06-08 DOI: 10.1007/s42001-023-00211-0

Christelle Khalaf, G. Michaud, G. J. Jolley

引用次数: 1

Centre assessment grades in 2020: a natural experiment for investigating bias in teacher judgements. 2020年中心评估成绩：调查教师判断中的偏见的自然实验。

IF 3.2

Journal of Computational Social Science Pub Date : 2023-05-15 DOI: 10.1007/s42001-023-00206-x

Louis Magowan

{"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":"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.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"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}

引用次数: 0

Framing climate change in Nature and Science editorials: applications of supervised and unsupervised text categorization 《自然》和《科学》社论中的气候变化框架:监督和非监督文本分类的应用

IF 3.2

Journal of Computational Social Science Pub Date : 2023-05-05 DOI: 10.1007/s42001-023-00199-7

Manfred Stede, Yannic Bracke, Luka Borec, Neele Charlotte Kinkel, Maria Skeppstedt

引用次数: 0

You are how (and where) you search? Comparative analysis of web search behavior using web tracking data. 你是如何（在哪里）搜索的？使用网络跟踪数据对网络搜索行为进行比较分析。

IF 3.2

Journal of Computational Social Science Pub Date : 2023-05-03 DOI: 10.1007/s42001-023-00208-9

Aleksandra Urman, Mykola Makhortykh

{"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":"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.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"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}

引用次数: 4

Computational approach to studying media coverage of organizations 研究组织媒体报道的计算方法

IF 3.2

Journal of Computational Social Science Pub Date : 2023-04-11 DOI: 10.1007/s42001-023-00204-z

Hyunsun Kim-Hahm

引用次数: 0

Predicting perceived ethnicity with data on personal names in Russia 用人名数据预测俄罗斯人的种族认知

IF 3.2

Journal of Computational Social Science Pub Date : 2023-04-04 DOI: 10.1007/s42001-023-00205-y

Alexey Bessudnov, Denis Tarasov, Viacheslav Panasovets, V. Kostenko, I. Smirnov, V. Uspenskiy

引用次数: 1

COCO: an annotated Twitter dataset of COVID-19 conspiracy theories. COCO:新冠肺炎阴谋论的注释推特数据集。

IF 3.2

Journal of Computational Social Science Pub Date : 2023-04-04 DOI: 10.1007/s42001-023-00200-3

Johannes Langguth, Daniel Thilo Schroeder, Petra Filkuková, Stefan Brenner, Jesper Phillips, Konstantin Pogorelov

{"title":"COCO: an annotated Twitter dataset of COVID-19 conspiracy theories.","authors":"Johannes Langguth, Daniel Thilo Schroeder, Petra Filkuková, Stefan Brenner, Jesper Phillips, Konstantin Pogorelov","doi":"10.1007/s42001-023-00200-3","DOIUrl":"10.1007/s42001-023-00200-3","url":null,"abstract":"The COVID-19 pandemic has been accompanied by a surge of misinformation on social media which covered a wide range of different topics and contained many competing narratives, including conspiracy theories. To study such conspiracy theories, we created a dataset of 3495 tweets with manual labeling of the stance of each tweet w.r.t. 12 different conspiracy topics. The dataset thus contains almost 42,000 labels, each of which determined by majority among three expert annotators. The dataset was selected from COVID-19 related Twitter data spanning from January 2020 to June 2021 using a list of 54 keywords. The dataset can be used to train machine learning based classifiers for both stance and topic detection, either individually or simultaneously. BERT was used successfully for the combined task. The dataset can also be used to further study the prevalence of different conspiracy narratives. To this end we qualitatively analyze the tweets, discussing the structure of conspiracy narratives that are frequently found in the dataset. Furthermore, we illustrate the interconnection between the conspiracy categories as well as the keywords.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717507","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: 1