Journal of Computational Social Science最新文献_第8页

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":null,"pages":null},"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":null,"pages":null},"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

Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling 印度推特用户中关于癌症的推文主题——使用潜在狄利克雷分配主题建模的计算方法

IF 3.2

Journal of Computational Social Science Pub Date : 2023-08-27 DOI: 10.1007/s42001-023-00222-x

Thilagavathi Ramamoorthy, Bagavandas Mappillairaju

引用次数: 0

Social media-based social–psychological community resilience analysis of five countries on COVID-19 基于社交媒体的五国COVID-19社会心理社区恢复力分析

IF 3.2

Journal of Computational Social Science Pub Date : 2023-08-18 DOI: 10.1007/s42001-023-00220-z

J. Valinejad, Zhen Guo, Jin-Hee Cho, I. Chen

引用次数: 0

Temporal communication dynamics in the aftermath of large-scale upheavals: do digital footprints reveal a stage model? 大规模剧变后的时间交流动态:数字足迹是否揭示了一个阶段模型?

IF 3.2

Journal of Computational Social Science Pub Date : 2023-07-24 DOI: 10.1007/s42001-023-00218-7

Pablo M. Flores, Martin Hilbert

引用次数: 0

The value of manual annotation in assessing trends of hate speech on social media: was antisemitism on the rise during the tumultuous weeks of Elon Musk’s Twitter takeover? 人工注释在评估社交媒体上仇恨言论趋势方面的价值:在埃隆·马斯克(Elon Musk)接管Twitter的动荡几周里，反犹太主义是否有所抬头?

IF 3.2

Journal of Computational Social Science Pub Date : 2023-07-13 DOI: 10.1007/s42001-023-00219-6

Günther Jikeli, Katharina Soemer

引用次数: 2

Multi-balanced redistricting Multi-balanced选区重划

IF 3.2

Journal of Computational Social Science Pub Date : 2023-07-07 DOI: 10.1007/s42001-023-00217-8

Daryl R. DeFord, Elliot Kimsey, R. Zerr

引用次数: 0

Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation 种族隔离与态度的空间格局:基于登记数据和社会模拟的联系研究

IF 3.2

Journal of Computational Social Science Pub Date : 2023-06-30 DOI: 10.1007/s42001-023-00216-9

Thomas Feliciani, J. Tolsma, A. Flache

引用次数: 0