International Conference on Social, Cultural, and Behavioral Modeling最新文献

Tracking China's Cross-Strait Bot Networks Against Taiwan 追踪中国针对台湾的两岸僵尸网络

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2023-10-16 DOI: 10.1007/978-3-031-43129-6_12

Charity S. Jacobs, L. Ng, K. Carley

引用次数: 0

CCTFv1: Computational Modeling of Cyber Team Formation Strategies 网络团队形成策略的计算模型

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2023-07-17 DOI: 10.48550/arXiv.2307.10258

Tristan J. Calay, Basheer Qolomany, Aos Mulahuwaish, L. Hossain, J. B. Abdo

引用次数: 0

Inductive Linear Probing for Few-shot Node Classification 基于感应线性探测的少射节点分类

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2023-06-14 DOI: 10.48550/arXiv.2306.08192

Hirthik Mathavan, Zhen Tan, Nivedh Mudiam, Huan Liu

引用次数: 0

Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes 拆解仇恨:了解针对NBA运动员的仇恨言论趋势

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2023-06-05 DOI: 10.48550/arXiv.2306.03086

Edinam Kofi Klutse, Samuel Nuamah-Amoabeng, Hanjia Lyu, Jiebo Luo

{"title":"Dismantling Hate: Understanding Hate Speech Trends Against NBA Athletes","authors":"Edinam Kofi Klutse, Samuel Nuamah-Amoabeng, Hanjia Lyu, Jiebo Luo","doi":"10.48550/arXiv.2306.03086","DOIUrl":"https://doi.org/10.48550/arXiv.2306.03086","url":null,"abstract":"Social media has emerged as a popular platform for sports fans to express their opinions regarding athletes' performance. Fans consistently hold high expectations for athletes, anticipating exceptional performances week after week. This ongoing phenomenon sometimes gives rise to highly negative sentiments, with the worst-case scenario involving the occurrence of hate speech. The National Basketball Association (NBA) is widely recognized as one of the most popular sports leagues globally. However, an unfortunate aspect that has emerged in recent years is the presence of abusive fans within the league. Consequently, the focus of this research is to identify which NBA athletes experience abuse on Twitter and delve deeper into the underlying reasons behind such mistreatment. To address the research questions at hand, the study employs a curated set of keywords to query the Twitter API, gathering a comprehensive collection of tweets that potentially contain hate speech directed toward NBA players. A deep learning classification model is implemented, effectively identifying tweets that genuinely exhibit hate speech. We further use keyword search methods to detect the specific groups that are targeted by hate speech the most and identify topics of hate speech tweets. The findings of our research indicate that certain groups of athletes are particularly vulnerable to hate speech from fans. Racism, physique shaming, play style, and anti-LGBTQ remarks are the major themes. These findings contribute to a broader understanding of the challenges faced by NBA athletes in the digital space and provide a foundation for developing strategies to combat hate speech and foster a more inclusive environment for all individuals involved in the NBA community.","PeriodicalId":336133,"journal":{"name":"International Conference on Social, Cultural, and Behavioral Modeling","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605259","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

Automated Utterance Labeling of Conversations Using Natural Language Processing 使用自然语言处理的对话自动话语标记

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2022-08-12 DOI: 10.48550/arXiv.2208.06525

M. Laricheva, Chiyu Zhang, Y. Liu, Guan-Jhih Chen, Terence Tracey, Richard Young, G. Carenini

{"title":"Automated Utterance Labeling of Conversations Using Natural Language Processing","authors":"M. Laricheva, Chiyu Zhang, Y. Liu, Guan-Jhih Chen, Terence Tracey, Richard Young, G. Carenini","doi":"10.48550/arXiv.2208.06525","DOIUrl":"https://doi.org/10.48550/arXiv.2208.06525","url":null,"abstract":". Conversational data is essential in psychology because it can help researchers understand individuals’ cognitive processes, emotions, and behaviors. Utterance labelling is a common strategy for analyzing this type of data. The development of NLP algorithms allows researchers to automate this task. However, psychological conversational data present some challenges to NLP researchers, including multilabel classification, a large number of classes, and limited available data. This study explored how automated labels generated by NLP methods are comparable to human labels in the context of conversations on adulthood transition. We proposed strategies to handle three common challenges raised in psychological studies. Our findings showed that the deep learning method with domain adaptation (RoBERTa-CON) outperformed all other machine learning methods; and the hierarchical labelling system that we proposed was shown to help researchers strategically analyze conversational data. Our Py-thon code and NLP model are available at https://github.com/mlaricheva/auto-mated_labeling.","PeriodicalId":336133,"journal":{"name":"International Conference on Social, Cultural, and Behavioral Modeling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124333557","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

Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data 互信息评分:增加分类聚类任务的可解释性与应用于儿童福利数据

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2022-08-03 DOI: 10.48550/arXiv.2208.01802

P. Sankhe, Seventy F. Hall, Melanie Sage, Maria Y. Rodriguez, V. Chandola, Kenneth Joseph

引用次数: 0

Using Diffusion of Innovations Theory to Study Connective Action Campaigns 运用创新扩散理论研究关联行动运动

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2021-07-06 DOI: 10.1007/978-3-030-80387-2_13

Billy Spann, M. Maleki, Esther Mead, Erik Buchholz, Dr Nidhi Agarwal, T. Williams

引用次数: 1

Multi-agent Naïve Utility Calculus: Intent Recognition in the Stag-Hunt Game 多智能体Naïve效用演算:猎鹿游戏中的意图识别

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2021-07-06 DOI: 10.1007/978-3-030-80387-2_32

Lux Miranda, O. Garibay

引用次数: 1

Identifying Shifts in Collective Attention to Topics on Social Media 识别集体关注社交媒体话题的转变

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2021-07-06 DOI: 10.1007/978-3-030-80387-2_22

Yuzi He, Ashwin Rao, K. Burghardt, Kristina Lerman

引用次数: 2

On the Effect of Social Norms on Performance in Teams with Distributed Decision Makers 社会规范对分布式决策者团队绩效的影响

International Conference on Social, Cultural, and Behavioral Modeling Pub Date : 2021-04-13 DOI: 10.1007/978-3-030-80387-2_29

Ravshanbek Khodzhimatov, Stephan Leitner, F. Wall

引用次数: 1