Complaining is a speech act that expresses a negative inconsistency between reality and human expectations. While prior studies mostly focus on identifying the existence or the type of complaints, in this work, we present the first study in computational linguistics of measuring the intensity of complaints from text. Analyzing complaints from such perspective is particularly useful, as complaints of certain degrees may cause severe consequences for companies or organizations. We create the first Chinese dataset containing 3,103 posts about complaints from Weibo, a popular Chinese social media platform. These posts are then annotated with complaints intensity scores using Best-Worst Scaling (BWS) method. We show that complaints intensity can be accurately estimated by computational models with the best mean square error achieving 0.11. Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers. We finally show that our complaints intensity scores can be incorporated for better estimating the popularity of posts on social media.
Automatically Identifying Complaints in Social Media
Bloomberg LP（彭博有限合伙企业）；Politehnica University of Bucharest（布加勒斯特理工大学）；University of Sheffield（谢菲尔德大学）
抱怨是一种基本的言语行为，经常用于人类和计算机中介的沟通，以表达在特定情况下现实和期望之间的负面不匹配。自动识别社交媒体中的投诉对于组织或品牌改善客户体验或开发用于处理和响应投诉的对话系统至关重要。在本文中，我们介绍了第一个系统的分析抱怨在计算语言学。我们收集了一个新的注释数据集，在Twitter上用英语表达的书面投诉。\footnote{Data and code is available here: \url{https://github.com/danielpreotiuc/complaints-social-media}}我们提出了一个广泛的语言分析抱怨作为一种言语行为在社交媒体和训练强大的功能为基础的和神经模型的投诉在九个领域实现预测性能高达79 F1使用远程监督。
An Iterative Approach for Identifying Complaint Based Tweets in Social Media Platforms
MIDAS, IIIT-Delhi（德里印度信息技术学院MIDAS中心）；University of Maryland, College Park（马里兰大学帕克分校）；Bloomberg（彭博公司）；Netaji Subhas Institute of Technology（纳吉吉·萨布哈什理工学院）
Computational Analysis of Insurance Complaints: GEICO Case Study
College of Information and Communications, University of South Carolina（南卡罗来纳大学信息与传播学院）；South Carolina Honors College, University of South Carolina（南卡罗来纳大学南卡罗来纳荣誉学院）
Pegasus@Dravidian-CodeMix-HASOC2021: Analyzing Social Media Content for Detection of Offensive Text
Indian Institute of Information Technology Tiruchirappalli（蒂鲁吉拉帕利信息技术学院）；Kongu Engineering College（贡古工程学院）；Sri Krishna Adithya College of Arts and Science（斯里克里希纳阿迪蒂亚文理学院）；Sultan Idris Education University（苏丹伊德里斯教育大学）
为了解决检测攻击性评论/帖子的难题，这些评论/帖子是相当非正式的，非结构化的，错误的和代码混合的，我们在这篇研究论文中介绍了两种创造性的方法。社交媒体平台上的攻击性评论/帖子可能会影响个人，团体或未成年人。为了对两种流行的德拉威语言（泰米尔语和马拉雅拉姆语）的评论/帖子进行分类，作为HASOC - DravidianCodeMix FIRE 2021共享任务的一部分，我们采用了两个基于transformer的原型，它们成功地在所有任务中排名前8位。我们的方法的代码可以查看和使用。
From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media
Vector Institute for Artificial Intelligence（向量人工智能研究所）；Carnegie Mellon University（卡内基梅隆大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
The Media Bias Detector: A Framework for Annotating and Analyzing the News at Scale
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；Harvard Business School（哈佛商学院）；Microsoft Research（微软研究院）；Annenberg School for Communication, University of Pennsylvania（传播学院，宾夕法尼亚大学）；Department of Operations, Information, and Decisions, University of Pennsylvania（运营、信息与决策系，宾夕法尼亚大学）
Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
Booz Allen Hamilton（博思艾伦咨询公司）；International Water Management Institute（国际水管理研究院）；NOVA Information Management School (NOVA IMS)（NOVA信息管理学院）；New York University Abu Dhabi（纽约大学阿布扎比分校）
A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
Dartmouth College（达特茅斯学院）；Geisel School of Medicine（盖泽尔医学院）；Dartmouth Health（达特茅斯医疗中心）；Dartmouth Hitchcock Medical Center（达特茅斯-希契科克医疗中心）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Yale School of Medicine（耶鲁医学院）；Yale University School of Public Health（耶鲁大学公共卫生学院）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；New York State Psychiatric Institute（纽约州精神病学研究所）
Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
LSTM Based Sentiment Analysis for Cryptocurrency Prediction
University of Maryland, Baltimore County（马里兰大学巴尔的摩分校）；Leibniz University Hannover（汉诺威莱布尼茨大学）；McGill University（麦吉尔大学）；University of Stavanger（斯塔万格大学）；Guangdong Pharmaceutical University（广东药科大学）；University of Southern Queensland（南昆士兰大学）
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media
Emory University（埃默里大学）；University of Pennsylvania（宾夕法尼亚大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Georgia Institute of Technology（佐治亚理工学院）
