Twitter has been heavily used as an important channel for communicating and discussing about events in real-time. In such major events, many uninformative tweets are also published rapidly by many users, making it hard to follow the events. In this paper, we address this problem by investigating machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We examine both traditional approaches with a rich set of handcrafted features and state of the art approaches with automatically learned features. We further propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former and our proposed model performs the best, suggesting highly effective mechanisms for tracking mass events.
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets
VinAI Research；Australian e-Health Research Centre, CSIRO（澳大利亚电子健康研究中心（CSIRO））；Oracle Digital Assistant, Oracle（Oracle数字助手（Oracle公司））；The University of Queensland（昆士兰大学）；Oracle（Oracle公司）
An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection
University of Cincinnati（辛辛那提大学）
本文介绍了一项探索性研究的结果，该研究针对Twitter上不断生成的大数据，这些数据与分享有关COVID-19大流行的信息、新闻、观点、意见、想法、反馈和经验有关，特别关注Omicron变体，这是目前SARS-CoV-2的全球主要变体。共研究了12028条关于Omicron变体的推文，并分析了推文的具体特征，包括情感，语言，来源，类型和嵌入式URL。这项研究的结果是多方面的。首先，从情感分析中，观察到50.5%的推文具有中性情感。其他情绪-坏的，好的，可怕的和伟大的分别在15.6%，14.0%，12.5%和7.5%的推文中被发现。其次，语言翻译的结果显示，65.9%的推文是用英语发布的。其次是西班牙语、法语、意大利语和其他语言。第三，来源追踪的结果显示，Android版Twitter与35.2%的推文相关。紧随其后的是Twitter Web App、iPhone版Twitter、iPad版Twitter和其他来源。第四，研究推文的类型发现，转发占推文的60.8%，其次是原创推文和回复，分别占推文的19.8%和19.4%。第五，在嵌入URL分析方面，发现最常见的嵌入推文的域名是twitter.com，其次是biorxiv.org，nature.com和其他域名。最后，为了支持该领域的类似研究，我们开发了一个Twitter数据集，该数据集包含自2021年11月24日首次发现该变体病例以…
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs
School of ECE, National Technical University of Athens（雅典国立技术大学电子与计算机工程学院）；Department of Informatics, Athens University of Economics and Business（雅典经济与商业大学信息学院）；Department of Informatics, University of Athens（雅典大学信息学院）；Behavioral Signal Technologies
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets
