COVID-19 has created a major public health problem worldwide and other
problems such as economic crisis, unemployment, mental distress, etc. The
pandemic is deadly in the world and involves many people not only with
infection but also with problems, stress, wonder, fear, resentment, and hatred.
Twitter is a highly influential social media platform and a significant source
of health-related information, news, opinion and public sentiment where
information is shared by both citizens and government sources. Therefore an
effective analysis of COVID-19 tweets is essential for policymakers to make
wise decisions. However, it is challenging to identify interesting and useful
content from major streams of text to understand people's feelings about the
important topics of the COVID-19 tweets. In this paper, we propose a new
\textit{framework} for analyzing topic-based sentiments by extracting key
topics with significant labels and classifying positive, negative, or neutral
tweets on each topic to quickly find common topics of public opinion and
COVID-19-related attitudes. While building our model, we take into account
hybridization of BiLSTM and GRU structures for sentiment analysis to achieve
our goal. The experimental results show that our topic identification method
extracts better topic labels and the sentiment analysis approach using our
proposed hybrid deep learning model achieves the highest accuracy compared to
traditional models.

提取了 COVID-19 推文的关键主题和情感，使用深度学习模型实现了更好的主题标签提取和情感分析，以及最高的准确率。

基于混合深度学习框架自动发现 COVID-19 推特中的话题和情感

Exploring a Hybrid Deep Learning Framework to Automatically Discover  Topic and Sentiment in COVID-19 Tweets

The proliferation of online misinformation has emerged as one of the biggest
threats to society. Considerable efforts have focused on building
misinformation detection models, still the perils of misinformation remain
abound. Mitigating online misinformation and its ramifications requires a
holistic approach that encompasses not only an understanding of its intricate
landscape in relation to the complex issue and topic-rich information ecosystem
online, but also the psychological drivers of individuals behind it. Adopting a
time series analytic technique and robust causal inference-based design, we
conduct a large-scale observational study analyzing over 32 million COVID-19
tweets and 16 million historical timeline tweets. We focus on understanding the
behavior and psychology of users disseminating misinformation during COVID-19
and its relationship with the historical inclinations towards sharing
misinformation on Non-COVID topics before the pandemic. Our analysis
underscores the intricacies inherent to cross-topic misinformation, and
highlights that users' historical inclination toward sharing misinformation is
positively associated with their present behavior pertaining to misinformation
sharing on emergent topics and beyond. This work may serve as a valuable
foundation for designing user-centric inoculation strategies and
ecologically-grounded agile interventions for effectively tackling online
misinformation.

在线谣言的泛滥已成为社会面临的最大威胁之一。本文采用时间序列分析技术和稳健的因果推断设计，对超过 3200 万条 COVID-19 推文和 1600 万条历史时间线推文进行了大规模观察性研究，旨在理解 COVID-19 期间传播谣言的用户行为和心理，并与大流行前在非 COVID 话题上分享谣言的历史倾向关联。我们的分析强调了跨主题谣言的复杂性，并突出了用户在分享新兴话题及其他方面的谣言行为与其历史倾向之间的积极关联。本研究可为设计面向用户的免疫策略和基于生态的敏捷干预措施提供有价值的基础，以有效应对在线谣言。

通过 COVID-19 大流行的视角对在线流言背后的人类的观察性研究

Understanding the Humans Behind Online Misinformation: An Observational  Study Through the Lens of the COVID-19 Pandemic

When any extraordinary event takes place in the world wide area, it is the
social media that acts as the fastest carrier of the news along with the
consequences dealt with that event. One can gather much information through
social networks regarding the sentiments, behavior, and opinions of the people.
In this paper, we focus mainly on sentiment analysis of twitter data of India
which comprises of COVID-19 tweets. We show how Twitter data has been extracted
and then run sentimental analysis queries on it. This is helpful to analyze the
information in the tweets where opinions are highly unstructured,
heterogeneous, and are either positive or negative or neutral in some cases.

本文主要关注印度 COVID-19 推文的 Twitter 数据的情感分析，展示了如何提取 Twitter 数据并对其进行情感分析查询，以便分析推文中信息的结构不统一、异质且在某些情况下为积极、消极或中性的观点。

印度推特上的 Covid-19 公众情绪分析分类

Covid-19 Public Sentiment Analysis for Indian Tweets Classification

We present CoNTACT: a Dutch language model adapted to the domain of COVID-19
tweets. The model was developed by continuing the pre-training phase of RobBERT
(Delobelle, 2020) by using 2.8M Dutch COVID-19 related tweets posted in 2021.
In order to test the performance of the model and compare it to RobBERT, the
two models were tested on two tasks: (1) binary vaccine hesitancy detection and
(2) detection of arguments for vaccine hesitancy. For both tasks, not only
Twitter but also Facebook data was used to show cross-genre performance. In our
experiments, CoNTACT showed statistically significant gains over RobBERT in all
experiments for task 1. For task 2, we observed substantial improvements in
virtually all classes in all experiments. An error analysis indicated that the
domain adaptation yielded better representations of domain-specific
terminology, causing CoNTACT to make more accurate classification decisions.

利用 2021 年发布的 280 万条荷兰语 COVID-19 相关推文进行预训练的 CoNTACT 模型，相比于 RobBERT，在两项任务中（二元疫苗犹豫检测和检测疫苗犹豫的论据）均表现出显著的性能提升，证明了领域自适应的重要性，并且使得 CoNTACT 在领域特定词汇的表示上更加精准。