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2021, Academia Letters
2021 •
Background. After a year and half and over 4 million deaths, the COVID-19 pandemic continues to be widespread, and its related topics continue to dominate the global media. Although COVID-19 diagnoses have been well monitored, neither the impacts of the disease on human behavior and social dynamics nor the effectiveness of policy interventions aimed at its containment are fully understood. Monitoring the spatial and temporal patterns of behavior, social dynamics and policy—and then their interrelations—can provide critical information for preparatory action and effective response. Methods. Here we present an open-source dataset of 1.92 million keyword-selected Twitter posts, updated weekly from January 2020 to present, along with a dynamic dashboard showing totals at national and subnational administrative divisions. Results. The dashboard presents 100% of the geotagged tweets that contain keywords or hashtags related COVID-19. We validated our inclusion criteria using a machine lea...
2020 •
Applied Intelligence
Design and analysis of a large-scale COVID-19 tweets datasetJournal of Medical Internet Research
Twitter talk on COVID-19: A temporal examination of topics, trends and sentiments (Preprint)2020 •
With restricted movements and stay-at-home orders due to COVID-19 pandemic, social media platforms like Twitter have become an outlet for users to express their concerns, opinions and feelings about the pandemic. Individuals, health agencies and governments are using Twitter to communicate about COVID-19. This research builds on the emergent stream of studies to examine COVID-19 related English tweets covering a time period from Jan 1, 2020 to May 9, 2020. We perform a temporal assessment and examine variations in the topics and sentiment-scores to uncover key trends. To examine key themes and topics from COVID-19 related English tweets posted by individuals, and to explore the trends and variations in how the COVID-19 related tweets, key topics and associated sentiments changed over a period of time before and after the disease was declared as pandemic. Combining data from two publicly available COVID-19 tweet datasets with our own search, we compiled a dataset of 13.9 million COVI...
Cureus
An Analysis of the #CovidPain Tweet Chat During the First Wave of the COVID-19 Pandemic in 20202021 •
Introduction: In March 2020, we organized two tweet chats to discuss the COVID-19 pandemic and its impact on people affected by chronic pain. The objective of this study is to evaluate the #CovidPain tweet chat activities that took place at the early stages of the COVID-19 pandemic. Methods: We performed a quantitative analysis of the magnitude, range, engagement, and sentiment of each tweet chat. The data was extracted from Twitter and analyzed in Twitter Analytics and Symplur Signals using frequency and distributions. Then, we conducted a qualitative content analysis of the narrative tweets generated in response to the questions posted during the tweet chats. Results: The two tweet chats attracted 2305 participants, which generated 4351 tweets. The participants were healthcare providers, patient advocates, researchers/academics, and caregivers. COVID-19 had both negative and positive impacts. The negative consequences of COVID-19 included the reduction of physical activity, cancel...
2020 •
The goal of ConronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity and to model the human emotions during the COVID-19 pandemic. The main objective is to explore the psychology and behavior of the societies at large which can assist in managing the economic and social crisis during the ongoing pandemic as well as the after-effects of it. The novel coronavirus (COVID-19) pandemic forced people to stay at home to reduce the spread of the virus by maintaining the social distancing. However, social media is keeping people connected both locally and globally. People are sharing information (e.g. personal opinions, some facts, news, status, etc.) on social media platforms which can be helpful to understand the various public behavior such as emotions, sentiments, and mobility during the ongoing pandemic. In this paper, we describe the CoronaVis Twitter dataset (focused on the United States) that we have been collecting from earl...
2021 IEEE International Conference on Smart Computing (SMARTCOMP)
Understanding the Societal Disruption due to COVID-19 via User Tweets2021 •
2021 •
With a large proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about the vaccines spreading on social media. In this paper, we present a dataset of English-language Twitter posts about COVID19 vaccines. We show statistics for our dataset regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also demonstrate how we are able to perform analysis of the prevalence over time of highand lowcredibility sources, topic groups of hashtags, and geographical distributions. We have developed a live dashboard to allow people to track hashtag changes over time. The dataset can be used in studies about the impact of online information on COVID-19 vaccine uptake and health outcomes.
Scientometrics
COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier2021 •
With a substantial proportion of the population currently hesitant to take the COVID-19 vaccine, it is important that people have access to accurate information. However, there is a large amount of low-credibility information about vaccines spreading on social media. In this paper, we present the CoVaxxy dataset, a growing collection of English-language Twitter posts about COVID-19 vaccines. Using one week of data, we provide statistics regarding the numbers of tweets over time, the hashtags used, and the websites shared. We also illustrate how these data might be utilized by performing an analysis of the prevalence over time of high- and low-credibility sources, topic groups of hashtags, and geographical distributions. Additionally, we develop and present the CoVaxxy dashboard, allowing people to visualize the relationship between COVID-19 vaccine adoption and U.S. geo-located posts in our dataset. This dataset can be used to study the impact of online information on COVID-19 healt...
Europe Oceans 2005
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2020 •