Corona Virus (COVID-19) Geolocation-based Sentiment Data

Lamsal, Rabindra

doi:10.21227/fpsb-jz61

Cited by 14 publications

(10 citation statements)

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“…The data we use in this work were obtained from a collection of geotagged tweet identifiers related to the COVID-19 pandemic 37 . They included only tweets that had keywords associated to COVID-19, such as #corona, #coronavirus, #covid, #covid19, #covid-19, and #sarscov2, for example.…”

Section: A Data Collectionmentioning

confidence: 99%

The relationship between sentiment score and COVID-19 cases in the United States

Luu,

Follmann

2022

Preprint

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The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders, and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19 related tweets. We connect users' sentiment with COVID-19 cases across the USA and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemicrelated legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.

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Section: A Data Collectionmentioning

confidence: 99%

The relationship between sentiment score and COVID-19 cases in the United States

Luu,

Follmann

2022

Preprint

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“…The TBCOV dataset is shared through the CrisisNLP repository 7 . The dataset contains three types of releases covering different dimensions of the data.…”

Section: Data Recordsmentioning

confidence: 99%

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

Imran¹,

Qazi²,

Ofli³

2021

Preprint

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The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports. Such information can help authorities understand the emergent situation and react accordingly. Moreover, social media plays a vital role in tackling misinformation and disinformation. This work presents TBCOV, a large-scale Twitter dataset comprising more than two billion multilingual tweets related to the COVID-19 pandemic collected worldwide over a continuous period of more than one year. More importantly, several state-of-the-art deep learning models are used to enrich the data with important attributes, including sentiment labels, named-entities (e.g., mentions of persons, organizations, locations), user types, and gender information. Last but not least, a geotagging method is proposed to assign country, state, county, and city information to tweets, enabling a myriad of data analysis tasks to understand real-world issues. Our sentiment and trend analyses reveal interesting insights and confirm TBCOV's broad coverage of important topics.

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“…TweetsKB is currently used to shape the understanding of solidarity discourse in the context of migration, e.g. as part of the SOLDISK project 19 . In addition, ongoing joint work with media and communication studies researchers 20 uses TweetsKB to investigate the societal impact of the ongoing Corona pandemic and most importantly, acceptance and trust for mitigating measures, the individual risk assessment and the impact of specific media events or information campaigns on related discourse and solidarity within society.…”

Section: Other Usage and Impactmentioning

confidence: 99%

“…The number of keywords and hashtags range from 3 [16] to 800 [25]. Some of the datasets further apply language filters [1,10,20,33] or other requirements such as the availability of location information [19]. Instead of filtering from Twitter streaming data, authors of ArCOV-19 [14] collect tweets returned by the Twitter standard search API 28 when using COVID-19 related keywords (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…to acquire actual tweet content of the tweets. Some also contain further information of tweets such as the publishing time [2], user ID [10,15,25,33], geo-location [19,23,25] and retweet information [33]. A few datesets contain automatic annotations such as frequent used terms [2], sentiment scores per tweet [20], geo-location inferred from tweets [15] or places mentioned [23,25].…”

Section: Related Workmentioning

confidence: 99%

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TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

Dimitrov,

Baran,

Fafalios

et al. 2020

Preprint

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Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning, NLP and information retrieval methods. With respect to the recent outbreak of COVID-19, online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection or entity recognition. However, obtaining, archiving and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning the period Oct'19-Apr'20. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis, use cases and usage of the corpus.

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Corona Virus (COVID-19) Geolocation-based Sentiment Data

Cited by 14 publications

References 0 publications

The relationship between sentiment score and COVID-19 cases in the United States

The relationship between sentiment score and COVID-19 cases in the United States

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

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