Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

Gharavi, Erfaneh; Nazemi, Neda; Dadgostari, Faraz

doi:10.48550/arxiv.2005.00475

Cited by 7 publications

(11 citation statements)

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“…A wealth of recent studies have utilized the tweets during this pandemic for extracting useful information and presenting insights into the public health. Particularly, sentiment analysis has been utilized in the analysis of lockdown life [10], topic modelling has been used for the analysis of the response of politicians [8] and situation forecasting has been leveraged for surfacing the techniques of crisis management [4] during COVID-19.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Analysis through Transformer Language Models

Hameed¹,

Waheed²

2021

Preprint

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Understanding the public sentiment and perception in a healthcare crisis is essential for developing appropriate crisis management techniques. While some studies have used Twitter data for predictive modelling during COVID-19, fine-grained sentiment analysis of the opinion of people on social media during this pandemic has not yet been done. In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. For this purpose, we perform supervised training of four transformer language models on the downstream task of multi-label classification of tweets into seven tone classes: [confident, anger, fear, joy, sadness, analytical, tentative]. We achieve a LRAP (Label Ranking Average Precision) score of 0.9267 through RoBERTa. This trained transformer model is able to correctly predict, with high accuracy, the tone of a tweet. We then leverage this model for predicting tones for 200,000 tweets on COVID-19. We then perform a country-wise analysis of the tone of tweets, and extract useful indicators of the psychological condition about the people in this pandemic.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Analysis through Transformer Language Models

Hameed¹,

Waheed²

2021

Preprint

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“…As a result, several interesting solutions, aiming at different aspects of the pandemic, have been proposed over the last year [14]. For instance, there have been efforts for an early COVID-19 outbreak detection to help in an emergency response preparedness [15]. Similarly, a large portion of the efforts aimed at an automatic diagnosis, prognosis, and treatment [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…For instance, there have been efforts for an early COVID-19 outbreak detection to help in an emergency response preparedness [15]. Similarly, a large portion of the efforts aimed at an automatic diagnosis, prognosis, and treatment [15], [16]. Fake news detection, risk assessment, logistic planning, and understanding of social interventions, such as monitoring social distancing, are the other key aspects of the pandemic that received the attention of the community [14], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

A Benchmark Dataset for Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Applications

Ahmad,

Qolomany,

Khan

et al. 2021

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Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In total, we employ eight different methods achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. We also highlight the key advantages, drawbacks, and users' concerns over the applications. Moreover, we also collect and annotate a large-scale dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The presented analysis and the dataset are expected to provide a baseline/benchmark for future research in the domain.

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“…Numerous studies have been published of late in this realm, including studies on pharmacovigilance (27), medication non-adherence (28), toxicovigilance (29), foodborne illness (30), and tracking infectious/viral disease spread (31, 32). The onset and spread of SARS-COV-2 causing the COVID-19 pandemic has been the impetus for new health related social media text mining research to look at symptoms (33, 34, 35, 36, 37, 38) and potentially predict new outbreaks (38, 39, 40). However, methods to determine the biological sex of a social media user are limited and imprecise, and research using these alternative data has not yet reached the point where sex-specific health outcomes can be accurately elucidated.…”

Section: Introductionmentioning

confidence: 99%

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

Golder

OConnor

Wang

et al. 2022

Preprint

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A bias in health research to favor understanding of diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature that used machine learning or NLP techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (1) "women" or "men" or "sex," (2) "big data" or "artificial intelligence" or "NLP", and (3) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in the included studies is disproportionately less than women. Even though AI and NLP techniques are widely applied in health research, few studies use them to take advatage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process to- wards correction is slow. We reflected on what would be the best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

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Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

Cited by 7 publications

References 0 publications

COVID-19 Tweets Analysis through Transformer Language Models

COVID-19 Tweets Analysis through Transformer Language Models

A Benchmark Dataset for Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Applications

Best Practices on Big Data Analytics to Address Sex-Specific Biases in our Understanding of the Etiology, Diagnosis and Prognosis of Diseases

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