Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health. Health-related big data and technological advances have the potential to significantly aid the fight against such pandemics, including the current pandemic's ongoing and long-term impacts. Specifically, the field of natural language processing (NLP) has enormous potential at a time when vast amounts of text-based data are continuously generated from a multitude of sources, such as health/hospital systems, published medical literature, and social media. Effectively mitigating the impacts of the pandemic requires tackling challenges associated with the application and deployment of NLP systems. In this paper, we review the applications of NLP to address diverse aspects of the COVID-19 pandemic. We outline key NLP-related advances on a chosen set of topics reported in the literature and discuss the opportunities and challenges associated with applying NLP during the current pandemic and future ones. These opportunities and challenges can guide future research aimed at improving the current health and social response systems and pandemic preparedness.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944;p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

A Light-Weight Text Summarization System for Fast Access to Medical Evidence.

As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner. Typical medical text summarization approaches require domain knowledge, and the performances of such systems rely on resource-heavy medical domain-specific knowledge sources and pre-processing methods (e.g., text classification) for deriving semantic information. Consequently, these systems are often difficult to speedily customize, extend, or deploy in low-resource settings, and they are often operationally slow. In this paper, we propose a fast and simple extractive summarization approach that can be easily deployed and run, and may thus aid medical experts and researchers obtain fast access to the latest research evidence. At runtime, our system utilizes similarity measurements derived from pre-trained medical domain-specific word embeddings in addition to simple features, rather than computationally-expensive pre-processing and resource-heavy knowledge bases. Automatic evaluation using ROUGE-a summary evaluation tool-on a public dataset for evidence-based medicine shows that our system's performance, despite the simple implementation, is statistically comparable with the state-of-the-art. Extrinsic manual evaluation based on recently-released COVID19 articles demonstrates that the summarizer performance is close to human agreement, which is generally low, for extractive summarization.

Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.

OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.