An Interactive Analysis of User-reported Long COVID Symptoms using T witter Data (2022)

Abstract: With millions of documented recoveries from COVID-19 worldwide, various long-term sequelae have been observed in a large group of survivors. This paper is aimed at systematically analyzing user-generated conversations on Twitter that are related to long-term COVID symptoms for a better understanding of the Long COVID health consequences. Using an interactive information extraction tool built especially for this purpose, we extracted key information from the relevant tweets and analyzed the user-reported Long COVID symptoms with respect to their demographic and geographical characteristics. The results of our analysis are expected to improve the public awareness on long-term COVID-19 sequelae and provide important insights to public health authorities.

## introduction the covid-19 pandemic has affected millions of people all over the world. despite the growing knowledge of covid-19, much still remains unclear, especially potential long-term health consequences. the term of long covid was brought up by the patients on twitter in may 2020, in order to express their long-term covid illness @xcite . many long covid sufferers shared their persistent symptoms on social media bringing numerous discussions of similar symptoms experienced by others. long covid, also known as post covid-19 syndrome, has no strict definition. the cdc of the united states foot_0 describes long covid as symptoms for four or more weeks after the infection, however, who foot_1 and british national institute for health and care excellence (nice) foot_2 suggest three months after onset of covid-19. though the majority of infected people experience mild symptoms with no necessity of hospitalization, the post-covid syndrome is being reported by not just hospitalized patients. therefore, massive user-generated long covid data available on social media has a significant value for tracking and analyzing the long-term syndrome. thus, in this work, we aim to apply natural language processing (nlp) approaches to explore the characteristics of long covid symptoms reported by the twitter users in terms of the patient gender, age, and location, as well as in terms of the symptoms duration. by extracting and analyzing key information from long covid-related tweets, we can discover less known chronic physical or mental conditions experienced by large groups of covid-19 patients, and explore the relations between symptoms and demographic or geographic characteristics of patients. moreover, we also seek to study the long covid evolution over time. to address this need, we compare the results of datasets collected in different time periods. as part of this study, we developed an online dashboard 4 to visualize the analysis of long covid symptoms harvested from twitter. a snapshot is shown in fig. 1 . this interactive dashboard provides multi-scale information and insights. our contributions can be summarized as follows: ## related work in response to the covid-19 pandemic, extensive research has been conducted to help the healthcare community respond to this unprecedented emergency. as the concerns of covid-19 long-term consequences are rising, more efforts are being invested in this topic. current research about long covid uses standardized questionnaires or medical assessment to follow up the long-term symptoms of patients with clinical records @xcite . due to the lack of sufficient data about long-term covid-19 complications, some studies explore the covid-19 sequelae through the review of earlier papers @xcite @xcite . numerous studies are using nlp approaches to contribute to the global response to this pandemic crisis. for instance, @xcite use nlp pipeline to extract covid symptoms from unstructured notes. with the use of nlp algorithms, @xcite assess the ct imaging reports for tracking of covid-19 pandemic in the united states. data from social media is widely used for covid symptom analysis @xcite . however, very few studies focus on long covid with social media attributes. singh and reddy (2020) and banda et al. ( 2020 ) analyzed the long-term symptoms distribution by mining and manually reviewing tweets of around 100 self-reporting users. sarker and ge (2021) analyzed the major long covid symptoms distribution by extracting symptoms from posts on reddit, using approximate matching approach based on an expanded meta-lexicon. they mainly analyzed the major symptoms distribution. in this work, we utilize various nlp approaches to explore the long covid symptoms reported on twitter. we aim to conduct multi-scale analysis of long covid symptoms, including not only the symptoms distribution and duration but also the effect of demographic and geographical patient characteristics. ## methodology our data analysis pipeline is demonstrated in fig. 2 , using a synthetic tweet based on several real tweets as an example. first, noun phrases (nps) are identified in each tweet and then classified to different categories. next, long covid-related information is extracted from the identified nps for further analysis. ## case study based on the proposed pipeline, we conducted indepth analysis of long covid symptoms reported on twitter using a sample of long covid-related tweets between may to december 2020, and a sample of october 2021. to explore the long covid evolution over time, we compared the results of may-december 2020 and october 2021. ## results analysis after conducting information extraction with multiple pre-trained models, we performed comprehensive analysis of symptom-related data. we compared the results of may-december 2020 and the results of october 2021 to analyze the long covid evolution over time. the results of may-december 2020 were presented in an interactive dashboard. long covid symptoms analysis extracting the symptoms from the tweets, we analyzed the symptom distribution using the frequency of each symptom reported in each dataset. fig. 4(a) shows the distribution of the symptoms in may-december 2020. from fig. 4 (a), we can see that the major symptoms are fatigue, breathlessness, joint or muscle pain, heart problems, and brain fog. some rarely reported symptoms are headache, sleep disorders, cough, sore throat, skin rash. fig. 4 shows the comparison of symptom distribution in may-december 2020 and october 2021. generally speaking, the top symptoms and rare symptoms stay approximately the same, except for the following changes. the percentages of fatigue and heart problems have increased from 46.6% to 56.7% and from 9.42% to 13.6%, respectively. the percentage of breathlessness and joint or muscle pain have decreased from 12% to 4.98% and from 16% to 10.2%, respectively. these changes may be explained by the mutations of the virus over time. ## discussion some important insights can be gained from the analysis. for example, women tend to experience long covid more frequently than men, which is similar to the findings of some medical studies @xcite @xcite . additionally, 'mental problems' is one of the top symptoms shown in our results, which is rarely mentioned as a common symptom in other works. in our work, 'mental problems' refers to depression, anxiety, loneliness and other mental or emotional health issues. similarly, sarker and ge (2021) reported 55.2% users in the reddit dataset experiencing mental problems since their covid onset. ## conclusions in this work, we conducted a comprehensive analysis of long covid symptoms reported by the twitter users with respect to their demographic and geographical characteristics. the presented case study provides detailed information and important insights about multiple aspects of long-term symptoms. the comparative analysis of two periods of time (in 2020 and 2021) shows the consistent and the evolving characteristics of the long covid. furthermore, an interactive online dashboard was built to visualize the results of the 2020 dataset. limitations of this work include the possible effect of large amounts of noise in the twitter data on our results. besides, the data analyzed was limited to english tweets, which might not be representative of all segments of the world population affected by covid-19. thus, our future work will focus on analyzing larger amounts of data in multiple languages. symptoms co-occurrence and presence of comorbidities may also be explored in the future work. moreover, non-binary gender class may be taken into account in the future work as well.