COVID -19 T witter Monitor: Aggregating and Visualizing COVID -19 Related Trends in Social Media (2020)

Abstract: Social media platforms offer extensive information about the development of the COVID-19 pandemic and the current state of public health. In recent years, the Natural Language Processing community has developed a variety of methods to extract health-related information from posts on social media platforms. In order for these techniques to be used by a broad public, they must be aggregated and presented in a user-friendly way. We have aggregated ten methods to analyze tweets related to the COVID-19 pandemic, and present interactive visualizations of the results on our online platform, the COVID-19 Twitter Monitor. In the current version of our platform, we offer distinct methods for the inspection of the dataset, at different levels: corpus-wide, single post, and spans within each post. Besides, we allow the combination of different methods to enable a more selective acquisition of knowledge. Through the visual and interactive combination of various methods, interconnections in the different outputs can be revealed.

## introduction today, social media platforms are important sources of information, with a usage rate of over 70% for adults in the usa and a continuous increase in popularity. 1 platforms like twitter are characterized by their thematic diversity and realtime coverage of worldwide events. as a result, social media platforms offer information, especially about ongoing events that are locally and dynamically fluctuating, such as the sars-cov-2 (covid-19) pandemic. the micro-posts contain not only the latest news or announcements of new medical findings but also reports about personal well-being and the sentiment towards public health interventions. information contained in tweets has multiple usages. for instance, domain experts could discover how recent scientific studies find an echo in social media, while the health industry could find patients' reactions to certain drugs, and the general public could observe trends towards popular topics (e.g. the alternating popularity of politicians over time). 2 preprocessing analysis detection classification database database interactive web visualization download & downsampling . . . to provide universal access to the acquisition of knowledge from public discourse about covid-19 on twitter, social media mining methods have to be easily applicable. we present a platform called covid-19 twitter monitor that analyzes, categorizes, and extracts textual information from posts on twitter. our platform provides insights into the development and spread of the covid-19 pandemic. the information is retrieved at different levels: spans within each post (e.g. drug name detection), single post (e.g. text language identification) and corpus level (e.g. distribution of hashtags). we use a variety of different methods to analyse pandemic-related micro-posts with a focus on health issues. in contrast to existing platforms, we aim to provide a platform for newly emerging diseases for which no disease-specific supervised datasets are available. therefore, our approach concentrates on unsupervised and pre-trained supervised methods that can be expected to generalize to unseen conditions. we have created an interactive web interface that integrates these methods and thus simplifies the use of social media mining methods. when visualizing the results, different methods are combined to display underlying connections in the extracted information. with this approach, we hope to make the contained information more accessible and provide an increased level of insight. since we present the first version of our web platform foot_0 , this is only an initial description of our approach and the preliminary results. ## related work numerous studies show that social media platforms are particularly suitable for obtaining health-related information like the identification of drug abuse or the tracking of infectious diseases @xcite @xcite . the detection of infectious disease outbreaks by using data from twitter has already been tested, for instance in the case of the ebola virus @xcite and the zika virus @xcite . @xcite propose using social media data for disease surveillance by locally determining and forecasting influenza prevalence. they address obstacles that the typically informal text on social media platforms presents to common language processing systems, and stress the need for normalized medical terminology and general linguistic normalization for social media. in addition, they argue that sentiment analysis is particularly suitable for analyzing the patient's impression of the quality of health care or public opinion on certain drugs. likewise, @xcite apply sentiment analysis to tweets concerning virus outbreaks to gain information for disease control. @xcite describe how to identify disease outbreaks using text-based data. like @xcite , they show how topic modeling can be used to discover health-related topics in the microposts. twitter adapted topic models were also utilized by @xcite to conduct syndromic surveillance regarding the flu. @xcite showed that more intricate data retrieval techniques can be applied to noisy twitter data, such as identifying posts that mention personal health (phm). further fine-grained analyses were conducted by them, for example, to determine whether the tweet concerns the health of the author or a person familiar with the author. going even further, @xcite showed that twitter-related phm classifiers can operate disease agnostic; they trained their system on four disease contexts and could achieve a precision of 0.77 on 34 other health issues. ## data we use the covid-19 twitter dataset published by the panacea lab @xcite . the dataset consists of daily twitter chatter (about 4m tweets per day), collected by the twitter stream api for the keywords "covd19", "coronaviruspandemic", "covid-19", "2019ncov", "coronaoutbreak", "coronavirus", and "wuhanvirus". in addition, the panacea lab extended the dataset over time with twitter datasets of research groups from the universitat autònoma de barcelona, the national research university higher school of economics, and the kazan federal university. as of june 14, 2020, version 14.0 of the dataset contains over 400 million unique tweets. since the data has been collected irrespective of language, all languages are covered, with a pronounced prevalence of english (60%), spanish (16%), and french (4%) tweets. an update is provided every two days and a cumulative update is provided every week. we use the cleaned version of the dataset provided by the panacea lab without retweets. ## methods and results in the following section we describe the different methods used by our platform. we start by creating a randomly selected subset of the panacea lab's covid-19 twitter dataset. 5 we then process this subset with the preprocessing steps described below and apply the methods for the data analysis as described in this section. subsequently, we store the obtained results and the original data subset in a database accessed by our interactive web platform for visualization of the results, as illustrated in figure 2 . ## conclusion and future work in this paper, we have presented the first version of our interactive web platform for the aggregation and visualization of different methods for social media mining regarding covid-19. concerning social media mining, one of the biggest challenges in the analysis of emerging pandemics such as covid-19 is that we initially have a very limited number of supervised datasets. therefore, the selected methods applied to our platform are either unsupervised or have been previously trained on other social media datasets. we have demonstrated that by the combined visualization of different methods, underlying connections in the data can be revealed. in particular, we aimed to make the interactive web platform comprehensible for both a specialist audience and the general public. as future work we intend to move the system to a more capable platform in order to be able to include a much larger number of tweets, and to add novel capabilities, such as the detection of tweets generated by bots as opposed to those generated by humans, through the usage of a tool such as the botometer. 13