Real-time Classification, Geolocation and Interactive Visualization of COVID -19 Information Shared on Social Media to Better Understand Global Developments (2020)

Abstract: As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.

## introduction as the covid-19 pandemic continues to rapidly evolve on a global scale, members of the public and crisis managers alike need access to digestible, useful, and up-to-date information. such information is crucial in enabling informed and responsive decision making that minimizes risk and mitigates harm caused by the virus. throughout crises, social media platforms in particular have been invaluable to crisis managers as they provide unparalleled situational awareness through grassroots citizens reporting @xcite . furthermore, various organizations and individuals share the most recent and relevant information on social media: enabling affected people to stay informed, and crisis managers to measure outreach effectiveness @xcite . however, just as social media can empower the effective dissemination of useful information, it also enables the spread of harmful misinformation. in fact, the signal-to-noise ratio of information shared on social media is a well-known problem @xcite , and misinformation has been a pervasive problem throughout the covid-19 pandemic @xcite . extracting up-to-date, useful, and digestible information from social media platforms also has its own challenges: to address these challenges, we built a prototype dashboard that classifies and geolocates covid-19 tweets in real-time, making it easier to explore useful and up-to-date information in a digestible and intuitive way (figure 1 ). the primary components of our system are: ## related work the real-time visualization of classified and geolocated tweets on an interactive map has been demonstrated before. for example, @xcite use a naive bayes classifier to identify tweets about news, and statistical ner combined with a gazetteer to geoparse tweets by extracting and resolving location names (toponyms) from their content. @xcite apply a similar geoparsing approach to tweets from natural disasters, as they find less than 1% of tweets are geotagged. however, due to the highly local nature of natural disasters, they eschew ner in favor of simple string matching with a predetermined set of valid toponyms. furthermore, they do not perform tweet classification. in contrast, @xcite classify tweets by disaster type, but do not perform geoparsing, relying on geotagged tweets instead. similar other works classify, geoparse, and map tweets in realtime during various crises @xcite @xcite @xcite . to the best of our knowledge, we are the first to combine, in a real-time tweet mapping interface, both neural ner geoparsing and multi-class classification for a single event type of global scale. we present a prototype dashboard for the real-time classification, geolocation and interactive visualization of covid-19 information shared on social media. we design our system to make it as easy as possible for users to find useful and up-to-date information in a digestible format, addressing fundamental issues in extracting valuable information from social media during crises. more specifically, we leverage natural language processing to geolocate a portion of the large amount of non-geotagged tweets we capture, helping users find geographically relevant information. we also fine-tune a novel l2 classification layer to categorize tweets in useful labels, helping users structure and navigate the large volume of tweets we capture. lastly, we also show our novel l2 classification layer surpasses traditional linear layers in terms of accuracy and per-class f1 scores on a dataset of respiratory virus tweets. however, following reviewer feedback we find that this improvement is not consistent on other datasets, suggesting the need for future research to better understand the factors at play. ## future work our dashboard prototype has several opportunities for improvement. these make interesting potential avenues for future research in natural language processing and include: