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CoviDash-SM: A PUBLIC COVID19 DASHBOARD FOR SOCIAL MEDIA DATA-BASED
RESEARCH AND SURVEILLANCE
Sahithi Lakamana, MS, Mohammed Ali Al-Garadi, PhD, Yuan-Chi Yang, PhD, Abeed Sarker, PhD
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322
BACKGROUND
Social media is of particularly high
-
utility during the COVID19 pandemic
as it has become the primary mode
of communication for many.
Social media is rich with COVID19
-
related information, which can be
characterized and curated via natural
language processing (NLP) and
machine learning methods.
Our dashboards (global & US) will
provide aggregated statistics derived
automatically
via NLP methods.
SELF
-REPORT DETECTION
Global
Data Collected: 346,179,203
Self Reports: 167,845
United States
Data Collected: 1,522,833
Self Reports: 3,505
DASHBOARD LINKS & CONTACTS
Dashboards can be accessed at:
https://sarkerlab.org/covid_sm_data
_bundle/
Abeed
Sarker:
abeed.sarker@emory.edu
Sahithi Lakamana:
sahithi.krishnaveni.lakamana@emory
.edu
Sentiment Analysis Syndromic SurveillanceTopic Modelling
GEOSPATIAL INFORMATION