Program

In-person AND virtual! – We are pioneering a new conference format that will connect in-person and virtual audiences to create a unique experience. Matching the innovation that comes through our members’ work, ISPOR is pushing the boundaries of innovation to design an event that works in today’s quickly changing environment. 

In-person registration included the full virtual experience, and virtual-only attendees will be able to tune into live in-person sessions and/or watch captured in-person sessions on-demand in addition to having a variety of virtual-only sessions to attend.

Using Twitter to Examine Public Perceptions about COVID-19 in the United States: A Sentiment Analysis

Speaker(s)

Ali AA1, Adjei K1, Fatimah S1, Ezendu K2, Taeb M1, Chi H1, King CD1, Diaby V3
1Florida A & M University, Tallahassee, FL, USA, 2Florida A&M University, Tallahassee, FL, USA, 3Otsuka Pharmaceutical, Gainesville, FL, USA

OBJECTIVES: Nowadays, social media has become the primary news source for Americans, rendering its data a valuable vehicle to examine how citizens feel about the health threats brought about by the COVID-19 pandemic. We examined risk perceptions of the public about the COVID-19 in the United States using Twitter data.

METHODS: We conducted a sentiment analysis of COVID-19 pandemic-related tweets, queried from the Twitter Historical PowerTrack between January 20, 2020, to August 27, 2020. Tweets were divided into basic units (tokens), which were passed to a sentiment classifier (Python’s scikit-learn’s logistic regression). The latter classified tweet sentiments as positive, negative, or neutral by assigning them a polarity.

RESULTS: A total of 229,388 COVID-19 pandemic related tweets were analyzed. Popular bigrams included “COVID-19,” “coronavirus,” “social distancing,” “death” and “new cases.” The polarity of the tweets was: 13.6% (30,131) positive sentiments, 85% (196,034) negative sentiments and 1.4% (3,223) neutral.

CONCLUSION: Our study demonstrates the importance and usefulness of Twitter data along with natural language processing techniques to capture an individuals’ risk perceptions about the COVID-19 in real-time. These findings can assist policymakers to better handle uncertainty in the COVID-19 response and mitigate specific public concerns about the pandemic.

Code

PCR77

Topic

Patient-Centered Research

Topic Subcategory

Patient-reported Outcomes & Quality of Life Outcomes

Disease

Infectious Disease (non-vaccine)