TY - JOUR
T1 - Sentiment Analysis of Tweets on Soda Taxes
AU - An, Ruopeng
AU - Yang, Yuyi
AU - Batcheller, Quinlan
AU - Zhou, Qianzi
N1 - Publisher Copyright:
© 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - Context: As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. Objective: This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. Design: We designed a search algorithm to systematically identify and collect soda tax-related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. Setting: Computer modeling. Participants: Approximately 370 000 soda tax-related tweets posted on Twitter from January 1, 2015, to April 16, 2022. Main Outcome Measure: Sentiment associated with a tweet. Results: Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax-related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. Conclusions: Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.
AB - Context: As a primary source of added sugars, sugar-sweetened beverage (SSB) consumption may contribute to the obesity epidemic. A soda tax is an excise tax charged on selling SSBs to reduce consumption. Currently, 8 cities/counties in the United States have imposed soda taxes. Objective: This study assessed people's sentiments toward soda taxes in the United States based on social media posts on Twitter. Design: We designed a search algorithm to systematically identify and collect soda tax-related tweets posted on Twitter. We built deep neural network models to classify tweets by sentiments. Setting: Computer modeling. Participants: Approximately 370 000 soda tax-related tweets posted on Twitter from January 1, 2015, to April 16, 2022. Main Outcome Measure: Sentiment associated with a tweet. Results: Public attention paid to soda taxes, indicated by the number of tweets posted annually, peaked in 2016, but has declined considerably ever since. The decreasing prevalence of tweets quoting soda tax-related news without revealing sentiments coincided with the rapid increase in tweets expressing a neutral sentiment toward soda taxes. The prevalence of tweets expressing a negative sentiment rose steadily from 2015 to 2019 and then slightly leveled off, whereas that of tweets expressing a positive sentiment remained unchanged. Excluding news-quoting tweets, tweets with neutral, negative, and positive sentiments occupied roughly 56%, 29%, and 15%, respectively, during 2015-2022. The authors' total number of tweets posted, followers, and retweets predicted tweet sentiment. The finalized neural network model achieved an accuracy of 88% and an F1 score of 0.87 in predicting tweet sentiments in the test set. Conclusions: Despite its potential to shape public opinion and catalyze social changes, social media remains an underutilized source of information to inform government decision making. Social media sentiment analysis may inform the design, implementation, and modification of soda tax policies to gain social support while minimizing confusion and misinterpretation.
KW - machine learning
KW - neural network
KW - social media
KW - soda tax
KW - sugar-sweetened beverage
KW - tweet
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85165521618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165521618&partnerID=8YFLogxK
U2 - 10.1097/PHH.0000000000001721
DO - 10.1097/PHH.0000000000001721
M3 - Article
C2 - 36812042
AN - SCOPUS:85165521618
SN - 1078-4659
VL - 29
SP - 633
EP - 639
JO - Journal of Public Health Management and Practice
JF - Journal of Public Health Management and Practice
IS - 5
ER -