TY - GEN
T1 - Natural vs. Artificially Sweet Tweets
T2 - AAAI International Workshop on Health Intelligence, W3PHIAI 2020
AU - Batan, Hande
AU - Radpour, Dianna
AU - Kehlbacher, Ariane
AU - Klein-Seetharaman, Judith
AU - Paul, Michael J.
N1 - Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This ongoing project aims to use social media data to study consumer behaviors regarding natural and artificial sweeteners Following the recent shifts to natural sweeteners such as Stevia versus artificial, and traditionally-used ones like aspartame in recent years, there has been discussion around potential negative side effects, including memory loss and other chronic illnesses. These issues are discussed on Twitter, and we hypothesize that Twitter may provide insights into how people make nutritional decisions about the safety of sweeteners given the inconclusive science surrounding the topic, how factors such as risk and consumer attitude are interrelated, and how information and misinformation about food safety is shared on social media. As an initial step, we describe a new dataset containing 308,738 de-duplicated English-language tweets spanning multiple years. We conduct a topic model analysis and characterize tweet volumes over time, showing a diversity of sweetener-related content and discussion. Our findings suggest a variety of research questions that these data may support.
AB - This ongoing project aims to use social media data to study consumer behaviors regarding natural and artificial sweeteners Following the recent shifts to natural sweeteners such as Stevia versus artificial, and traditionally-used ones like aspartame in recent years, there has been discussion around potential negative side effects, including memory loss and other chronic illnesses. These issues are discussed on Twitter, and we hypothesize that Twitter may provide insights into how people make nutritional decisions about the safety of sweeteners given the inconclusive science surrounding the topic, how factors such as risk and consumer attitude are interrelated, and how information and misinformation about food safety is shared on social media. As an initial step, we describe a new dataset containing 308,738 de-duplicated English-language tweets spanning multiple years. We conduct a topic model analysis and characterize tweet volumes over time, showing a diversity of sweetener-related content and discussion. Our findings suggest a variety of research questions that these data may support.
KW - Nutrition
KW - Public health
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85097052131&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097052131&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-53352-6_16
DO - 10.1007/978-3-030-53352-6_16
M3 - Conference contribution
AN - SCOPUS:85097052131
SN - 9783030533519
T3 - Studies in Computational Intelligence
SP - 179
EP - 185
BT - Explainable AI in Healthcare and Medicine - Building a Culture of Transparency and Accountability
A2 - Shaban-Nejad, Arash
A2 - Michalowski, Martin
A2 - Buckeridge, David L.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 February 2020 through 7 February 2020
ER -