Abstract
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.
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References
Abbar, S., Mejova, Y., Weber, I.: You tweet what you eat: studying food consumption through Twitter. In: Conference on Human Factors in Computing Systems (CHI) (2015)
Ayers, J.W., Althouse, B.M., Johnson, M., Dredze, M., Cohen, J.E.: What’s the healthiest day? circaseptan (weekly) rhythms in healthy considerations. Am. J. Prev. Med. 47(1), 73–76 (2014)
Blei, D.M., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
De Choudhury, M., Sharma, S., Kiciman, E.: Characterizing dietary choices, nutrition, and language in food deserts via social media. In: Conference on Computer Supported Cooperative Work and Social Computing (CSCW). pp. 1157–1170. ACM, New York (2016). 10.1145/2818048.2819956, http://doi.acm.org/10.1145/2818048.2819956
Fried, D., Surdeanu, M., Kobourov, S., Hingle, M., Bell, D.: Analyzing the language of food on social media. In: IEEE International Conference on Big Data (2014)
Kusmierczyk, T., Trattner, C., Norvag, K.: Temporality in online food recipe consumption and production. In: International Conference on World Wide Web (WWW) (2015)
Mejova, Y., Haddadi, H., Noulas, A., Weber, I.: #foodporn: obesity patterns in culinary interactions. In: International Conference on Digital Health, pp. 51–58 (2015)
Nguyen, C.Q., Li, D., Meng, H.W., Kath, S., Nsoesie, E., Li, F., Wen, M.: Building a national neighborhood dataset from geotagged Twitter data for indicators of happiness, diet, and physical activity. JMIR Public Health Surveill. 2(2), e158 (2016). https://doi.org/10.2196/publichealth.5869, http://www.ncbi.nlm.nih.gov/pubmed/27751984
Pagoto, S., Schneider, K.L., Evans, M., Waring, M.E., Appelhans, B., Busch, A.M., Whited, M.C., Thind, H., Ziedonis, M.: Tweeting it off: characteristics of adults who tweet about a weight loss attempt. J. Am. Med. Inform. Assoc. 21(6), 1032–1037 (2014)
Paul, M.J., Dredze, M.: Social monitoring for public health. In: Synthesis Lectures on Information Concepts, Retrieval, and Services, pp. 1–185. Morgan & Claypool (2017)
Pawar, R., Krynitsky, A., Rader, J.: Sweeteners from plants-with emphasis on Stevia rebaudiana (bertoni) and Siraitia grosvenorii (swingle). Anal. Bioanal. Chem. 405, 4397–4407 (2013)
Sharma, S., De Choudhury, M.: Detecting and characterizing nutritional information of food and ingestion content in Instagram. In: International Conference on World Wide Web (WWW) (2015)
West, R., White, R.W., Horvitz, E.: From cookies to cooks: Insights on dietary patterns via analysis of web usage logs. In: International Conference on World Wide Web (WWW). Republic and Canton of Geneva, Switzerland (2013)
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Batan, H., Radpour, D., Kehlbacher, A., Klein-Seetharaman, J., Paul, M.J. (2021). Natural vs. Artificially Sweet Tweets: Characterizing Discussions of Non-nutritive Sweeteners on Twitter. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_16
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DOI: https://doi.org/10.1007/978-3-030-53352-6_16
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