Literature DB >> 36112977

Opinions on Homeopathy for COVID-19 on Twitter.

Jeevith Bopaiah1, Kiran Garimella2, Ramakanth Kavuluru3.   

Abstract

Homeopathy is a medical system originating in Germany more than 200 years ago. Based on prior investigations, mainstream health agencies and medical research communities indicate that there is little evidence that homeopathy can be an effective treatment for any specific health condition. However, it continues to be practiced as a popular form of alternative medicine in many countries, even during the ongoing COVID-19 pandemic. In this paper, we mine opinions on homeopathy for COVID-19 expressed in Twitter data. Our experiments are conducted with a dataset of nearly 60K tweets collected during a seven month period ending in July 2020. We first built text classifiers (linear and neural models) to mine opinions on homeopathy (positive, negative, neutral) from tweets using a dataset of 2400 hand-labeled tweets obtaining an average macro F-score of 81.5% for the positive and negative classes. We applied this model to identify opinions from the full dataset. Our results show that the number of unique positive tweets is twice that of the number of unique negative tweets; but when including retweets, there are 23% more negative tweets overall indicating that negative tweets are getting more retweets and better traction on Twitter. Using a word shift graph analysis on the Twitter bios of authors of positive and negative tweets, we observe that opinions on homeopathy appear to be correlated with political/religious ideologies of the authors (e.g., liberal vs nationalist, atheist vs Hindu). To our knowledge, this is the first study to analyze public opinions on homeopathy on any social media platform. Our results surface a tricky landscape for public health agencies as they promote evidence-based therapies and preventative measures for COVID-19.

Entities:  

Keywords:  COVID-19; homeopathy; opinion mining; social media

Year:  2022        PMID: 36112977      PMCID: PMC9472594          DOI: 10.1145/3501247.3531575

Source DB:  PubMed          Journal:  Proc ACM Web Sci Conf


  18 in total

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5.  Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic.

Authors:  Abdullah Bin Shams; Ehsanul Hoque Apu; Ashiqur Rahman; Md Mohsin Sarker Raihan; Nazeeba Siddika; Rahat Bin Preo; Molla Rashied Hussein; Shabnam Mostari; Russell Kabir
Journal:  Healthcare (Basel)       Date:  2021-02-03

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Authors: 
Journal:  Lancet Infect Dis       Date:  2020-07-17       Impact factor: 25.071

7.  Partisan public health: how does political ideology influence support for COVID-19 related misinformation?

Authors:  Nicholas Francis Havey
Journal:  J Comput Soc Sci       Date:  2020-11-02

8.  An Emotion Care Model using Multimodal Textual Analysis on COVID-19.

Authors:  Vedika Gupta; Nikita Jain; Piyush Katariya; Adarsh Kumar; Senthilkumar Mohan; Ali Ahmadian; Massimiliano Ferrara
Journal:  Chaos Solitons Fractals       Date:  2021-01-22       Impact factor: 5.944

9.  Prevalence of Health Misinformation on Social Media: Systematic Review.

Authors:  Victor Suarez-Lledo; Javier Alvarez-Galvez
Journal:  J Med Internet Res       Date:  2021-01-20       Impact factor: 5.428

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Authors:  S Anne Moorhead; Diane E Hazlett; Laura Harrison; Jennifer K Carroll; Anthea Irwin; Ciska Hoving
Journal:  J Med Internet Res       Date:  2013-04-23       Impact factor: 5.428

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