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Haupt MR, Chiu M, Chang J, Li Z, Cuomo R, Mackey TK. Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding. PLoS One 2023; 18:e0295414. [PMID: 38117843 PMCID: PMC10732406 DOI: 10.1371/journal.pone.0295414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/21/2023] [Indexed: 12/22/2023] Open
Abstract
The spread of misinformation and conspiracies has been an ongoing issue since the early stages of the internet era, resulting in the emergence of the field of infodemiology (i.e., information epidemiology), which investigates the transmission of health-related information. Due to the high volume of online misinformation in recent years, there is a need to continue advancing methodologies in order to effectively identify narratives and themes. While machine learning models can be used to detect misinformation and conspiracies, these models are limited in their generalizability to other datasets and misinformation phenomenon, and are often unable to detect implicit meanings in text that require contextual knowledge. To rapidly detect evolving conspiracist narratives within high volume online discourse while identifying nuanced themes requiring the comprehension of subtext, this study describes a hybrid methodology that combines natural language processing (i.e., topic modeling and sentiment analysis) with qualitative content coding approaches to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as 'X'). Discourse that focused on correcting 5G conspiracies was also analyzed for comparison. Sentiment analysis shows that conspiracy-related discourse was more likely to use language that was analytic, combative, past-oriented, referenced social status, and expressed negative emotions. Corrections discourse was more likely to use words reflecting cognitive processes, prosocial relations, health-related consequences, and future-oriented language. Inductive coding characterized conspiracist narratives related to global elites, anti-vax sentiment, medical authorities, religious figures, and false correlations between technology advancements and disease outbreaks. Further, the corrections discourse did not address many of the narratives prevalent in conspiracy conversations. This paper aims to further bridge the gap between computational and qualitative methodologies by demonstrating how both approaches can be used in tandem to emphasize the positive aspects of each methodology while minimizing their respective drawbacks.
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Affiliation(s)
- Michael Robert Haupt
- Department of Cognitive Science, University of California San Diego, La Jolla, California, United States of America
- Global Health Policy & Data Institute, San Diego, California, United States of America
| | - Michelle Chiu
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, United States of America
| | - Joseline Chang
- Rady School of Management, University of California San Diego, La Jolla, California, United States of America
| | - Zoe Li
- Global Health Policy & Data Institute, San Diego, California, United States of America
- S-3 Research, San Diego, California, United States of America
| | - Raphael Cuomo
- Department of Anesthesiology, University of California, San Diego School of Medicine, San Diego, California, United States of America
| | - Tim K. Mackey
- S-3 Research, San Diego, California, United States of America
- Global Health Program, Department of Anthropology, University of California, San Diego, California, United States of America
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Kumar N, Chen K, Shi Y, Altice FL. Online platforms' framing around vaping. Drug Test Anal 2023; 15:1297-1302. [PMID: 36445242 DOI: 10.1002/dta.3417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 11/15/2022] [Accepted: 11/22/2022] [Indexed: 12/02/2022]
Abstract
In this paper, we provide a descriptive overview of how vaping is framed differently between various online platforms (Wikipedia, Quora, Medium, Reddit, Stack Exchange, wikiHow, Facebook, and online news media). We provide an overview of >1 million posts and news articles about vaping to study the differences in framing between online platforms. Findings indicate an inconsistent framing around vaping across platforms. Stakeholders may utilize our findings to intervene around the framing of vaping and may design communications campaigns that improve the way society sees vaping, possibly aiding smoking cessation and reducing youth vaping.
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Affiliation(s)
- Navin Kumar
- Yale School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Keyu Chen
- Yale School of Medicine, Yale University, New Haven, Connecticut, USA
- Internal Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, USA
| | - Yiwen Shi
- Internal Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, USA
- Internal Medicine, Yale University, New Haven, Connecticut, USA
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