Online Discussions About Tinnitus: What Can We Learn From Natural Language Processing of Reddit Posts?

Author:

Manchaiah Vinaya12345ORCID,Londero Alain6,Deshpande Aniruddha K.78,Revel Manon9,Palacios Guillaume10,Boyd Ryan L.111213,Ratinaud Pierre14

Affiliation:

1. Department of Otolaryngology–Head and Neck Surgery, University of Colorado School of Medicine, Aurora

2. UCHealth Hearing and Balance, University of Colorado Hospital, Aurora

3. Virtual Hearing Lab, Collaborative Initiative between University of Colorado School of Medicine and University of Pretoria, Aurora, CO

4. Department of Speech-Language Pathology and Audiology, University of Pretoria, Gauteng, South Africa

5. Department of Speech and Hearing, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India

6. Hôpital Européen Georges Pompidou, Assistance Publique – Hôpitaux de Paris; Faculté de Médecine Paris Descartes – Université de Paris, France

7. Department of Speech-Language-Hearing Sciences, Hofstra University, Long Island, NY

8. Long Island Doctor of Audiology Consortium, Garden City, NY

9. Institute for Data, Systems and Society, Massachusetts Institute of Technology, Cambridge

10. PainkillAR, TELECOM ParisTech, France

11. Department of Psychology, Lancaster University, United Kingdom

12. Security Lancaster, Lancaster University, United Kingdom

13. Data Science Institute, Lancaster University, United Kingdom

14. Laboratory of Applied Studies and Research in Social Sciences, University of Toulouse, France

Abstract

Background: This study was aimed at identifying key topics in online discussions about tinnitus by examining a large data set extracted from Reddit social media using a natural language processing technique. Method: A corpus of 113,215 posts about tinnitus was extracted from Reddit's application programming interface. After cleaning the data for duplications and posts without any text information, the sample was reduced to 101,905 posts, which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the co-occurrence of texts. These clusters were named by a panel of tinnitus experts ( n = 9) by reading typical text segments within each cluster. Results: The cluster analysis identified 16 unique clusters that belong to two topics, which were named “tinnitus causes and consequences” and “tinnitus management and coping.” Based on their characteristics, the clusters were named: tinnitus timeline (10%), tinnitus perception (9.7%), medical triggers and modulators (8.8%), hearing research (8.8%), attention and silence (8.6%), social media posts about tinnitus (7.4%), hearing protection (7.3%), interaction with hearing health care providers (6.7%), mental health and coping (5.8%), music listening (5.7%), hope for a cure (5.6%), interactions with people without tinnitus (5.4%), dietary supplements and alternative therapies (3.2%), sleep (3.9%), dietary effects (1.7%), and writing about tinnitus and being thankful to online community (1.4%). Conclusions: Despite some limitations, tinnitus posts on Reddit provide rich real-world data to identify various issues and complaints that tinnitus patients and their significant others discuss in online communities. Some of the clusters identified here are novel (e.g., tinnitus timeline, interactions with people without tinnitus) and have not been much discussed in the tinnitus literature. The results suggest that individuals with tinnitus relay on social media for support and highlight the service delivery needs in providing social support through other means (e.g., support groups).

Publisher

American Speech Language Hearing Association

Subject

General Medicine

Reference33 articles.

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