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. 2023 Jun 16:2023:261-270.
eCollection 2023.

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media

Yuting Guo  1 , Swati Rajwal  2 , Sahithi Lakamana  1 , Chia-Chun Chiang  3 , Paul C Menell  3 , Adnan H Shahid  3 , Yi-Chieh Chen Pharm D  4 , Nikita Chhabra  5 , Wan-Ju Chao  6 , Chieh-Ju Chao  7 , Todd J Schwedt  5 , Imon Banerjee  8   9 , Abeed Sarker  1
Affiliations

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media

Yuting Guo et al. AMIA Jt Summits Transl Sci Proc. .

Abstract

Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by those with migraine; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts, and used them for training and evaluating supervised machine learning methods. Our best system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

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Figures

Figure 1.
Figure 1.
The framework of our generalizable NLP system—model development and validation on Twitter data followed by additional evaluation on Reddit posts.
Figure 2.
Figure 2.
Examples from the bias analysis. The green color signifies positive attention to the words while red shows negative.
Figure 3.
Figure 3.
The normalized sentiment distributions of the medications: (a) Twitter and (b) Reddit.
See this image and copyright information in PMC

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