Cancer Through the Lens of Social Media: An Examination of Cancer-Related Content on Reddit

Rao, Shreeya Sudhesh; Vibha,; Shitij,; Kamath, Giridhar B; Rao, Suchetha

doi:10.1007/978-981-19-5331-6_35

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The public commonly shares their opinions, concerns, symptoms, treatments, and adverse reactions to medications on social media such as Twitter [7][8] and Facebook and news aggregators such as Reddit [9] [10]. These highly invaluable data contain trends, and latent themes that may be significantly important to aid public policymakers and governments in prioritizing their strategies to help combat the outbreaks [11][12] [13]. To the best of the authors' knowledge, very few early studies reported in the public health literature analyze public discourse and sentiment towards growing measles outbreak [14] [15].…”

Section: Introductionmentioning

confidence: 99%

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

Anoop

2023

Preprint

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Objectives: This study employs text mining and natural language processing approaches for analyzing and unearthing public discourse and sentiment towards the recent spiking Measles outbreaks reported across the globe. Study design: A detailed qualitative study was designed using text mining and natural language processing on the user-generated comments from Reddit, a social news aggregation and discussion website. Methods: A detailed analysis was conducted using topic modeling and sentiment analysis on Reddit comments (n=87203) posted between October 1 and December 15, 2022. Topic modeling was used to leverage major themes related to the Measles health emergency and public discourse; the sentiment analysis was performed to check how the general public responded to different aspects of the outbreak. Results: Our results revealed several intriguing and helpful themes, including parental concerns, anti-vaxxer discussions, and measles symptoms from the user-generated content. The results further confirm that even though there have been administrative interventions to promote vaccinations that affirm the parents' concerns to a greater extent, the anti-vaccination or vaccine hesitancy prevalent in the general public reduces the effect of such intercessions. Conclusions: A proactive analysis of public discourse and sentiments during health emergencies and disease outbreaks is vital. This study effectively explored public perceptions and sentiments to assist health policy researchers and stakeholders in making informed and data-driven decisions.}

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Section: Introductionmentioning

confidence: 99%

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

Anoop

2023

Preprint

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We Chased COVID-19; Did We Forget Measles? - Public Discourse and Sentiment Analysis on Spiking Measles Cases Using Natural Language Processing

Anoop¹,

Govindarajan

2023

Lecture Notes in Computer Science

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Research report: BCG therapy for bladder cancer: Exploring patient experiences and concerns through artificial intelligence-based social media analysis

Khene,

Tachibana,

Bhanvadia

et al. 2024

Bladder Cancer

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Background There is a notable disparity between the guidelines for BCG therapy in non-muscle invasive bladder cancer (NMIBC). Reddit has emerged as a popular online platform for individuals seeking information and exchanging their experiences related to bladder cancer. Objective To investigate and classify public opinions about intravesical BCG therapy as shared on Reddit, a popular social media platform. Methods This study employed an artificial intelligence-based approach to examine discussions related to intravesical BCG therapy on a social media platform over the past ten years. An artificial intelligence framework was developed to categorize these conversations into distinct topics and thematic categories. This framework included a partially supervised model for processing natural language (using BERT [Bidirectional Encoder Representations from Transformers]), a method for reducing data complexity, and an algorithm for clustering. Additionally, each conversation was assessed for sentiment. Results A total of 1223 unique discussions related to BCG therapy were analyzed, comprising 110 unique posts and 1113 comments from 268 distinct authors. We identified four overarching thematic groups: 1) BCG administration procedures, (2) hesitancy in initiating or maintaining BCG treatment, (3) issues related to BCG shortage and alternative treatments, and (4) side effects of BCG treatment. Sentiment analysis of the 1223 discussions revealed that 25.2% (308) exhibited a negative sentiment, 58.3% (713) were neutral, and 16.5% (202) showed a positive sentiment. Conclusion Online social media often contains detailed personal experiences with BCG therapy, not commonly found in medical literature. Understanding these experiences can help medical professionals improve care and treatment adherence in NMIBC.

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Cancer Through the Lens of Social Media: An Examination of Cancer-Related Content on Reddit

Cited by 3 publications

References 16 publications

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

We Chased COVID-19; Did We Forget Measles? - Public Discourse and Sentiment Analysis on Spiking Measles Cases Using Natural Language Processing

Research report: BCG therapy for bladder cancer: Exploring patient experiences and concerns through artificial intelligence-based social media analysis

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