Discourse about human papillomavirus (HPV)-associated oropharyngeal cancer (OPC) on Twitter: Lessons for public health education about OPC and dental care

Chung, Jae Eun; Mustapha, Indra Z.; Jiang, Li; Gu, Xing

doi:10.1016/j.puhip.2022.100239

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Natural language processing technology promotes text analysis based on social media comments [39]; this technology can learn the deeper semantic features of the comment text and the features that are consistent with the current context, according to different training corpus, to input a better text vector representation for downstream classification tasks. Some researchers have used large-scale pretrained language models [40], global matrix decomposition [41], and local context windows [42] for text vector representation. Local context windows are more suitable for semantically aggregating AR risk factors [43].…”

Section: Word Embedding and Text Classification Based On Deep Learningmentioning

confidence: 99%

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Gu,

Wang,

Chai

et al. 2024

J Med Internet Res

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Background Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. Objective This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? Methods This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. Results Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body’s immune system and lead to the development of allergies. Conclusions Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.

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Section: Word Embedding and Text Classification Based On Deep Learningmentioning

confidence: 99%

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Gu,

Wang,

Chai

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Twitter data has been useful in helping with public health activities such as disease monitoring, detection of incidents, pharmaceutical surveillance, predictions, disease monitoring, and geographical verification [ 17 ]. Previous studies have shown that material published on social media sites such as Twitter provides vital insights into how people know and think about health topics [ 18 , 19 ]. Different studies suggest that Twitter as a platform can raise awareness of oral health and oral cancers.…”

Section: Introductionmentioning

confidence: 99%

Oral Cancer and Twitter: An Analysis of Oral Cancer Awareness Month Tweets

Binmadi

2024

Cureus

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Objective: The objective of this research was to assess Twitter usage during Oral Cancer Awareness Month and explore the content and engagement related to oral cancer. Methods: A comprehensive search was performed using relevant hashtags and keywords related to oral cancer on Twitter throughout the oral cancer awareness month, April 2022. All extracted tweets that match the inclusion criteria were analyzed for content, users were classified, and their countries were identified. Result: A total of 5551 English tweets were identified during Oral Cancer Awareness Month, and 5543 were included in the analysis covering a wide range of oral cancer-related topics. The analyzed tweets encompassed a diverse range of topics, from cancer and oral health to oncology, cancer research, cancer awareness, and even discussions related to alcohol. We found that the majority of users who post on Twitter were individuals. The most common tweets were posted from the USA. Conclusions: This study provides an analysis of Twitter activity during Oral Cancer Awareness Month, highlighting the diverse range of content being shared, offering valuable insights. The findings demonstrate the importance of leveraging social media platforms to disseminate information and raise awareness. With a strategic approach to social media, organizations and individuals worldwide have the power to amplify their message, attract attention, and effectively advocate for oral cancer awareness.

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Discourse about human papillomavirus (HPV)-associated oropharyngeal cancer (OPC) on Twitter: Lessons for public health education about OPC and dental care

Cited by 2 publications

References 31 publications

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Oral Cancer and Twitter: An Analysis of Oral Cancer Awareness Month Tweets

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