Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches

2021 ◽  
Vol 58 (1) ◽  
pp. 102390
Author(s):  
Yuehua Zhao ◽  
Jingwei Da ◽  
Jiaqi Yan
Keyword(s):  
Machine Learning ◽  
Online Health Communities ◽  
Behavioral Features ◽  
Health Communities

scholarly journals Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

2022 ◽  
Author(s):  
Renáta Németh ◽  
Fanni Máté ◽  
Eszter Katona ◽  
Márton Rakovics ◽  
Domonkos Sik
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Data Science ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Online Health Communities ◽  
Health Communities ◽  
Open Question

AbstractSupervised machine learning on textual data has successful industrial/business applications, but it is an open question whether it can be utilized in social knowledge building outside the scope of hermeneutically more trivial cases. Combining sociology and data science raises several methodological and epistemological questions. In our study the discursive framing of depression is explored in online health communities. Three discursive frameworks are introduced: the bio-medical, psychological, and social framings of depression. ~80 000 posts were collected, and a sample of them was manually classified. Conventional bag-of-words models, Gradient Boosting Machine, word-embedding-based models and a state-of-the-art Transformer-based model with transfer learning, called DistilBERT were applied to expand this classification on the whole database. According to our experience ‘discursive framing’ proves to be a complex and hermeneutically difficult concept, which affects the degree of both inter-annotator agreement and predictive performance. Our finding confirms that the level of inter-annotator disagreement provides a good estimate for the objective difficulty of the classification. By identifying the most important terms, we also interpreted the classification algorithms, which is of great importance in social sciences. We are convinced that machine learning techniques can extend the horizon of qualitative text analysis. Our paper supports a smooth fit of the new techniques into the traditional toolbox of social sciences.

scholarly journals IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1469 ◽  
Author(s):  
Pradeepa Sampath ◽  
Gayathiri Packiriswamy ◽  
Nishmitha Pradeep Kumar ◽  
Vimal Shanmuganathan ◽  
Oh-Young Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Learning Algorithm ◽  
Vital Role ◽  
Online Health Communities ◽  
Health Community ◽  
Chi Squared ◽  
Health Related ◽  
Health Communities ◽  
Processing Techniques

The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.

scholarly journals Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

2019 ◽  
Vol 28 (01) ◽  
pp. 208-217 ◽  
Author(s):  
Mike Conway ◽  
Mengke Hu ◽  
Wendy W. Chapman
Keyword(s):  
Public Health ◽  
Mental Health ◽  
Machine Learning ◽  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Online Health Communities ◽  
Machine Learning Methods ◽  
Health Communities ◽  
Health Applications

Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.

Protecting privacy and confidentiality in online health communities

2013 ◽  
Author(s):  
Jacqueline Amoozegar ◽  
Douglas Rupert ◽  
Jennifer Gard Read ◽  
Rebecca Moultrie ◽  
Kathryn Aikin ◽  
...  
Keyword(s):  
Online Health Communities ◽  
Health Communities ◽  
Privacy And Confidentiality

E-HEALTH AND ONLINE HEALTH COMMUNITIES IN THE DAILY LIVES OF OLDER RURAL RESIDENTS DURING THE PANDEMIC

2021 ◽  
pp. 538-544
Author(s):  
К. А. Галкин
Keyword(s):  
Older People ◽  
Medical Care ◽  
Rural Areas ◽  
Computer Literacy ◽  
Self Care ◽  
Leningrad Region ◽  
Rural Medicine ◽  
Online Health Communities ◽  
Health Communities

Ситуация пандемии COVID-19 в очередной раз напомнила о необходимости использования онлайн-сообществ здоровья, особенно в тех районах, где не хватает мест в местных больницах или существуют проблемы с получением качественной медицинской помощи. Это, например, сельские районы, где медицина ориентирована на лечение экстренно возникающих заболеваний и у врачей существуют сложности с возможностью лечения новой коронавирусной инфекции. Онлайн-сообщества здоровья в таком случае предоставляют возможность узнать необходимую информацию, а также общаться со специалистами, которые знают особенности нового коронавируса и могут дать необходимые советы. В настоящей статье на примере глубинных интервью с пожилыми людьми из сёл Ленинградской обл. и Республики Карелия рассмотрена роль телемедицины для пожилых людей и общения в онлайн-сообществах здоровья в контексте преодоления одиночества и изолированности, которая существует в сельской местности. В статье проанализированы особенности и основные препятствия для использования пожилыми людьми телемедицины и общения в онлайнсообществах здоровья - это проблемы с инфраструктурой и отключением электричества, отсутствие у пожилых людей компьютерной грамотности для общения и взаимодействия в онлайн-сообществах здоровья. Роль последних рассмотрена с точки зрения развития самозаботы пожилых людей в сельской местности в периферийных поселениях из-за отсутствия необходимой медицинской помощи. The situation of the COVID-19 pandemic has once again reminded of the need to use telemedicine and online health communities, especially in areas where there are not enough places in local hospitals or there are problems with obtaining quality medical care, such as rural areas where rural medicine is focused on treatment of emergency diseases and doctors have difficulties with the possibility of treatment, as well as explaining about the new coronavirus infection to patients and how this disease can be treated. In this case, online health communities provide an opportunity to find out the necessary information, as well as communicate with specialists who know the features of the new coronavirus and can give the necessary advice. Using the example of in-depth interviews with older people from villages in the Leningrad Region and the Republic of Karelia, the article examines the role of telemedicine for older people and communication in online health communities in the context of overcoming loneliness and isolation that exist in rural areas. The article analyzes the features of the use of telemedicine and the key barriers to the use of telemedicine and communication of older people in online disease communities. In rural areas the main barriers to telemedicine use are infrastructure problems and power outages, as well as the lack of computer literacy for communication and elder people’s interaction in online health communities. In the article the role of online health communities is considered in the context of the self-care of older people and from the point of view of the development of self-care of older people in rural peripheral settlements due to the lack of necessary medical care.

Sign in / Sign up

Export Citation Format

Share Document