Abstract
Social media tools are widely used by diabetics. Two of the most common diabetes diseases, Type1 and Type2, are well represented on the social media site Reddit. In the R/diabetes subheading, natural language processing tools were used to review comments, track reviewer relevance and comments on common treatments for diabetes. All comments from the sub-title r/diabetes (n=110.483) between 2019-2022 were received and processed using natural language processing tools. Between 2019-2022, the volume of comments on alternative treatments and lifestyle changes showed a fluctuating process. The study was conducted to examine the comments under 3 main headings. General expressions about diabetes (5 terms), active ingredients of drugs used in treatment (6 terms), alternative treatment methods and lifestyles (6 terms). Although almost all of the terms show a fluctuating course in terms of comments over the years, they increased in 2022, except for the terms "bicycling" and "Repaglinide". In the inclusion of the term “Hydrotherapy” in the comments, there is a regular increase in years as a whole, while there is a regular decrease in the expression “Bicycling”. A high increase was observed in the term “glizlazide” active ingredient in the transition from 2020 to 2021. These analyzes suggest additional research areas regarding patient interest and sensitivity to diabetes treatments.
References
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- International Diabetes Federation. IDF diabetes Atlas, 10th edn. Brussels, Belgium: 2021. Available at: https://www.diabetesatlas.org
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- Nabizadeh-ShahreBabak Z., Karimi N., Khadivi P., Roshandel R., Emami A., Samavi S. Detection of COVID-19 in X-ray images by classification of bag of visual words using neural networks. Biomedical Signal Processing and Control 2021; 68: 102750.
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Doğal Dil İşlemede Veri Ön İşleme Tekniklerinin Sosyal Medya Yorumlarında Kullanılarak Şeker Hastalığı Analizi
Abstract
Sosyal medya araçları şeker hastaları tarafından yaygın olarak kullanılmaktadır. En yaygın şeker hastalıklarından ikisi olan Tip1 ve Tip2, sosyal medya sitesi Reddit'te iyi bir şekilde temsil edilmektedir. R/diabetes alt başlığında yorumları incelemek, yorum yapanların ilgi düzeyini ve şeker hastalığı için yaygın tedavilerle ilgili yorumları v izlemek için doğal dil işleme araçlarını kullanılmıştır. 2019-2022 yılları arasında r/diabetes (n=110.483) alt başlığından gelen tüm yorumlar alındı ve doğal dil işleme araçları kullanılarak işlendi. 2019-2022 yılları arasında alternatif tedaviler, yaşam tarzı değişiklikleri ile ilgili yorum hacmi dalgalı bir süreç göstermiştir. Çalışma 3 ana başlık altındaki yorumları inceleyecek şekilde yapılmıştır. Şeker hastalığı ile ilgili genel ifadeler (5 terim), tedavide kullanılan ilaçların etkin maddeleri (6 terim), alternatif tedavi yöntemleri ve yaşam stilleri (6 terim) başlığıdır. Terimlerin tamamına yakını yıllara göre yorumlarda yer alma sıklığı dalgalı bir seyir göstermekle beraber “bicycling” ve “Repaglinide” terimleri hariç 2022 yılında artış göstermişlerdir. “Hydrotherapy” teriminin yorumlarda yer almasında, bütünde yıllarda düzenli bir artış varken “Bicycling” ifadesine düzenli bir azalış söz konusudur. “Glizlazide” etken madde terimde, 2020 yılından 2021 yılına geçişte yüksek bir artış gözlemlenmiştir. Bu analizler şeker hastalığı tedavileriyle ilgili hasta ilgi düzeyi ve duyarlılığı ile ilgili olarak ek araştırma alanı önermektedir.
References
- Bird S., Klein E., Loper E. Natural language processing with python 2009.
- Cummins JA., Zhou G., Nambudiri VE. Natural language processing for large scale analysis of eczema and psoriasis social media comments. JID Innovations 2023b; 00210.
- Eldib AH., Dhaver S., Al‐Badri M., Salah T., Kibaa K., Elenani O., Tomah S., Gardner H., Hamdy O. Magnitude of A1C improvement in relation to baseline A1C and amount of weight loss in response to intensive lifestyle intervention in real‐world diabetes practice: 13 years of observation. Journal of Diabetes 2023.
- Exploring the Role of social media as a support mechanism among persons with diabetes: An online ethnographic study. Journal of Content, Community & Communication 2019; 10(9).
- Falissard B., Simpson EL., Guttman-Yassky E., Papp KA., Thyssen JP., Gadkari A., Saba G., Gautier L., Abbe A., Eckert L. Correction to: qualitative assessment of adult patients’ perception of atopic dermatitis using natural language processing analysis in a cross-sectional study. Dermatology and Therapy 2020; 10(2): 307–310.
- Freihat AA., Abbas M., Bella G., Giunchiglia F. Towards an optimal solution to lemmatization in arabic. Procedia Computer Science 2018; 142: 132–140.
- Hutto C., Gilbert, E. Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media 2014; 8(1): 216–225.
- International Diabetes Federation. IDF diabetes Atlas, 10th edn. Brussels, Belgium: 2021. Available at: https://www.diabetesatlas.org
- Moulaei K., Dinari Z., Dinari F., Jahani Y., Bahaadinbeigy K. The role of social networks in diabetes self‐care: A cross‐sectional study. HealthScienceReports 2022; 5(3).
- Nabizadeh-ShahreBabak Z., Karimi N., Khadivi P., Roshandel R., Emami A., Samavi S. Detection of COVID-19 in X-ray images by classification of bag of visual words using neural networks. Biomedical Signal Processing and Control 2021; 68: 102750.
- Nelakurthi AR., Pinto AM., Cook CB., Jones LC., Boyle M., Y, J., Lappas T., He J. Should patients with diabetes be encouraged to integrate social media into their care plan? Future Science OA 2018; 4(7).
- Oyebode O., Orji R. Detecting factors responsible for diabetes prevalence in nigeria using social. Media and Machine Learning 2019.
- Řehůřek R Sojka P. Software framework for topic modelling with large corpora. proc. LREC 2010 Workshop New Chall. NLP Framew. Valletta, Malta: ELRA 2010: 45–50
- Thomas RL., Alabraba V., Barnard S., Beba H., Brake J., Cox AJ., Bowker R., Edwards DM., Epps A., Fletcher-Salt T., Holmes P., Kar P., Kausar N., Kelly B., Leveridge M., Newland-Jones P., Ng SM., Puttana A., Stewart R. Use of social media as a platform for education and support for people with diabetes during a global pandemic. Journal of Diabetes Science and Technology 2021; 17(2): 353–363.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Machine Learning (Other) |
| Journal Section | RESEARCH ARTICLES |
| Authors | |
| Publication Date | March 11, 2024 |
| Submission Date | June 6, 2023 |
| Acceptance Date | October 22, 2023 |
| Published in Issue | Year 2024 Volume: 7 Issue: 2 |
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