Volume 9, Issue 3 (2021)
Health Educ Health Promot 2021, 9(3): 177-184 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Baghi S, Ebrahimzadeh M, Hedayati N. Effective Factors on Eating Disorders Prevention Methods; Analysis of Food-Related Data on Twitter. Health Educ Health Promot 2021; 9 (3) :177-184
URL: http://hehp.modares.ac.ir/article-5-48792-en.html
URL: http://hehp.modares.ac.ir/article-5-48792-en.html
1- Department of Computer Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Information Technology Entrepreneurship, Faculty of Entrepreneurship, Tehran University, Tehran, Iran ,mh.ebrahimzadeh@ut.ac.ir
3- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2- Information Technology Entrepreneurship, Faculty of Entrepreneurship, Tehran University, Tehran, Iran ,
3- Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract: (1947 Views)
Aims: Eating disorders are making a point of challenge for health-related researches. Using big data for this type of researches can effectively help researchers use a beneficial resource of information worldwide in real-time. This study aimed to introduce a more accurate index for analyzing food-related data and making relations between people's opinions and the prevention treatments for eating disorders.
Instrument & Methods: In this data mining study, more than 2 million eating-related tweets were collected from Twitter in 2017 and analyzed by novel methods for big data research. Three main indicators (Basic-sentiment-rate, Health-rate, and Relation-rate) were used to predict if every user is more likely to have a healthy or unhealthy diet. Finally, these parameters were normalized, clustered, and combined to obtain an overall sentiment rate.
Findings: Location and gender were estimated as effective indicators making the relationship between peoples' opinion and prevention treatments for eating disorders. Some combinations of factors were also considered influencing indicators when applied together, such as gender+age and gender+location.
Conclusion: Punishment/reward combination criteria that are predicted with both gender and location data by FSR index is the most effective factor in making the relationship between peoples' opinion and prevention treatments for eating disorders.
Instrument & Methods: In this data mining study, more than 2 million eating-related tweets were collected from Twitter in 2017 and analyzed by novel methods for big data research. Three main indicators (Basic-sentiment-rate, Health-rate, and Relation-rate) were used to predict if every user is more likely to have a healthy or unhealthy diet. Finally, these parameters were normalized, clustered, and combined to obtain an overall sentiment rate.
Findings: Location and gender were estimated as effective indicators making the relationship between peoples' opinion and prevention treatments for eating disorders. Some combinations of factors were also considered influencing indicators when applied together, such as gender+age and gender+location.
Conclusion: Punishment/reward combination criteria that are predicted with both gender and location data by FSR index is the most effective factor in making the relationship between peoples' opinion and prevention treatments for eating disorders.
Keywords: Eating Disorders [MeSH], Prevention [MeSH], Data Mining [MeSH], Big Data [MeSH], Twitter [MeSH]
Article Type: Descriptive & Survey |
Subject:
Health Media
Received: 2020/12/30 | Accepted: 2021/03/7 | Published: 2021/07/24
Received: 2020/12/30 | Accepted: 2021/03/7 | Published: 2021/07/24
References
1. Twitter. Selected company metrics and financials [Internet]. New York: Twitter; 2018 [cited 2020 Dec 13]. Available from: https://www.marketscreener.com/quote/stock/TWITTER-38965267/news/Twitter-Selected-Company-Metrics-and-Financials--31818921/ [Link]
2. Sinnenberg L, Buttenheim AM, Padrez K, Mancheno Ch, Ungar L, Merchant RM. Twitter as a tool for health research: A systematic review. Am J Pub Health. 2017;107(1):e1-8. [Link] [DOI:10.2105/AJPH.2016.303512] [PMID] [PMCID]
3. Paul JM, Dredze M. You are what you tweet: Analyzing twitter for public health. Artif Intell. 2011;38:265-72. [Link]
4. Washington Health Care Authority. Budget Information (2017-2019) [Internet]. Washington: Washington Health Care Authority; 2020 [cited 2020 13 December]. Available from: https://www.hca.wa.gov/about-hca/budget-information. [Link]
5. Emilien C, Hollis JH. A brief review of salient factors influencing adult eating behaviour. Nutr Res Rev. 2017;30(2):233-46. [Link] [DOI:10.1017/S0954422417000099] [PMID]
6. Sloan DM. Does warm weather climate affect eating disorder pathology?. Int J Eat Disord. 2002;32(2):240-4. [Link] [DOI:10.1002/eat.10077] [PMID]
7. De Boer J, de Wittb A, Aikinga H. Help the climate, change your diet: A cross-sectional study on how to involve consumers in a transition to a low-carbon society. Appetite. 2016;98:19-27. [Link] [DOI:10.1016/j.appet.2015.12.001] [PMID]
8. Widener MJ, Li W. Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Appl Geogr. 2014;54:189-97. [Link] [DOI:10.1016/j.apgeog.2014.07.017]
9. Lipson Sk, Sonneville KR. Eating disorder symptoms among undergraduate and graduate students at 12 U.S. colleges and universities. Eat Behav. 2017;24:81-8. [Link] [DOI:10.1016/j.eatbeh.2016.12.003] [PMID]
10. Adeoye OA, Adeoye B. Gender, age and religion as determinants of eating habit of youth in ikenne local government of Ogun state, Nigeria. Edo J Couns. 2010;2(1):110-18. [Link] [DOI:10.4314/ejc.v2i1.52660]
11. Maruti Rongte S, Sengupta R. Assessment of the dietary patterns among school going children with age-group (9 years -15 years)-a gender comparison. Indian J Appl Res. 2015;5(5):19-21. [Link]
12. Rao D, Yarowsky D, Shreevats A, Gupta M. Classifying latent user attributes in Twitter. Proceedings of the 2nd international workshop on search and mining user-generated contents; 2010, 4 October. Toronto: ACM Digital Library; 2010. [Link] [DOI:10.1145/1871985.1871993]
13. Collomb A, Brunie L, Costea C. A study and comparison of sentiment analysis methods for reputation evaluation. Unknown proceeding. Unknown city: LIRIS; 2013. [Link]
14. Goyal S. Sentimental analysis of Twitter data using text mining and hybrid classification approach. Int J Adv Res Ideas Innov Technol. 2016;2(5):1-9. [Link]
15. Manning SD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D. The Stanford CoreNLP natural language processing toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations; 2014 June; Baltimore, Maryland. Pennsylvania: Association for Computational Linguistics; 2014. [Link] [DOI:10.3115/v1/P14-5010]
16. Socher R, Perelygin A, Wu J, Chuang J, Manning ChD, Ng A, et al. Recursive deep models for semantic compositionality over a sentiment treebank. Conference on empirical methods in natural language processing; 2013 Oct; Seattle, Washington, USA. Pennsylvania: Association for Computational Linguistics; 2013. [Link]
17. Lobstein T, Davies S. Defining and labelling 'healthy' and 'unhealthy' food. Public Health Nutr. 2009;12(3):331-40. [Link]
18. Lewis DD. Naive (Bayes) at forty: The independence assumption in information retrieval. In: Nédellec C, Rouveirol C, editors. Machine learning: ECML-98. ECML 1998. Lecture notes in computer science (Lecture notes in artificial intelligence). Volume 1398. Heidelberg: Springer; 1998. [Link] [DOI:10.1007/BFb0026666]
19. Milligan GW, Cooper MC. An examination of procedures for determining the number of clusters in a data set. Psychometrika. 1985;50:159-79. [Link] [DOI:10.1007/BF02294245]
20. Eneva KT, Murray S, O'Garro-Moore J, Yiu A, Alloy LB, Avena NM, et al. Reward and punishment sensitivity and disordered eating behaviors in men and women. J Eat Disord. 2017;5:6. [Link] [DOI:10.1186/s40337-017-0138-2] [PMID] [PMCID]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |