research-article

Mining User-Generated Content to Identify Social Support in Chinese Online Smoking Cessation Community

Authors:

Zhuo ChengAuthors Info & Claims

ICMHI '19: Proceedings of the 3rd International Conference on Medical and Health Informatics

Pages 135 - 141

https://doi.org/10.1145/3340037.3340055

Published: 17 May 2019 Publication History

Abstract

Purpose/Significance: This study on the social support behavior of online smoking cessation community users can provide support for community management personnel to guide user behavior, enrich and deepen the functions of online smoking cessation communities and improve user stickiness, and also provides effective basis for public health workers to develop smoking cessation strategy in online communities. Method/Process: All the posts published in Baidu JieYanBa from August 1, 2018 to October 31, 2018 were gained. 2758 posts from core users were selected. Theme coding was adopted to divide the stage of smoking cessation of the poster. Keywords extraction and co-keywords network analysis were carried out to identify the types of social support and analyze its similarities and differences in different smoking cessation stages. Results: With the development of the smoking cessation stage, the proportion of emotional support and information support is on the rise. Emotional support is the main theme of social support in the preparatory stage, the action stage and the maintenance stage. The types and proportions of social support change regularly at different stages of smoking cessation.

References

[1]

Word Health Organization. 2018. Tobacco. Retrieved from https://www.who.int/news-room/fact-sheets/detail/tobacco

[2]

Word Health Organization. 2018. Quitting Tobacco. Retrieved from https://www.who.int/tobacco/quitting/en/

[3]

Lin, N., Ensel, W. M., Simeone, R. S., Kuo, W. J. J. o. h. and Behavior, S. 1979. Social support, stressful life events, and illness: A model and an empirical test. Journal of health and Social Behavior. (1979), 108--119.

[4]

Zhang, S., O'Carroll Bantum, E., Owen, J., Bakken, S. and Elhadad, N. J. 2017. Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact. J Am Med Inform Assoc. 24, 2 (2017), 451--459.

[5]

Cobb, N. K., Graham, A. L., Bock, B. C., Papandonatos, G., Abrams, D. B. J. N. and Research, T. 2005. Initial evaluation of a real-world Internet smoking cessation system. Nicotine & Tobacco Research. 7, 2 (2005), 207--216.

[6]

Berthon, P., Pitt, L., Kietzmann, J. and McCarthy, I. 2015. CGIP: managing consumer-generated intellectual property. California Management Review. 57, 4 (2015), 43--62.

[7]

Zhang, M., Yang, C. C. and Gong, X. 2013. Social Support and Exchange Patterns in an Online Smoking Cessation Intervention Program. In 2013 IEEE International Conference on Healthcare Informatics (2013).

Digital Library

[8]

Zhang, M, Z. and Yang, C. C. 2014. Using Content and Network Analysis to Understand the Social Support Exchange Patterns and User Behaviors of an Online Smoking Cessation Intervention Program. Journal of the Association for Information Science & Technology. 66, 3 (2014), 564--575.

Digital Library

[9]

Zhang, M. and Yang, C. C. 2014. Classifying user intention and social support types in online healthcare discussions. In 2014 IEEE International Conference on Healthcare Informatics (2014).

Digital Library

[10]

Benedetto, G., Prima, A. D., Sciacca, S. and Grosso, G. 2017. Design, functionality, and validity of the SWInCaRe, a web-based application used to administer cancer registry records. Health Informatics Journal. (2017).

[11]

Ficcadenti, V., Cerqueti, R. and Ausloos, M. 2019. A joint text mining-rank size investigation of the rhetoric structures of the US Presidents' speeches. Expert Systems with Applications. 123 (2019), 127--142.

Digital Library

[12]

Duari, S. and Bhatnagar, V. 2019. sCAKE: Semantic Connectivity Aware Keyword Extraction. Information Sciences. 477 (2019), 100--117.

[13]

Otte, E. and Rousseau, R. J. 2002. Social network analysis: a powerful strategy, also for the information sciences. Journal of information Science. 28, 6 (2002), 441--453.

[14]

Li, M. and Chu, Y. 2017. Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science. 43, 6 (2017), 725--741.

Digital Library

[15]

Markazi-Moghaddam, N., Mohammad, A., Ravaghi, H., Rashidian, A., Khatibi, T. and Jame, S. Z. 2016. A knowledge map for hospital performance concept: Extraction and analysis: A narrative review article. Iranian journal of public health. 45, 7 (2016), 843.

[16]

Chen, X., Chen, J., Wu, D., Xie, Y. and Li, J. 2016. Mapping the research trends by co-word analysis based on keywords from funded project. Procedia Computer Science. 91 (2016), 547--555.

[17]

Choi, J., Yi, S. and Lee, K. 2011. Analysis of keyword networks in MIS research and implications for predicting knowledge evolution. Information & Management. 48, 8 (2011), 371--381.

Digital Library

[18]

Leydesdorff, L. and Vaughan, L. 2006. Co-occurrence matrices and their applications in information science: Extending ACA to the Web environment. Journal of the American Society for Information Science and technology. 57, 12 (2006), 1616--1628.

Digital Library

[19]

Loohach, R. and Garg, K. 2012. Effect of distance functions on k-means clustering algorithm. International Journal of Computer Applications. 49, 6 (2012), 7--9.

[20]

Prochaska, J. O., Redding, C. A. and Evers, K. E. 2015. The transtheoretical model and stages of change. Health Behaviour and Health Education. (2008), 67--96.

Cited By

Nikkhah SJohn SYalamarti KL. Mueller ED. Miller A(2021)Helping Their Child, Helping Each Other:Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3462204.3481759(140-143)Online publication date: 23-Oct-2021
https://dl.acm.org/doi/10.1145/3462204.3481759
Torres-Carrión PReátegui RBustamante BGordón JBoada MRuisoto P(2020)Cluster Analysis for Abstemious Characterization Based on Psycho-Social InformationApplied Technologies10.1007/978-3-030-42520-3_15(184-193)Online publication date: 3-Mar-2020
https://doi.org/10.1007/978-3-030-42520-3_15

Index Terms

Mining User-Generated Content to Identify Social Support in Chinese Online Smoking Cessation Community
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

Recommendations

Social Support and Exchange Patterns in an Online Smoking Cessation Intervention Program
ICHI '13: Proceedings of the 2013 IEEE International Conference on Healthcare Informatics

Smoking and tobacco use are acquired behaviors and the most preventable cause of death in United States. Many studies show that a large amount of diseases, deaths, years of potential life lost and productivity loses are associated with smoking and ...
A comparative study of smoking cessation intervention programs on social media
SBP'12: Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction

As an alternative to traditional face-to-face counseling and support group, social media has become a new venue for health intervention programs. Different online intervention channels have their own characteristics and advantages. This study focuses on ...
Patterns of support in an online community for smoking cessation
C&T '13: Proceedings of the 6th International Conference on Communities and Technologies

Social support offers various benefits for health and behaviour change. However, previous work has shown that individuals are typically reluctant to ask for support on social network sites, unless they can present a changed, healthier identity. To ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMHI '19: Proceedings of the 3rd International Conference on Medical and Health Informatics

May 2019

207 pages

ISBN:9781450371995

DOI:10.1145/3340037

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 May 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Natural Science Foundation of China

Conference

ICMHI 2019

ICMHI 2019: The third International Conference on Medical and Health Informatics 2019

May 17 - 19, 2019

Xiamen, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
79
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nikkhah SJohn SYalamarti KL. Mueller ED. Miller A(2021)Helping Their Child, Helping Each Other:Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3462204.3481759(140-143)Online publication date: 23-Oct-2021
https://dl.acm.org/doi/10.1145/3462204.3481759
Torres-Carrión PReátegui RBustamante BGordón JBoada MRuisoto P(2020)Cluster Analysis for Abstemious Characterization Based on Psycho-Social InformationApplied Technologies10.1007/978-3-030-42520-3_15(184-193)Online publication date: 3-Mar-2020
https://doi.org/10.1007/978-3-030-42520-3_15

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten