Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.6
2.5
Journals
Behavioral Sciences
Special Issues
Promoting Health Behaviors in the New Media Era
share announcement

Promoting Health Behaviors in the New Media Era

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Health Psychology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 7733

Special Issue Editor

Dr. Qinghua Yang

E-Mail Website
Guest Editor
Bob Schieffer College of Communication, Texas Christian University, Fort Worth, TX 76129, USA
Interests: health education and promotion; health information acquisition and professing; new media and technology; substance use
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New media is revolutionizing not only human communication in almost every context (e.g., interpersonal, health care, organizational, intercultural, etc.), but also how health education and promotion are performed. For instance, the Internet, social media, and generative artificial intelligence (AI), such as ChatGPT, have become the primary health information sources for the public. Conversational AI, which involves software capable of engaging in human-like interaction, has been increasingly employed as part of interventions to address a wide range of health conditions. With new media playing an increasingly important role in individuals’ acquisition, processing, and sharing of health information, it is complex but imperative to understand how new media can be leveraged in health education and promotion. Example research questions to be answered in this Special Issue include (but are not limited to) (a) how our health education and promotion are predictably similar or fundamentally different because of new media and technology, (b) how new media is reshaping health information acquisition, processing, and retransmission, (c) the content, dynamism, and cognitive and behavioral outcomes of health information on new media, (d) the opportunities and challenges of new media and technology in reshaping our health behaviors, and (e) how health researchers and professionals could leverage the advantages of new media and minimize the disadvantages.

This Special Issue welcomes all submissions that attempt to answer these questions and anticipates including papers that cover a diverse set of countries and disciplines and employ a variety of research methods (e.g., quantitative, qualitative, or mixed methods). Authors should note that “new media” is broadly defined, which includes but is not limited to social media, digital media, algorithms, mobile devices, AI, virtual reality, etc. Scholars employing new cutting-edge methods in their inquiries, such as social network analysis, computational textual analysis, eye tracking, neuroimaging, etc., are particularly encouraged to submit their research.

Dr. Qinghua Yang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • new media
  • social media
  • artificial intelligence
  • health behaviors
  • social scientific approach

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 5628 KiB  
Article
Deep Learning-Based Detection of Depression and Suicidal Tendencies in Social Media Data with Feature Selection
by İsmail Baydili, Burak Tasci and Gülay Tasci
Behav. Sci. 2025, 15(3), 352; https://doi.org/10.3390/bs15030352 - 12 Mar 2025
Viewed by 1577
Abstract
Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, the ability to automatically detect individuals at risk through their social media [...] Read more.
Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, the ability to automatically detect individuals at risk through their social media activity holds significant potential for early intervention and mental health support. This study proposes a machine learning-based framework that integrates pre-trained language models and advanced feature selection techniques to improve the detection of depression and suicidal tendencies from social media data. We utilize six diverse datasets, collected from platforms such as Twitter and Reddit, ensuring a broad evaluation of model robustness. The proposed methodology incorporates Cumulative Weight-based Iterative Neighborhood Component Analysis (CWINCA) for feature selection and Support Vector Machines (SVMs) for classification. The results indicate that the model achieves high accuracy across multiple datasets, ranging from 80.74% to 99.96%, demonstrating its effectiveness in identifying risk factors associated with mental health issues. These findings highlight the potential of social media-based automated detection methods as complementary tools for mental health professionals. Future work will focus on real-time detection capabilities and multilingual adaptation to enhance the practical applicability of the proposed approach. Full article
(This article belongs to the Special Issue Promoting Health Behaviors in the New Media Era)
►▼ Show Figures

Figure 1

15 pages, 870 KiB  
Article
The Mediating Role of Hesitancy in the Associations Between Mental Disorders and Social Support Seeking During the COVID-19 Pandemic
by Qinghua Yang, Muniba Saleem, Elizabeth Dobson and Suzanne Grimmesey
Behav. Sci. 2024, 14(11), 979; https://doi.org/10.3390/bs14110979 - 22 Oct 2024
Cited by 1 | Viewed by 1134
Abstract
The COVID-19 pandemic has consequential impacts on not only physical but also mental health. However, the types of social support that individuals with mental health needs sought during the pandemic and their underlying reasons for it are not well known. Drawing on a [...] Read more.
The COVID-19 pandemic has consequential impacts on not only physical but also mental health. However, the types of social support that individuals with mental health needs sought during the pandemic and their underlying reasons for it are not well known. Drawing on a community needs survey with 4282 participants, we found a positive association between self-reported anxiety and seeking social support from health professionals, family and friends, and mediated sources. There was also a positive association between self-reported depression and seeking support from medical professionals and mediated sources but a negative association with seeking support from family and friends. Importantly, a positive indirect effect was observed between mental health and seeking support from family and friends through hesitancy, whereas negative indirect effects were documented between mental health and seeking support from health professionals and mediated sources through hesitancy. Theoretical, practical, and methodological implications were discussed. Full article
(This article belongs to the Special Issue Promoting Health Behaviors in the New Media Era)
►▼ Show Figures

Figure 1

15 pages, 451 KiB  
Article
Voluntary Participation Mediates the Relationship Between Multi-Membership in Online Communities and Life Satisfaction Among Chinese Populations: A Gendered Perspective
by Xiaorui Huang and Mingqi Fu
Behav. Sci. 2024, 14(11), 976; https://doi.org/10.3390/bs14110976 - 22 Oct 2024
Viewed by 1163
Abstract
Whether and how multi-membership in online communities might relate to life satisfaction within the Chinese population remain unclear. This study adopts a gendered perspective to explore the mediating role of voluntary participation in the relationship mentioned above based on a cross-sectional analysis of [...] Read more.
Whether and how multi-membership in online communities might relate to life satisfaction within the Chinese population remain unclear. This study adopts a gendered perspective to explore the mediating role of voluntary participation in the relationship mentioned above based on a cross-sectional analysis of 2558 respondents from the 2019 Chinese Social Survey (CSS). Multivariable regressions and a mediation analysis were adopted for analyses. The findings reveal that a higher level of multi-membership in online communities is associated with greater life satisfaction for both males (B = 0.31, SE = 0.11) and females (B = 0.10, SE = 0.02). Specifically, the positive relationship is partially mediated (6.6%) by increased voluntary participation among females, where involvement in multiple types of online communities correlates with a heightened likelihood of engaging in voluntary activities (B = 0.006, Z = 3.910), which in turn contributes to higher levels of life satisfaction (B = 0.114, Z = 2.760). However, voluntary participation does not exhibit a significant mediating role in the relationship between multi-membership and life satisfaction among males. These findings provide valuable insights into the intricate ways in which online interactions can affect voluntary participation and life satisfaction, underscoring the importance of considering gender differences in these dynamics. Full article
(This article belongs to the Special Issue Promoting Health Behaviors in the New Media Era)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 23527 KiB  
Review
Current Status and Future Directions of Artificial Intelligence in Post-Traumatic Stress Disorder: A Literature Measurement Analysis
by Ruoyu Wan, Ruohong Wan, Qing Xie, Anshu Hu, Wei Xie, Junjie Chen and Yuhan Liu
Behav. Sci. 2025, 15(1), 27; https://doi.org/10.3390/bs15010027 - 30 Dec 2024
Cited by 1 | Viewed by 2943
Abstract
This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such [...] Read more.
This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.6.19), and CiteSpace (version 6.3.R1) on the relevant literature from the Web of Science Core Collection (WoSCC). The analysis reveals a significant increase in publications since 2017. Kerry J. Ressler has emerged as the most influential author in the field to date. The United States leads in the number of publications, producing seven times more papers than Canada, the second-ranked country, and demonstrating substantial influence. Harvard University and the Veterans Health Administration are also key institutions in this field. The Journal of Affective Disorders has the highest number of publications and impact in this area. In recent years, keywords related to functional connectivity, risk factors, and algorithm development have gained prominence. The field holds immense research potential, with AI poised to revolutionize PTSD management through early symptom detection, personalized treatment plans, and continuous patient monitoring. However, there are numerous challenges, and fully realizing AI’s potential will require overcoming hurdles in algorithm design, data integration, and societal ethics. To promote more extensive and in-depth future research, it is crucial to prioritize the development of standardized protocols for AI implementation, foster interdisciplinary collaboration—especially between AI and neuroscience—and address public concerns about AI’s role in healthcare to enhance its acceptance and effectiveness. Full article
(This article belongs to the Special Issue Promoting Health Behaviors in the New Media Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop