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© 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background: Fathers’ experiences across the transition to parenthood are underreported in the literature. Social media offers the potential to capture fathers’ experiences in real time and at scale while also removing the barriers that fathers typically face in participating in research and clinical care.

Objective: This study aimed to assess the feasibility of using social media data to map the discussion topics of fathers across the fatherhood transition.

Methods: Discussion threads from two Web-based parenting communities, r/Daddit and r/PreDaddit from the social media platform Reddit, were collected over a 2-week period, resulting in 1980 discussion threads contributed to by 5853 unique users. An unsupervised machine learning algorithm was then implemented to group discussion threads into topics within each community and across a combined collection of all discussion threads.

Results: Results demonstrated that men use Web-based communities to share the joys and challenges of the fatherhood experience. Minimal overlap in discussions was found between the 2 communities, indicating that distinct conversations are held on each forum. A range of social support techniques was demonstrated, with conversations characterized by encouragement, humor, and experience-based advice.

Conclusions: This study demonstrates that rich data on fathers’ experiences can be sourced from social media and analyzed rapidly using automated techniques, providing an additional tool for researchers exploring fatherhood.

Details

Title
Exploring the Transition to Fatherhood: Feasibility Study Using Social Media and Machine Learning
Author
Teague, Samantha J  VIAFID ORCID Logo  ; Shatte, Adrian BR  VIAFID ORCID Logo 
Section
Social Media for Parenting
Publication year
2018
Publication date
Jul-Dec 2018
Publisher
JMIR Publications
e-ISSN
25616722
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2509672953
Copyright
© 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.