“I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis

Social media is a growing platform for health-related discourse, opinion and experience sharing, including breastfeeding. For instance, nursing mothers share their personal experiences and opinions about breastfeeding on social networks, such as Facebook and Twitter. Unravelling the sentiments behin...

Full description

Bibliographic Details
Main Authors: Oladapo Oyebode, Richard Lomotey, Rita Orji
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9402798/
id doaj-c7ddabd0c450431582205972b09ab64a
record_format Article
spelling doaj-c7ddabd0c450431582205972b09ab64a2021-04-26T23:00:50ZengIEEEIEEE Access2169-35362021-01-019610746108910.1109/ACCESS.2021.30730799402798&#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic AnalysisOladapo Oyebode0https://orcid.org/0000-0002-5797-7790Richard Lomotey1https://orcid.org/0000-0002-5215-7806Rita Orji2https://orcid.org/0000-0001-6152-8034Faculty of Computer Science, Dalhousie University, Halifax, NS, CanadaDepartment of Information Sciences and Technology (IST), The Pennsylvania State University at Beaver, Monaca, PA, USAFaculty of Computer Science, Dalhousie University, Halifax, NS, CanadaSocial media is a growing platform for health-related discourse, opinion and experience sharing, including breastfeeding. For instance, nursing mothers share their personal experiences and opinions about breastfeeding on social networks, such as Facebook and Twitter. Unravelling the sentiments behind these experiences will promote adequate knowledge of many challenges, benefits, and factors influencing breastfeeding behaviours. To achieve this, we mine breastfeeding-related tweets and then perform sentiment analysis of the tweets using lexicon-based and machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e., <italic>positive</italic> or <italic>negative</italic>). Specifically, we implement and compare four lexicon-based sentiment classifiers, as well as five ML-based classifiers. Our results show that VADER-EXT (our extended version of VADER) performed best with an overall F1-score of 82.4&#x0025;, compared to the other lexicon-based classifiers. On the other hand, Support Vector Machine (SVM) outperformed the other four ML-based classifiers with an overall F1-score of 73.7&#x0025;. The overall best performing classifier is then used in determining the sentiment polarity of tweets. Next, we conduct thematic analysis of both positive and negative tweets to identify the factors influencing breastfeeding behaviours either positively or negatively. Our findings reveal various <italic>health-related factors</italic> (such as lactational issues, medical issues, and nutritional issues), <italic>social factors</italic>, <italic>psychological factors</italic>, and <italic>situational factors</italic> affecting breastfeeding behaviours negatively. Also, <italic>perceived benefits</italic>, <italic>maternal self-efficacy</italic>, <italic>social support</italic>, and <italic>education and training support</italic> emerged as the positive factors influencing breastfeeding behaviours. Finally, we reflect on our findings and recommend interventions that address the negative factors to promote positive breastfeeding behaviours.https://ieeexplore.ieee.org/document/9402798/Breastfeedinghealth informaticslexicon-based approachmachine learningsentiment analysissocial media
collection DOAJ
language English
format Article
sources DOAJ
author Oladapo Oyebode
Richard Lomotey
Rita Orji
spellingShingle Oladapo Oyebode
Richard Lomotey
Rita Orji
&#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
IEEE Access
Breastfeeding
health informatics
lexicon-based approach
machine learning
sentiment analysis
social media
author_facet Oladapo Oyebode
Richard Lomotey
Rita Orji
author_sort Oladapo Oyebode
title &#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
title_short &#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
title_full &#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
title_fullStr &#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
title_full_unstemmed &#x201C;I Tried to Breastfeed but&#x2026;&#x201D;: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis
title_sort &#x201c;i tried to breastfeed but&#x2026;&#x201d;: exploring factors influencing breastfeeding behaviours based on tweets using machine learning and thematic analysis
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description Social media is a growing platform for health-related discourse, opinion and experience sharing, including breastfeeding. For instance, nursing mothers share their personal experiences and opinions about breastfeeding on social networks, such as Facebook and Twitter. Unravelling the sentiments behind these experiences will promote adequate knowledge of many challenges, benefits, and factors influencing breastfeeding behaviours. To achieve this, we mine breastfeeding-related tweets and then perform sentiment analysis of the tweets using lexicon-based and machine learning (ML) techniques with the aim of detecting their sentiment polarity (i.e., <italic>positive</italic> or <italic>negative</italic>). Specifically, we implement and compare four lexicon-based sentiment classifiers, as well as five ML-based classifiers. Our results show that VADER-EXT (our extended version of VADER) performed best with an overall F1-score of 82.4&#x0025;, compared to the other lexicon-based classifiers. On the other hand, Support Vector Machine (SVM) outperformed the other four ML-based classifiers with an overall F1-score of 73.7&#x0025;. The overall best performing classifier is then used in determining the sentiment polarity of tweets. Next, we conduct thematic analysis of both positive and negative tweets to identify the factors influencing breastfeeding behaviours either positively or negatively. Our findings reveal various <italic>health-related factors</italic> (such as lactational issues, medical issues, and nutritional issues), <italic>social factors</italic>, <italic>psychological factors</italic>, and <italic>situational factors</italic> affecting breastfeeding behaviours negatively. Also, <italic>perceived benefits</italic>, <italic>maternal self-efficacy</italic>, <italic>social support</italic>, and <italic>education and training support</italic> emerged as the positive factors influencing breastfeeding behaviours. Finally, we reflect on our findings and recommend interventions that address the negative factors to promote positive breastfeeding behaviours.
topic Breastfeeding
health informatics
lexicon-based approach
machine learning
sentiment analysis
social media
url https://ieeexplore.ieee.org/document/9402798/
work_keys_str_mv AT oladapooyebode x201citriedtobreastfeedbutx2026x201dexploringfactorsinfluencingbreastfeedingbehavioursbasedontweetsusingmachinelearningandthematicanalysis
AT richardlomotey x201citriedtobreastfeedbutx2026x201dexploringfactorsinfluencingbreastfeedingbehavioursbasedontweetsusingmachinelearningandthematicanalysis
AT ritaorji x201citriedtobreastfeedbutx2026x201dexploringfactorsinfluencingbreastfeedingbehavioursbasedontweetsusingmachinelearningandthematicanalysis
_version_ 1721507404488638464