Healthcare

Research

11 pages, 577 KiB

Open AccessEditor’s ChoiceArticle

Examining the Public Messaging on ‘Loneliness’ over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade

by Qin Xiang Ng, Dawn Yi Xin Lee, Chun En Yau, Yu Liang Lim, Clara Xinyi Ng and Tau Ming Liew

Healthcare 2023, 11(10), 1485; https://doi.org/10.3390/healthcare11101485 - 19 May 2023

Cited by 4 | Viewed by 1477

Abstract

Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as [...] Read more.

Loneliness is an issue of public health significance. Longitudinal studies indicate that feelings of loneliness are prevalent and were exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. With the advent of new media, more people are turning to social media platforms such as Twitter and Reddit as well as online forums, e.g., loneliness forums, to seek advice and solace regarding their health and well-being. The present study therefore aimed to investigate the public messaging on loneliness via an unsupervised machine learning analysis of posts made by organisations on Twitter. We specifically examined tweets put out by organisations (companies, agencies or common interest groups) as the public may view them as more credible information as opposed to individual opinions. A total of 68,345 unique tweets in English were posted by organisations on Twitter from 1 January 2012 to 1 September 2022. These tweets were extracted and analysed using unsupervised machine learning approaches. BERTopic, a topic modelling technique that leverages state-of-the-art natural language processing, was applied to generate interpretable topics around the public messaging of loneliness and highlight the key words in the topic descriptions. The topics and topic labels were then reviewed independently by all study investigators for thematic analysis. Four key themes were uncovered, namely, the experience of loneliness, people who experience loneliness, what exacerbates loneliness and what could alleviate loneliness. Notably, a significant proportion of the tweets centred on the impact of the COVID-19 pandemic on loneliness. While current online interactions are largely descriptive of the complex and multifaceted problem of loneliness, more targeted prosocial messaging appears to be lacking to combat the causes of loneliness brought up in public messaging. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Graphical abstract

19 pages, 588 KiB

Open AccessFeature PaperArticle

A Study of Factors Influencing the Volume of Responses to Posts in Physician Online Community

by Jingfang Liu and Yu Zeng

Healthcare 2023, 11(9), 1275; https://doi.org/10.3390/healthcare11091275 - 29 Apr 2023

Viewed by 1136

Abstract

Today’s diverse health needs place greater demands on physicians. However, individual doctors have limited capabilities and may encounter many unsolvable medical problems. The physician online community provides a platform for physicians to communicate with each other and help each other. Physicians can post [...] Read more.

Today’s diverse health needs place greater demands on physicians. However, individual doctors have limited capabilities and may encounter many unsolvable medical problems. The physician online community provides a platform for physicians to communicate with each other and help each other. Physicians can post for help about problems they encounter at work. The number of responses to physicians’ posts is critical to whether or not the problem is resolved. This study collected information on 13,226 posts from a well-known physician online community in China to analyze the factors that influence the number of post replies. In the analysis of the post content of the physician online community, this study innovatively introduces word usage features in the medical field. TextMind was used to extract the rate of several types of words in posts that frequently appear when describing medical information. Ultimately, we found that the rate of time words, visual words, auditory words, and physiological process words used in posts had a positive and significant effect on the number of post responses. A series of new post features has been found to have an impact on the number of post replies in physician online communities. This finding is beneficial for physicians to quickly obtain peer assistance through online platforms, increasing the likelihood of solving workplace challenges and improving physician care, as well as the success of physician online communities. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

23 pages, 7135 KiB

Open AccessArticle

Examination of the Public’s Reaction on Twitter to the Over-Turning of Roe v Wade and Abortion Bans

by Heran Mane, Xiaohe Yue, Weijun Yu, Amara Channell Doig, Hanxue Wei, Nataly Delcid, Afia-Grace Harris, Thu T. Nguyen and Quynh C. Nguyen

Healthcare 2022, 10(12), 2390; https://doi.org/10.3390/healthcare10122390 - 29 Nov 2022

Cited by 5 | Viewed by 4061

Abstract

The overturning of Roe v Wade reinvigorated the national debate on abortion. We used Twitter data to examine temporal, geographical and sentiment patterns in the public’s reaction. Using the Twitter API for Academic Research, a random sample of publicly available tweets was collected [...] Read more.

The overturning of Roe v Wade reinvigorated the national debate on abortion. We used Twitter data to examine temporal, geographical and sentiment patterns in the public’s reaction. Using the Twitter API for Academic Research, a random sample of publicly available tweets was collected from 1 May–15 July in 2021 and 2022. Tweets were filtered based on keywords relating to Roe v Wade and abortion (227,161 tweets in 2021 and 504,803 tweets in 2022). These tweets were tagged for sentiment, tracked by state, and indexed over time. Time plots reveal low levels of conversations on these topics until the leaked Supreme Court opinion in early May 2022. Unlike pro-choice tweets which declined, pro-life conversations continued with renewed interest throughout May and increased again following the official overturning of Roe v Wade. Conversations were less prevalent in some these states had abortion trigger laws (Wyoming, North Dakota, South Dakota, Texas, Louisiana, and Mississippi). Collapsing across topic categories, 2022 tweets were more negative and less neutral and positive compared to 2021 tweets. In network analysis, tweets mentioning woman/women, supreme court, and abortion spread faster and reached to more Twitter users than those mentioning Roe Wade and Scotus. Twitter data can provide real-time insights into the experiences and perceptions of people across the United States, which can be used to inform healthcare policies and decision-making. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

20 pages, 1062 KiB

Open AccessArticle

Understanding Alcohol Use Discourse and Stigma Patterns in Perinatal Care on Twitter

by Fritz Culp, Yuqi Wu, Dezhi Wu, Yang Ren, Phyllis Raynor, Peiyin Hung, Shan Qiao, Xiaoming Li and Kacey Eichelberger

Healthcare 2022, 10(12), 2375; https://doi.org/10.3390/healthcare10122375 - 26 Nov 2022

Viewed by 1686

Abstract

(1) Background: perinatal alcohol use generates a variety of health risks. Social media platforms discuss fetal alcohol spectrum disorder (FASD) and other widespread outcomes, providing personalized user-generated content about the perceptions and behaviors related to alcohol use during pregnancy. Data collected from Twitter [...] Read more.

(1) Background: perinatal alcohol use generates a variety of health risks. Social media platforms discuss fetal alcohol spectrum disorder (FASD) and other widespread outcomes, providing personalized user-generated content about the perceptions and behaviors related to alcohol use during pregnancy. Data collected from Twitter underscores various narrative structures and sentiments in tweets that reflect large-scale discourses and foster societal stigmas; (2) Methods: We extracted alcohol-related tweets from May 2019 to October 2021 using an official Twitter search API based on a set of keywords provided by our clinical team. Our exploratory study utilized thematic content analysis and inductive qualitative coding methods to analyze user content. Iterative line-by-line coding categorized dynamic descriptive themes from a random sample of 500 tweets; (3) Results: qualitative methods from content analysis revealed underlying patterns among inter-user engagements, outlining individual, interpersonal and population-level stigmas about perinatal alcohol use and negative sentiment towards drinking mothers. As a result, the overall silence surrounding personal experiences with alcohol use during pregnancy suggests an unwillingness and sense of reluctancy from pregnant adults to leverage the platform for support and assistance due to societal stigmas; (4) Conclusions: identifying these discursive factors will facilitate more effective public health programs that take into account specific challenges related to social media networks and develop prevention strategies to help Twitter users struggling with perinatal alcohol use. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

16 pages, 495 KiB

Open AccessArticle

Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design

by George Shaw, Jr., Margaret Zimmerman, Ligia Vasquez-Huot and Amir Karami

Healthcare 2022, 10(11), 2320; https://doi.org/10.3390/healthcare10112320 - 19 Nov 2022

Cited by 1 | Viewed by 1652

Abstract

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method [...] Read more.

Natural language processing techniques have increased the volume and variety of text data that can be analyzed. The aim of this study was to identify the positive and negative topical sentiments among diet, diabetes, exercise, and obesity tweets. Using a sequential explanatory mixed-method design for our analytical framework, we analyzed a data corpus of 1.7 million diet, diabetes, exercise, and obesity (DDEO)-related tweets collected over 12 months. Sentiment analysis and topic modeling were used to analyze the data. The results show that overall, 29% of the tweets were positive, and 17% were negative. Using sentiment analysis and latent Dirichlet allocation (LDA) topic modeling, we analyzed 800 positive and negative DDEO topics. From the 800 LDA topics—after the qualitative and computational removal of incoherent topics—473 topics were characterized as coherent. Obesity was the only query health topic with a higher percentage of negative tweets. The use of social media by public health practitioners should focus not only on the dissemination of health information based on the topics discovered but also consider what they can do for the health consumer as a result of the interaction in digital spaces such as social media. Future studies will benefit from using multiclass sentiment analysis methods associated with other novel topic modeling approaches. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

10 pages, 431 KiB

Open AccessArticle

Vulnerable Narcissism and Problematic Social Networking Sites Use: Focusing the Lens on Specific Motivations for Social Networking Sites Use

by Alessandro Musetti, Valentina Grazia, Alessia Alessandra, Christian Franceschini, Paola Corsano and Claudia Marino

Healthcare 2022, 10(9), 1719; https://doi.org/10.3390/healthcare10091719 - 08 Sep 2022

Cited by 8 | Viewed by 1942

Abstract

Research highlighted that Problematic Social Networking Sites Use (PSNSU) and vulnerable narcissism are associated. However, the mechanisms underlying this relationship are still unclear. The present study aimed to test the mediating role of motives for social networking sites (SNSs) use between vulnerable narcissism [...] Read more.

Research highlighted that Problematic Social Networking Sites Use (PSNSU) and vulnerable narcissism are associated. However, the mechanisms underlying this relationship are still unclear. The present study aimed to test the mediating role of motives for social networking sites (SNSs) use between vulnerable narcissism and five symptoms of PSNSU (i.e., preference for online social interactions, mood regulation, cognitive preoccupation, compulsive use, and negative outcomes) in a sole model. Self-report questionnaires were completed by 344 SNSs users in the age range of 18–30 years (76.5% females; mean age = 23.80 years, standard deviation = 2.30 years). Vulnerable narcissism, three motives to use SNSs (coping, conformity, enhancement), and symptoms of PSNSU were assessed. Structural equation modeling was used to test for mediation. The results indicate that both motives with positive (i.e., enhancement) and negative (i.e., coping and conformity) valence partially mediated the association between vulnerable narcissism and different symptoms of PSNSU. We conclude that individuals with vulnerable narcissism may develop PSNSU not only as a compensatory strategy to cope with psychosocial difficulties but also as a result of a gratification-seeking process. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

15 pages, 339 KiB

Open AccessArticle

Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification

by Yuting Guo, Yao Ge, Yuan-Chi Yang, Mohammed Ali Al-Garadi and Abeed Sarker

Healthcare 2022, 10(8), 1478; https://doi.org/10.3390/healthcare10081478 - 05 Aug 2022

Cited by 6 | Viewed by 1972

Abstract

Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks, including those involving health-related social media data. We sought to evaluate the effectiveness of different pretrained transformer-based models for social [...] Read more.

Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks, including those involving health-related social media data. We sought to evaluate the effectiveness of different pretrained transformer-based models for social media-based health-related text classification tasks. An additional objective was to explore and propose effective pretraining strategies to improve machine learning performance on such datasets and tasks. We benchmarked six transformer-based models that were pretrained with texts from different domains and sources—BERT, RoBERTa, BERTweet, TwitterBERT, BioClinical_BERT, and BioBERT—on 22 social media-based health-related text classification tasks. For the top-performing models, we explored the possibility of further boosting performance by comparing several pretraining strategies: domain-adaptive pretraining (DAPT), source-adaptive pretraining (SAPT), and a novel approach called topic specific pretraining (TSPT). We also attempted to interpret the impacts of distinct pretraining strategies by visualizing document-level embeddings at different stages of the training process. RoBERTa outperformed BERTweet on most tasks, and better than others. BERT, TwitterBERT, BioClinical_BERT and BioBERT consistently underperformed. For pretraining strategies, SAPT performed better or comparable to the off-the-shelf models, and significantly outperformed DAPT. SAPT + TSPT showed consistently high performance, with statistically significant improvement in three tasks. Our findings demonstrate that RoBERTa and BERTweet are excellent off-the-shelf models for health-related social media text classification, and extended pretraining using SAPT and TSPT can further improve performance. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

25 pages, 2460 KiB

Open AccessArticle

Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach

by Heng Xu, Jun Zeng, Zhaodan Tai and Huihui Hao

Healthcare 2022, 10(2), 198; https://doi.org/10.3390/healthcare10020198 - 20 Jan 2022

Cited by 8 | Viewed by 3411

Abstract

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge [...] Read more.

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour. Full article

(This article belongs to the Special Issue Social Media for Health Information Management)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Social Media for Health Information Management

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI