Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach

Artificial intelligence in perinatal mental health research: A scoping review

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

When I am sixty-four… evaluating language markers of well-being in healthy aging narratives

Natural language use is a promising candidate for the development of innovative measures of well-being to complement self-report measures. The type of words individuals use can reveal important psychological processes that underlie well-being across the lifespan. In this preregistered, cross-sectional study, we propose a conceptual model of language markers of well-being and use written narratives about healthy aging (N = 701) and computerized text analysis (LIWC) to empirically validate the model. As hypothesized, we identified a model with three groups of language markers (reflecting affective, evaluative, and social processes). Initial validation with established self-report scales (N = 30 subscales) showed that these language markers reliably predict core components of well-being and underlying processes. Our results support the concurrent validity of the conceptual language model and allude to the added benefits of language-based measures, which are thought to reflect less conscious processes of well-being. Future research is needed to continue validating language markers of well-being across the lifespan in a theoretically informed and contextualized way, which will lay the foundation for inferring people's well-being from their natural language use.

A comprehensive review on emerging trends in the dynamic evolution of digital addiction and depression

Introduction

Using digital addiction as an umbrella term to cover any type of addictions to digital technologies such as the internet, smartphones, social media, or video games, the current study aimed to reveal the intellectual structure and evolution of research addressing digital addiction-depression relationship.

Methods

The study combined bibliometric and science mapping analysis methods for this purpose. Data for the study was gathered from Web of Science Core Collection after a comprehensive process of data search/extraction, and 241 articles were included in the final data set. A period-based, comparative science mapping analysis was performed using the SciMAT software.

Results

The analysis of data over three periods, Period 1 (1983-2016), Period 2 (2017-2019), and Period 3 (2020-2022) showed that internet addiction was the most significant theme across all three periods, which was followed by social media addiction. Depression, which emerged as a significant theme during Period 1, was later covered under anxiety disorder theme. Research interest was mostly on factors related to both addiction and depression such as cognitive distortion, insomnia, loneliness, self-esteem, social support, alexithymia, as well as cybervictimization or academic performance.

Discussion

The results suggested that much research is warranted on the digital addiction-depression relationship in different age cohorts, especially children and elderly. Similarly, the current analysis showed that this line of research particularly focused on internet, gaming and social media addiction, and evidence with regard to other types of digital addiction or related compulsive behaviors was almost absent. In addition, research was mostly inclined to understanding cause-effect relationships, which is significant, but preventive strategies seemed to be barely addressed. Likewise, the smartphone addiction-depression relationship arguably garnered less research interest, so future research would contribute to the field in this respect.

Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons

INTRODUCTION

In recent years, research has used psycholinguistic features in public discourse, networking behaviors on social media and profile information to train models for depression detection. However, the most widely adopted approach for the extraction of psycholinguistic features is to use the Linguistic Inquiry Word Count (LIWC) dictionary and various affective lexicons. Other features related to cultural factors and suicide risk have not been explored. Moreover, the use of social networking behavioral features and profile features would limit the generalizability of the model. Therefore, our study aimed at building a prediction model of depression for text-only social media data through a wider range of possible linguistic features related to depression, and illuminate the relationship between linguistic expression and depression.

METHODS

We collected 789 users' depression scores as well as their past posts on Weibo, and extracted a total of 117 lexical features via Simplified Chinese Linguistic Inquiry Word Count, Chinese Suicide Dictionary, Chinese Version of Moral Foundations Dictionary, Chinese Version of Moral Motivation Dictionary, and Chinese Individualism/Collectivism Dictionary.

RESULTS

Results showed that all the dictionaries contributed to the prediction. The best performing model occurred with linear regression, with the Pearson correlation coefficient between predicted values and self-reported values was 0.33, the R-squared was 0.10, and the split-half reliability was 0.75.

DISCUSSION

This study did not only develop a predictive model applicable to text-only social media data, but also demonstrated the importance taking cultural psychological factors and suicide related expressions into consideration in the calculation of word frequency. Our research provided a more comprehensive understanding of how lexicons related to cultural psychology and suicide risk were associated with depression, and could contribute to the recognition of depression.

Application of machine learning in predicting the risk of postpartum depression: A systematic review

BACKGROUND

Postpartum depression (PPD) presents a serious health problem among women and their families. Machine learning (ML) is a rapidly advancing field with increasing utility in predicting PPD risk. We aimed to synthesize and evaluate the quality of studies on application of ML techniques in predicting PPD risk.

METHODS

We conducted a systematic search of eight databases, identifying English and Chinese studies on ML techniques for predicting PPD risk and ML techniques with performance metrics. Quality of the studies involved was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Seventeen studies involving 62 prediction models were included. Supervised learning was the main ML technique employed and the common ML models were support vector machine, random forest and logistic regression. Five studies (30 %) reported both internal and external validation. Two studies involved model translation, but none were tested clinically. All studies showed a high risk of bias, and more than half showed high application risk.

LIMITATIONS

Including Chinese articles slightly reduced the reproducibility of the review. Model performance was not quantitatively analyzed owing to inconsistent metrics and the absence of methods for correlation meta-analysis.

CONCLUSIONS

Researchers have paid more attention to model development than to validation, and few have focused on improvement and innovation. Models for predicting PPD risk continue to emerge. However, few have achieved the acceptable quality standards. Therefore, ML techniques for successfully predicting PPD risk are yet to be deployed in clinical environments.

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

BACKGROUND

Detection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide.

OBJECTIVE

This systematic review aims to summarize the findings of previous studies concerning applying machine learning (ML) methods to text data from social media to detect depressive symptoms and to suggest directions for future research in this area.

METHODS

A bibliographic search was conducted for the period of January 1990 to December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Two reviewers retrieved and independently assessed the 418 studies consisting of 322 articles identified through database searching and 96 articles identified through other sources; 17 of the studies met the criteria for inclusion.

RESULTS

Of the 17 studies, 10 had identified depression based on researcher-inferred mental status, 5 had identified it based on users' own descriptions of their mental status, and 2 were identified based on community membership. The ML approaches of 13 of the 17 studies were supervised learning approaches, while 3 used unsupervised learning approaches; the remaining 1 study did not describe its ML approach. Challenges in areas such as sampling, optimization of approaches to prediction and their features, generalizability, privacy, and other ethical issues call for further research.

CONCLUSIONS

ML approaches applied to text data from users on social media can work effectively in depression detection and could serve as complementary tools in public mental health practice.

Adolescent Eating Disorder Risk and the Social Online World: An Update

The role of traditional media (television and magazines) in creating eating disorder risk has long been a topic of discussion and research, but the proliferation of social media and rapid increase in the use of the Internet by adolescents generates new dynamics and new risks for the development and maintenance of eating disorders. Recent research describes the relationship between Internet and social media use and eating disorders risk, with the greatest associations found among youth with high levels of engagement and investment in photo-based activities and platforms. Here, we review different types of online content and how they are relevant to eating disorders and consider the theoretical frameworks predicting relationships between Internet and social media and eating disorders, before examining the empirical evidence for the risks posed by the online content in the development and maintenance of eating disorders. We describe proeating disorder content specifically and examine the research related to it; we then consider the implications of such content, highlight directions for future research, and discuss possible prevention and intervention strategies.

Technology-Based Approaches for Supporting Perinatal Mental Health

PURPOSE OF REVIEW

This review explores advances in the utilization of technology to address perinatal mood and anxiety disorders (PMADs). Specifically, we sought to assess the range of technologies available, their application to PMADs, and evidence supporting use.

RECENT FINDINGS

We identified a variety of technologies with promising capacity for direct intervention, prevention, and augmentation of clinical care for PMADs. These included wearable technology, electronic consultation, virtual and augmented reality, internet-based cognitive behavioral therapy, and predictive analytics using machine learning. Available evidence for these technologies in PMADs was almost uniformly positive. However, evidence for use in PMADs was limited compared to that in general mental health populations. Proper attention to PMADs has been severely limited by issues of accessibility, affordability, and patient acceptance. Increased use of technology has the potential to address all three of these barriers by facilitating modes of communication, data collection, and patient experience.

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

BACKGROUND

The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the past decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies on depression and anxiety using Reddit.

OBJECTIVE

The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety.

METHODS

A scoping review was conducted using the Arksey and O'Malley framework. MEDLINE, Embase, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM academic databases were searched. Inclusion criteria were developed using the participants, concept, and context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured an analytic focus on depression or anxiety and used naturalistic written expressions from Reddit users as a primary data source.

RESULTS

A total of 54 studies were included in the review. Tables and corresponding analyses delineate the key methodological features, including a comparatively larger focus on depression versus anxiety, an even split of original and premade data sets, a widespread analytic focus on classifying the mental health states of Reddit users, and practical implications that often recommend new methods of professionally delivered monitoring and outreach for Reddit users.

CONCLUSIONS

Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology that favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding the interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications, suggesting new forms of engagement with Reddit users.

Machine Learning Methods for Predicting Postpartum Depression: Scoping Review

BACKGROUND

Machine learning (ML) offers vigorous statistical and probabilistic techniques that can successfully predict certain clinical conditions using large volumes of data. A review of ML and big data research analytics in maternal depression is pertinent and timely, given the rapid technological developments in recent years.

OBJECTIVE

This study aims to synthesize the literature on ML and big data analytics for maternal mental health, particularly the prediction of postpartum depression (PPD).

METHODS

We used a scoping review methodology using the Arksey and O'Malley framework to rapidly map research activity in ML for predicting PPD. Two independent researchers searched PsycINFO, PubMed, IEEE Xplore, and the ACM Digital Library in September 2020 to identify relevant publications in the past 12 years. Data were extracted from the articles' ML model, data type, and study results.

RESULTS

A total of 14 studies were identified. All studies reported the use of supervised learning techniques to predict PPD. Support vector machine and random forest were the most commonly used algorithms in addition to Naive Bayes, regression, artificial neural network, decision trees, and XGBoost (Extreme Gradient Boosting). There was considerable heterogeneity in the best-performing ML algorithm across the selected studies. The area under the receiver operating characteristic curve values reported for different algorithms were support vector machine (range 0.78-0.86), random forest method (0.88), XGBoost (0.80), and logistic regression (0.93).

CONCLUSIONS

ML algorithms can analyze larger data sets and perform more advanced computations, which can significantly improve the detection of PPD at an early stage. Further clinical research collaborations are required to fine-tune ML algorithms for prediction and treatment. ML might become part of evidence-based practice in addition to clinical knowledge and existing research evidence.