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

Reduced speech coherence in psychosis-related social media forum posts

The extraction of linguistic markers from social media posts, which are indicative of the onset and course of mental disorders, offers great potential for mental healthcare. In the present study, we extracted over one million posts from the popular social media platform Reddit to analyze speech coherence, which reflects formal thought disorder and is a characteristic feature of schizophrenia and associated psychotic disorders. Natural language processing (NLP) models were used to perform an automated quantification of speech coherence. We could demonstrate that users who are active on forums geared towards disorders with a higher degree of psychotic symptoms tend to show a lower level of coherence. The lowest coherence scores were found in users of forums on dissociative identity disorder, schizophrenia, and bipolar disorder. In contrast, a relatively high level of coherence was detected in users of forums related to obsessive-compulsive disorder, anxiety, and depression. Users of forums on posttraumatic stress disorder, autism, and attention-deficit hyperactivity disorder exhibited medium-level coherence. Our findings provide promising first evidence for the possible utility of NLP-based coherence analyses for the early detection and prevention of psychosis on the basis of posts gathered from publicly available social media data. This opens new avenues for large-scale prevention programs aimed at high-risk populations.

Evaluating generative AI responses to real-world drug-related questions

Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use treatment. Treatment for substance use disorders (SUDs) is particularly high stakes, requiring evidence-based clinical treatment, mental health expertise, and peer support. Thus, promises of AI systems addressing deficient healthcare resources and structural bias are relevant within this domain, especially in an anonymous setting. This study explores the effectiveness of generative AI in answering real-world substance use and recovery questions. We collect questions from online recovery forums, use ChatGPT and Meta's LLaMA-2 for responses, and have SUD clinicians rate these AI responses. While clinicians rated the AI-generated responses as high quality, we discovered instances of dangerous disinformation, including disregard for suicidal ideation, incorrect emergency helplines, and endorsement of home detox. Moreover, the AI systems produced inconsistent advice depending on question phrasing. These findings indicate a risky mix of seemingly high-quality, accurate responses upon initial inspection that contain inaccurate and potentially deadly medical advice. Consequently, while generative AI shows promise, its real-world application in sensitive healthcare domains necessitates further safeguards and clinical validation.

Autistic Women's Experience of Motherhood: A Qualitative Analysis of Reddit

Autistic mothers remain under-represented in parental and autism research despite the associated physical and psychosocial challenges that accompany the transition to motherhood. Extant literature suggests autistic mothers experience sensory difficulties, communication challenges, stigma, and comorbidities as difficulties, but these studies have focused on autistic women in the perinatal period. The aim of this study was to examine reflections on motherhood from a Reddit community for autistic parents. Identified themes were Autistic Mothering is Different, Autistic Mothers Need Autistic Mothers, Autistic Mothers Experience Stigma, and Learnings from Lockdown. Findings extend existing research by offering insight into the ways autism impacts mothers beyond the perinatal period and have important implications for the future design and delivery of support services for autistic mothers.

What words can tell us about social determinants of mental health: A multi-method analysis of sentiment towards migration experiences and community life in Lima, Perú

To support resilience in contexts of migration, a deeper understanding of the experiences of both receiving communities and migrants is required. Research on the impacts of migration on community life is limited in contexts with high internal migration (i.e., migrating within one's country of origin). Evidence suggests that cultural similarity, community relationships, and access to resources may be protective factors that could be leveraged to support the mental health of internal migrants. The current study uses data drawn from a sample of pregnant Peruvian women (N = 251), 87 of whom reported being internal migrants and 164 of whom reported being from the locale of the study (Lima, Perú). The aim was to better understand the social experience of internal migration for both local and migrant women. Inductive thematic analysis was used to examine migration experience and perceived impact of migration on community life. Internal migrants discussed three themes relative to their experiences: motivations, adjustment, and challenges. Experiences of women in receiving communities consisted of four themes related to migration: positive, negative, neutral, and mixed perceptions. Linguistic Inquiry and Word Count (LIWC-22) software was also used to assess sentiment towards migration. Across both analytic methods, migration motivations and perceptions were multifaceted and migrants reported a wide range of challenges before, during, and after migration. Findings indicated that attitudes toward migration are broadly positive, and that there is a more positive appraisal of migration's impact on the community life for internal as opposed to international migration.

Use of Social Media Data Mining to Examine Needs, Concerns, and Experiences of People With Traumatic Brain Injury

PURPOSE

Given the limited availability of topic-specific resources, many people turn to anonymous social media platforms such as Reddit to seek information and connect to others with similar experiences and needs. Mining of such data can therefore identify unmet needs within the community and allow speech-language pathologists to incorporate clients' real-life insights into clinical practices.

METHOD

A mixed-method analysis was performed on 3,648 traumatic brain injury (TBI) subreddit posts created between 2013 and 2021. Sentiment analysis was used to determine the sentiment expressed in each post; topic modeling and qualitative content analysis were used to uncover the main topics discussed across posts. Subgroup analyses were conducted based on injury severity, chronicity, and whether the post was authored by a person with TBI or a close other.

RESULTS

There was no significant difference between the number of posts with positive sentiment and the number of posts with negative sentiment. Comparisons between subgroups showed significantly higher positive sentiment in posts by or about people with moderate-to-severe TBI (compared to mild TBI) and who were more than 1 month postinjury (compared to less than 1 month). Posts by close others had significantly higher positive sentiment than posts by people with TBI. Topic modeling identified three meta-themes: Recovery, Symptoms, and Medical Care. Qualitative content analysis further revealed that returning to productivity and life as well as sharing recovery tips were the primary focus under the Recovery theme. Symptom-related posts often discussed symptom management and validation of experiences. The Medical Care theme encompassed concerns regarding diagnosis, medication, and treatment.

CONCLUSIONS

Concerns and needs shift over time following TBI, and they extend beyond health and functioning to participation in meaningful daily activities. The findings can inform the development of tailored educational resources and rehabilitative approaches, facilitating recovery and community building for individuals with TBI.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24881340.

Investigating Substance Use via Reddit: Systematic Scoping Review

BACKGROUND

Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research.

OBJECTIVE

This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention.

METHODS

A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions.

RESULTS

The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources.

CONCLUSIONS

This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.

Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support

Social media platforms are increasingly being used by intimate partner violence (IPV) victims to share experiences and seek support. If such information is automatically curated, it may be possible to conduct social media based surveillance and even design interventions over such platforms. In this paper, we describe the development of a supervised classification system that automatically characterizes IPV-related posts on the social network Reddit. We collected data from four IPV-related subreddits and manually annotated the data to indicate whether a post is a self-report of IPV or not. Using the annotated data (N=289), we trained, evaluated, and compared supervised machine learning systems. A transformer-based classifier, RoBERTa, obtained the best classification performance with overall accuracy of 78% and IPV-self-report class 𝐹1 -score of 0.67. Post-classification error analyses revealed that misclassifications often occur for posts that are very long or are non-first-person reports of IPV. Despite the relatively small annotated data, our classification methods obtained promising results, indicating that it may be possible to detect and, hence, provide support to IPV victims over Reddit.