The rising popularity of mental wellbeing technologies has led many individuals to explore binaural beats—an emerging form of sound therapy proliferating on web and mobile platforms. However, it currently remains unknown whether users can trust binaural tracks on online platforms, or if they deceive unsuspecting users. Our research aims to address this problem by understanding (1) what binaural beats listeners expect from tracks and (2) whether online tracks conform to these expectations. To understand user expectations, we perform thematic analysis on online forum threads and blog posts to extract binaural beats goals and expectations tied to these goals. Next, we design a methodology to measure binaural beats tracks’ conformance to commonly held user expectations. This methodology comprises, (1) obtaining a track’s intent to induce a mental state through track metadata analysis, (2) extracting a track’s binaural beats time-frequency model using Fast Fourier Transform, (3) mapping user expectations to rules that identify deceptive tracks, and validating them on the track’s extracted intent and time-frequency model. We evaluate ∼7K binaural beats tracks and find that only 7.5% conform to commonly held user expectations, while the remaining 92.5% deceive users with deviant claims (e.g., eroticism, weight loss) or deliver contradicting binaural beats. Our study underscores the significance of understanding users’ expectations and verifying conformance of online wellness technologies to expose discrepancies in expectations.

Given the limited understanding of how the COVID-19 pandemic impacted mental health on college campuses, this paper examines the evolution of the mental health of college students since the onset of the pandemic. We conducted a large-scale study on over 1.2M posts on 173 U.S. college subreddits over 17 months. In particular, we adopted a quasi-experimental approach to examine how the different stages of the pandemic (isolation period, normalization period, and vaccination period) impacted changes in social media discussions of college students. We measured the temporal shifts in the symptomatic mental health expressions and topics of discussion on college subreddits. We find that while the expressions of depression, anxiety, stress, and suicidal ideation significantly increased in the isolation period. Interestingly, these expressions gradually subsided in the normalization period, only to resurface in the vaccination period. We also find unique occurrences of discussion across social, academic, health, and COVID-19-induced topics. Our findings reveal that despite the fragility of college students' mental health in the face of crisis, college students show resilience with sufficient time. We discuss the implications of our work in terms of building tools for real-time comprehension of college students' mental health, and in designing timely and tailored mental health support for college students.

Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with ∼ 2M posts by ∼ 10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.

Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safeguard against actors violating their rules. However, the correlation between publicly outlined policies and employed action is less clear to general people.