Cory Jacob Cascalheira

Bots (i.e., automated software programs that perform various tasks) and fraudulent responders pose a growing and costly threat to psychological research as well as affect data integrity. However, few studies have been published on this topic. (a) Describe our experience with bots and fraudulent responders using a case study, (b) present various bot and fraud detection tactics (BFDTs) and identify the number of suspected bot and fraudulent respondents removed, (c) propose a consensus confidence system for eliminating bots and fraudulent responders to determine the number of BFDTs researchers should use, and (d) examine the initial effectiveness of dynamic versus static BFDT protocols. This study is part of a larger 14-day experience sampling method study with trauma-exposed sexual minority cisgender women and transgender and/or nonbinary people. Faced with several bot and fraudulent responder infiltrations during data collection, we developed an evolving BFDT protocol to eliminate bots and fraudulent responders. Throughout this study, we received 24,053 responses on our baseline survey. After applying our BFDT protocols, we eliminated 99.75% of respondents that were likely bots or fraudulent responders. Some BFDTs seemed to be more effective and afford higher confidence than others, dynamic protocols seemed to be more effective than static protocols, and bots and fraudulent responders introduced significant bias in the results. This study advances online psychological research by curating one of the largest samples of bot and fraudulent respondents and pilot testing the largest number of BFDTs to date. Recommendations for future research are provided. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

This study examined whether the 20-item Posttraumatic Stress Disorder Checklist (PCL-5), eight-item Patient Health Questionnaire (PHQ-8), and seven-item Generalized Anxiety Disorder Scale (GAD-7) exhibited factorial invariance across sexual orientation identity (heterosexual vs. sexual minority) and gender identity (cisgender men vs. cisgender women vs. transgender/gender diverse) in veterans. Data from a cohort study of veterans ( N = 1,062; 20.9% transgender and gender diverse; 66.3%–66.7% sexual minority) were used to conduct multigroup confirmatory factor analysis. Findings indicated that levels of factorial invariance were met for all measures, but partial residual invariance was required for the PHQ-8 and GAD-7. Thus, these instruments are appropriate for routine clinical assessment among veterans and in research with questions that can be answered with scale-level information, but item-level research questions involving the PHQ-8 and GAD-7 (e.g., daily diary studies) require caution when the studies involve sexually and gender diverse veterans.

Individuals from sexual and gender minority groups experience disproportionately high rates of poor health outcomes and mental disorders compared to their heterosexual and cisgender counterparts, largely as a consequence of minority stress as described by Meyer's (2003) model. This study presents the first comprehensive evaluation of transformer-based architectures for detecting minority stress in online discourse. We benchmark multiple transformer models including ELECTRA, BERT, RoBERTa, and BART against traditional machine learning baselines and graph-augmented variants. We further assess zero-shot and few-shot learning paradigms to assess their applicability on underrepresented datasets. Experiments are conducted on the two largest publicly available Reddit corpora for minority stress detection, comprising 12,645 and 5,789 posts, and are repeated over five random seeds to ensure robustness. Our results demonstrate that integrating graph structure consistently improves detection performance across transformer-only models and that supervised fine-tuning with relational context outperforms zero and few-shot approaches. Theoretical analysis reveals that modeling social connectivity and conversational context via graph augmentation sharpens the models' ability to identify key linguistic markers such as identity concealment, internalized stigma, and calls for support, suggesting that graph-enhanced transformers offer the most reliable foundation for digital health interventions and public health policy.

Individuals from sexual and gender minority groups experience disproportionately high rates of poor health outcomes and mental disorders compared to their heterosexual and cisgender counterparts, largely as a consequence of minority stress as described by Meyer’s (Psychol Bull 129(5):674, 2003) model. This study presents the first comprehensive evaluation of transformer-based architectures for detecting minority stress in online discourse. We benchmark multiple transformer models including ELECTRA, BERT, RoBERTa, and BART against traditional machine learning baselines and graph-augmented variants. We further assess zero-shot and few-shot learning paradigms to assess their applicability on underrepresented datasets. Experiments are conducted on the two largest publicly available Reddit corpora for minority stress detection, comprising 12,645 and 5789 posts, and are repeated over five random seeds to ensure robustness. Our results demonstrate that integrating graph structure consistently improves detection performance across transformer-only models and that supervised fine-tuning with relational context outperforms zero and few-shot approaches. Theoretical analysis reveals that modeling social connectivity and conversational context via graph augmentation sharpens the models’ ability to identify key linguistic markers such as identity concealment, internalized stigma, and calls for support, suggesting that graph-enhanced transformers offer the most reliable foundation for digital health interventions and public health policy.

Background: Sexual and gender minority (SGM) individuals are at heightened risk for substance use and sexually transmitted infections than their non-SGM peers. Collecting mobile phone usage data passively may open new opportunities for personalizing interventions, as behavioral risks could be identified without user input. Objective: This study aimed to determine (1) whether passively sensed mobile phone data can be used to identify substance use and sexual risk behaviors for sexually transmitted infection (STI) and HIV transmission among young SGM who have sex with men, (2) which outcomes can be predicted with a high level of accuracy, and (3) which passive data sources are most predictive of these outcomes. Methods: We developed a mobile phone app to collect participants’ messaging, location, and app use data and trained a machine learning model to predict risk behaviors for STI and HIV transmission. We used Scikit-learn to train logistic regression and gradient boosting classification models with simple linear model specification to predict participants' substance use and sexual behaviors (ie, condomless anal sex, number of sexual partners, and methamphetamine use), which were validated using self-report questionnaires. F₁-scores were used to quantify prediction accuracy of the model using different data sources (and combinations of these sources) for prediction. Differences between text, location, app use, and Linguistic Inquiry and Word Count (LIWC) domains by outcome were investigated using independent t tests where associations were considered significant at P<.05. Results: Among participants (n=82) who identified as SGM, were sexually active, and reported recent substance use, our model was highly predictive of methamphetamine use and having ≥6 sexual partners (F₁-scores as high as 0.83 and 0.69, respectively). The model was less predictive of condomless anal sex (highest F₁-score 0.38). Overall, text-based features were found to be most predictive, but app use and location data improved predictive accuracy, particularly for detecting ≥6 sexual partners. Methamphetamine use was significantly associated with dating app use (P=.01) and use of sex-related words (P=.002). Having ≥6 sex partners was associated with dating app use (0.02), use of sex-related words (P=.001), and traveling a further distance from home (P=.03), on average, compared to participants with fewer sex partners. Methamphetamine users were more likely to use social (P=.002) and affect words (P=.003) and less likely to use drive-related words (P=.02). People having 6 or more partners were more likely to use social, affect words, and cognitive process-related words (P=.003 and .004 respectively). Conclusions: Our results show that passively collected mobile phone data may be useful in detecting sexual risk behaviors. Expanding data collection may improve the results further, as certain behaviors, such as injection drug use, were quite rare in the study sample. These models may be used to personalize STI and HIV prevention as well as substance use harm reduction interventions.

The present study examined rates and factors associated with past-year Veterans Health Administration (VHA) overall healthcare utilization and VA mental health treatment among LGBTQ+ and cisgender heterosexual veterans. Baseline data from a national longitudinal study of LGBTQ+ and cisgender heterosexual veteran VHA users and non-users (N = 1,062) were used in generalized linear models to estimate the predicted prevalence of VHA healthcare utilization for each LGBTQ+ and cisgender subgroup, stratified by gender. Additional multivariable regressions were guided by the Andersen health services utilization model. There were no significant differences in healthcare utilization among women subgroups. Among men subgroups, cisgender gay men had lower predicted prevalence of VHA utilization than cisgender heterosexual and transgender men. In both the women's and men's models, VHA eligibility indicators and perceived lack of service availability were associated with increased odds for any past-year utilization; higher income, difficulty understanding eligibility/benefits, and logistical barriers were associated with decreased odds. Lifetime harassment at VHA was positively associated with past-year overall VHA utilization in women's models. LGBTQ+ specific factors explained significant variance in some models while transgender/gender diverse specific needs did not. These findings suggest that veterans generally need assistance navigating VHA eligibility issues and that LGBTQ+ veterans would benefit from systemic attention to provider sensitivity and availability of services focused on their needs. The high rates of harassment reported across women subgroups (19% to 25%) and by transgender men (38%) warrant institution-wide action.

Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source that drives these health disparities is minority stress (i.e., chronic and social stressors unique to LGBTQ+ communities' experiences adapting to the dominant culture). This stress is frequently expressed in LGBTQ+ users' posts on social media platforms. However, these expressions are not just straightforward manifestations of minority stress. They involve linguistic complexity (e.g., idiom or lexical diversity), rendering them challenging for many traditional natural language processing methods to detect. In this work, we designed a hybrid model using Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a pre-trained deep language model to improve the classification performance of minority stress detection. We experimented with our model on a benchmark social media dataset for minority stress detection (LGBTQ+ MiSSoM+). The dataset is comprised of 5,789 human-annotated Reddit posts from LGBTQ+ subreddits. Our approach enables the extraction of hidden linguistic nuances through pretraining on a vast amount of raw data, while also engaging in transductive learning to jointly develop representations for both labeled training data and unlabeled test data. The RoBERTa-GCN model achieved an accuracy of 0.86 and an F1 score of 0.86, surpassing the performance of other baseline models in predicting LGBTQ+ minority stress. Improved prediction of minority stress expressions on social media could lead to digital health interventions to improve the wellbeing of LGBTQ+ people-a community with high rates of stress-sensitive health problems.

Background and Purpose: This study aimed to develop and test a novel model integrating social-learning and self-medication frameworks by examining the association between self-efficacy to resist alcohol and other drug (AOD) use and daily AOD use and unhealthy drinking risk among trauma-exposed sexual minority women (SMW) and transgender and gender-diverse (TGD) people. We examined whether minority stressors moderated these associations. Methods: Data were from 57 trauma-exposed SMW and TGD people who participated in a 14-day daily diary study. Multilevel binary logistic models and ordinal logistic models were employed to examine associations between self-efficacy to resist AOD use and daily AOD use and unhealthy drinking risk at within- and between-person levels. We assessed same- and cross-level interactions between daily self-efficacy to resist AOD use and minority stressors in predicting AOD use and unhealthy drinking risk within the same 24-hour period (i.e., standardized as 6 pm to 6 pm; hereafter referred to as “same-day”). Results: Self-efficacy to resist AOD use was associated with lower AOD use and unhealthy drinking risk. Minority stressors were associated with daily AOD use. Among those who experienced higher (vs. lower) average sexual minority stressors over the 2-week daily diary period, higher-than-usual self-efficacy to resist AOD use was less protective in decreasing risk of same-day unhealthy drinking. Conclusions: Interventions aiming to mitigate AOD use and unhealthy drinking risk by bolstering self-efficacy to resist AOD use should consider the impact of recent cumulative exposure to sexual minority stressors in this population. Further, policy efforts are needed to reduce perpetuation of stigma.

Bots are automated software programs that pose an ongoing threat to psychological research by invading online research studies and their increasing sophistication over time. Despite this growing concern, research in this area has been limited to bot detection in existing data sets following an unexpected encounter with bots. The present three-condition, quasi-experimental study aimed to address this gap in the literature by examining the efficacy of three types of bot screening tools across three incentive conditions ($0, $1, and $5). Data were collected from 444 respondents via Twitter advertisements between July and September 2021. The efficacy of five task-based (i.e., anagrams, visual search), question-based (i.e., attention checks, ReCAPTCHA), and data-based (i.e., consistency, metadata) tools was examined with Bonferroni-adjusted univariate and multivariate logistic regression analyses. In general, study results suggest that bot screening tools function similarly for participants recruited across incentive conditions. Moreover, the present analyses revealed heterogeneity in the efficacy of bot screening tool subtypes. Notably, the present results suggest that the least effective bot screening tools were among the most commonly used tools in existing literature (e.g., ReCAPTCHA). In sum, the study findings revealed highly effective and highly ineffective bot screening tools. Study design and data integrity recommendations for researchers are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).