The Language of LGBTQ+ Minority Stress Experiences on Social Media

Saha, Koustuv; Kim, Sang Chan; Reddy, Manikanta D.; Carter, Albert J.; Sharma, Eva; Haimson, Oliver L.; Choudhury, Munmun De

doi:10.1145/3361108

Cited by 82 publications

(68 citation statements)

References 77 publications

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“…Given the sensitive nature of the information shared, including users’ vulnerabilities and personal information, privacy and ethical considerations are paramount. In this work, we followed the guidelines and directives in Eysenbach and Till [ 77 ], which describe recommendations to ethically conduct medical research with user-generated online data, and we relied on the vast experience of research works dealing with sensitive data gathered on social media [ 47 , 78 - 81 ]. The researchers had no interactions with the users and have no interest in harming any, and the analyses were performed and reported in the spirit of knowledge, prevention, and harm reduction.…”

Section: Discussionmentioning

confidence: 99%

“…Due to fair guarantees of anonymity, no limits on the number of characters in a post, and a large variety of debated topics, this platform is often used to uninhibitedly discuss personal experiences [ 42 ]. Reddit constitutes a nonintrusive and privileged data source to study a variety of issues [ 43 , 44 ], including sensitive topics such as mental health [ 45 ], weight loss [ 46 ], gender issues [ 47 ], and substance abuse [ 22 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions

Balsamo¹,

Bajardi²,

Salomone³

et al. 2021

J Med Internet Res

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Background The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health. Objective This study leverages Reddit, a social media platform, as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering. Methods We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption and developed a methodology based on word embedding to find alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering. Results We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed the evolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learned the language model of opioid consumption and provided alternative vocabularies for opioid substances, routes of administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration was proposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity of the adoption of opioid substances and routes of administration, observing relevant trends, such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured the strength of association between drug tampering, routes of administration, and substance consumption, finding evidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphine sublingually. Conclusions This work investigated some important consumption-related aspects of the opioid epidemic using Reddit data. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions

Balsamo¹,

Bajardi²,

Salomone³

et al. 2021

J Med Internet Res

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“…For the above research questions, we measure psychosocial effects in terms of symptomatic mental health expressions of anxiety, depression, stress, and suicidal ideation, and expressions seeking emotional and informational support. Our work is founded on a large body of work on studying mental health and psychosocial dynamics with social media data [24][25][26][27][28][29][30]. Several studies have leveraged Twitter (which is also the data of our current study) to study health attributes and public health [30], including symptoms related to diseases [31], disease contagion [32], obesity and physical health [33], mood and depressive disorders [28,34], mental health self-disclosures [27], post-traumatic stress disorder [35], addictive behaviors and substance use [36,37], etc.…”

Section: Aimmentioning

confidence: 99%

“…Towards our first research aim of understanding the psychosocial impacts of the COVID-19 outbreak, we conduct two types of analysis on our Twitter dataset, which we describe below. Our work builds upon the vast, rapidly growing literature studying mental health concerns and psychosocial expressions within social media data [19,21,[24][25][26][27][28]34,48,[50][51][52].…”

Section: Psychosocial Effects Of Covid-19mentioning

confidence: 99%

“…Social media provides a real-time platform where people often candidly self-disclose their concerns, opinions, and struggles during this pandemic [25]. Our research is, therefore, founded on prior work to understand people's psychosocial distress in terms of their symptomatic mental health expressions of anxiety, depression, stress, and suicidal ideation, and their expressions seeking emotional and informational support [20,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Social Media Reveals Psychosocial Effects of the COVID-19 Pandemic

Saha

Torous

et al. 2020

Preprint

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Background: The novel coronavirus disease 2019 (COVID-19) pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multi-faceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a mental health tsunami, the psychological effects of the COVID-19 crisis remains unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective: Our work aims to provide insights regarding people's psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support-seeking expressions in the pandemic context. Methods: We obtain 60M Twitter streaming posts originating from the U.S. from March, 24 to May, 25, 2020, and compare these with 40M posts from a comparable period in 2019 to causally attribute the effect of COVID-19 on people's social media self-disclosure. Using these datasets, we study people's self-disclosure on social media in terms of symptomatic mental health concerns and expressions seeking support. We employ transfer learning classifiers that identify the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examine the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 datasets. Results: We find that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis, mental health symptomatic expressions have increased by ~14%, and support seeking expressions have increased by 5%, both thematically related to COVID-19. We also observe a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlight that people express concerns that are contextually related to the COVID-19 crisis. Conclusions: We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people's mental health symptomatic and support-seeking expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their new normal. Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, healthcare and precautionary measures, and pandemic-related awareness. This work shows the potential to provide insights to mental healthcare and stakeholders and policymakers in planning and implementing measures to mitigate mental health risks amidst the health crisis.

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