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      Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts

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          Abstract

          Background

          Substance use disorder is characterized by distinct cognitive processes involved in emotion regulation as well as unique emotional experiences related to the relapsing cycle of drug use and recovery. Web-based communities and the posts they generate represent an unprecedented resource for studying subjective emotional experiences, capturing population types and sizes not typically available in the laboratory. Here, we mined text data from Reddit, a social media website that hosts discussions from pseudonymous users on specific topic forums, including forums for individuals who are trying to abstain from using drugs, to explore the putative specificity of the emotional experience of substance cessation.

          Objective

          An important motivation for this study was to investigate transdiagnostic clues that could ultimately be used for mental health outreach. Specifically, we aimed to characterize the emotions associated with cessation of 3 major substances and compare them to emotional experiences reported in nonsubstance cessation posts, including on forums related to psychiatric conditions of high comorbidity with addiction.

          Methods

          Raw text from 2 million posts made, respectively, in the fall of 2020 (discovery data set) and fall of 2019 (replication data set) were obtained from 394 forums hosted by Reddit through the application programming interface. We quantified emotion word frequencies in 3 substance cessation forums for alcohol, nicotine, and cannabis topic categories and performed comparisons with general forums. Emotion word frequencies were classified into distinct categories and represented as a multidimensional emotion vector for each forum. We further quantified the degree of emotional resemblance between different forums by computing cosine similarity on these vectorized representations. For substance cessation posts with self-reported time since last use, we explored changes in the use of emotion words as a function of abstinence duration.

          Results

          Compared to posts from general forums, substance cessation posts showed more expressions of anxiety, disgust, pride, and gratitude words. “Anxiety” emotion words were attenuated for abstinence durations >100 days compared to shorter durations ( t 12=3.08, 2-tailed; P=.001). The cosine similarity analysis identified an emotion profile preferentially expressed in the cessation posts across substances, with lesser but still prominent similarities to posts about social anxiety and attention-deficit/hyperactivity disorder. These results were replicated in the 2019 (pre–COVID-19) data and were distinct from control analyses using nonemotion words.

          Conclusions

          We identified a unique subjective experience phenotype of emotions associated with the cessation of 3 major substances, replicable across 2 time periods, with changes as a function of abstinence duration. Although to a lesser extent, this phenotype also quantifiably resembled the emotion phenomenology of other relevant subjective experiences (social anxiety and attention-deficit/hyperactivity disorder). Taken together, these transdiagnostic results suggest a novel approach for the future identification of at-risk populations, allowing for the development and deployment of specific and timely interventions.

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          Most cited references58

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          Efficient Estimation of Word Representations in Vector Space

          We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
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            Drug addiction, dysregulation of reward, and allostasis.

            M. Moal, K Koob (2001)
            This paper reviews recent developments in the neurocircuitry and neurobiology of addiction from a perspective of allostasis. A model is proposed for brain changes that occur during the development of addiction that explain the persistent vulnerability to relapse long after drug-taking has ceased. Addiction is presented as a cycle of spiralling dysregulation of brain reward systems that progressively increases, resulting in the compulsive use and loss of control over drug-taking. The development of addiction recruits different sources of reinforcement, different neuroadaptive mechanisms, and different neurochemical changes to dysregulate the brain reward system. Counteradaptive processes such as opponent-process that are part of normal homeostatic limitation of reward function fail to return within the normal homeostatic range and are hypothesized to form an allostatic state. Allostasis from the addiction perspective is defined as the process of maintaining apparent reward function stability by changes in brain reward mechanisms. The allostatic state represents a chronic deviation of reward set point and is fueled not only by dysregulation of reward circuits per se, but also by the activation of brain and hormonal stress responses. The manifestation of this allostatic state as compulsive drug-taking and loss of control over drug-taking is hypothesized to be expressed through activation of brain circuits involved in compulsive behavior such as the cortico-striatal-thalamic loop. The view that addiction is the pathology that results from an allostatic mechanism using the circuits established for natural rewards provides a realistic approach to identifying the neurobiological factors that produce vulnerability to addiction and relapse.
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              The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place

              We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated over the course of several recent years on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-level measures such as obesity rates.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                2023
                19 July 2023
                : 25
                : e45267
                Affiliations
                [1 ] Department of Psychiatry Icahn School of Medicine at Mount Sinai New York City, NY United States
                [2 ] Department of Neuroscience Icahn School of Medicine at Mount Sinai New York City, NY United States
                [3 ] Department of Anesthesiology Yale School of Medicine Yale University New Haven, CT United States
                [4 ] Yale Center for Analytical Sciences, Yale School of Public Health Yale University New Haven, CT United States
                Author notes
                Corresponding Author: Rita Z Goldstein rita.goldstein@ 123456mssm.edu
                Author information
                https://orcid.org/0000-0001-9842-356X
                https://orcid.org/0000-0001-8082-826X
                https://orcid.org/0000-0003-3176-6570
                https://orcid.org/0000-0002-1680-229X
                Article
                v25i1e45267
                10.2196/45267
                10398365
                37467010
                0098e03f-fc11-4812-bcd7-c31d7e9833f4
                ©Genevieve Yang, Sarah G King, Hung-Mo Lin, Rita Z Goldstein. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.07.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 22 December 2022
                : 12 April 2023
                : 2 May 2023
                : 9 June 2023
                Categories
                Original Paper
                Original Paper

                Medicine
                sentiment analysis,text mining,addiction phenotype,subjective experience phenotype,naturalistic big data,natural language processing,phenomenology,experience sampling

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