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While there are an increasing number of apps designed to help drug and alcohol users in recovery , drug dealers are also taking advantage of apps to increase their customer base. In a article for the Guardian, Leah Borromeo monstris explored the use of such mainstream apps as Instagram, Tinder, Kik and Depop for drug dealing. I have just 4 April updated the post with another app running in to legal challenges — weedmap. On Instagram, people looking to buy drugs simply search via hashtags such as weed4sale or the names of the drugs themselves mdma, mephedrone etc. The customer then contacts the owner of the account and the deal moves along through direct messages. In the case of Tinder, potential customers can swipe through profiles until they find a dealer and match with them. Buyers can either meet face-to-face or pay online and have their purchases posted to them. While online payments such as bitcoin and pre-paid gift cards such as Vanilla Visa are encrypted, Ms Borromeo says that more traceable measures such as unattributed bank transfers and PayPal are also used. In the UK, this means of selling New Psychoactive Substances or legal highs will soon probably be illegal once the government decides to implement the New Psychoactive Substance Act. It should have come into force on 6 April and is now scheduled for some time in Spring this year — the reasons for the delay are set out in this Guardian article by Alan Travis. In addition to the mis use of mainstream apps, there are also a number of apps dedicated to accessing drugs. For a full rundown, check out this article by Annie Lesser but dedicated apps which piqued my interest for their ingenuity and entrepreneurship include:. Weedmaps sometimes called Yelp for pot the smartphone app Weedmaps enables users to locate dispensaries and delivery services selling the green stuff. The company has been among the breakout success stories of legalization thus far. While federal illegality in the US makes it very difficult for a company to sell cannabis in more than one state, Weedmaps faces no such constraints. When a visitor lands in Portland, Denver or San Francisco, they might not know the local dispensary or product names, but they know to check Weedmaps to find out. Responding to Ajax, Weedmaps did something almost unheard of for a cannabis company: it politely told the regulator to get lost. Nestdrop started as an alcohol delivery service before morphing into an app to help you get medical marijuana delivered. With MyDx, you can find the perfect strain to fit any mood. The device and app developers behind MyDx are also working to apply this technology to test food and water for unwanted chemicals as well as air quality. This app is basically Tinder for stoners. You enter your energy level when on weed, what you want to do with the other party chat, go out, or stay in and list the activities you enjoy when high. When there are so many apps out there to help people access drugs more easily, the value of the work by the Global Drug Survey becomes even more obvious. GDS drugs meter app allows users to see how their drug use compares to other people just like them, offering objective, personalised feedback that takes their personal features in to account. With an overview of total drug use and in depth analysis for nine drugs at present, drugs meter gives objective feedback informed by medical experts. It is committed to giving honest, accurate information. All data is anonymous, secure and cannot be traced back to any individual. Please share any drug apps that you think may be of particular interest via the comments section below. Fascinating insights into changes in drug and alcohol taking behaviour during the global pandemic — interim findings from Global Drug Survey. GDS provides new data on the latest drug trends and crucial public health and policy issues, as well as a range of fascinating facts. The European Monitoring Centre for Drugs and Drug Addiction surveys the latest apps responding to drug use and associated harms. Submit your job opportunities here Contact me Advent Quiz. Click to Subscribe. Using apps to buy and sell drugs. Russell Webster April 19, While there are an increasing number of apps designed to help drug and alcohol users in recovery, drug dealers are also taking advantage of apps to increase their customer base. Share This Post. Related posts. Drug and alcohol use during coronavirus Fascinating insights into changes in drug and alcohol taking behaviour during the global pandemic — interim findings from Global Drug Survey. Seven things I learnt from the Global Drug Survey GDS provides new data on the latest drug trends and crucial public health and policy issues, as well as a range of fascinating facts. Smartphone apps for problem drug users The European Monitoring Centre for Drugs and Drug Addiction surveys the latest apps responding to drug use and associated harms. Drug and alcohol treatment goes digital Substance misuse treatment providers embrace digital for a better user experience. Eight things I learnt from the Global Drug Survey GDS provides new data on the latest drug trends and crucial public health and policy issues, as well as a range of fascinating facts. I am currently on recovery. I am on treatment with methadone. Twitter Facebook-f Linkedin-in Youtube. Back to Top. Consent Management Privacy Policy. Privacy Policy Required. You read and agreed to our Privacy Policy. Get every blog post by email for free. First Name. Last Name. No Thanks.

Intimate insight: MDMA changes how people talk about significant others

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Official websites use. Share sensitive information only on official, secure websites. Maryland Ave. The drug alters speech production and fluency, and may influence speech content. Here, we investigated the effect of MDMA on speech content, which may reveal how this drug affects social interactions. Participants completed a 5-min standardized talking task during which they discussed a close personal relationship e. The conversations were analyzed for selected content categories e. Both analytic methods revealed that MDMA altered speech content relative to placebo. Using the machine learning algorithm, we found that MDMA increased use of social words and words relating to both positive and negative emotions. These findings are consistent with reports that MDMA acutely alters speech content, specifically increasing emotional and social content during a brief semistructured dyadic interaction. Studying effects of psychoactive drugs on speech content may offer new insights into drug effects on mental states, and on emotional and psychosocial interaction. In addition, before it was classified in the US as a controlled substance, MDMA was used as an adjunct to psychotherapy by therapists because it appeared to decrease defensiveness and enhance feelings of emotional closeness Greer and Tolbert ; Wolfson More recently, clinical trials have suggested that the drug may be an effective therapeutic adjunct in patients with post-traumatic stress disorder Mithoefer et al. Analysis of speech content may shed light on the processes by which this drug produces its apparently unique prosocial effects. Several controlled laboratory studies support the idea that MDMA produces prosocial effects. Single doses of MDMA increase feelings of friendliness and euphoria, and feeling close to others Bedi et al. On measures of cognitive-emotional function, it increases recognition of positive emotions such as friendliness in others Hysek et al. MDMA also reduces the negative affect produced by simulated social exclusion Frye et al. Most of the research to date has utilized standardized, computerized tasks that are typically administered to individual participants, tested in nonsocial contexts. To understand the effects of a drug on social processes, it may be more appropriate to use procedures involving interpersonal interactions. Speech is a key element of human social interaction. Several drugs, including MDMA, alter speech production and fluency. Amphetamine and alcohol increase speech production Higgins and Stitzer ; Wardle et al. Drugs can also alter the content of speech. Bedi et al. The current study used similar approaches to further investigate the effects of MDMA on speech content, using a separate sample of participants and two different methods of speech content analysis. Healthy experienced drug users received single doses of MDMA 1. They performed a standardized 5-min dyadic speaking task with a research assistant in which they spoke about their relationship with another person. From the transcriptions we examined speech production and content. We hypothesized that MDMA would increase 1 the amount the talking i. Potential participants completed an initial telephone and an in-person psychiatric evaluation and medical examination, including an electrocardiogram and physical examination. Inclusion criteria were: age 18 — 35, at least high school education, fluency in English, and BMI 18 — All participants were Caucasian because this was part of a larger genetic study. Exclusion criteria were: smoking more than 10 cigarettes per day, night shift work, any significant medical or psychiatric condition e. Participants were told that the purpose of the study was to evaluate individual differences in drug response. They were told they could receive a stimulant explained as including drugs such as MDMA and amphetamine , sedative, cannabinoid or placebo. Participants were instructed to consume their normal amount of caffeine, but refrain from tobacco use for 9 hrs, and other drug use for 48 hrs, prior to each session. Women who used hormonal contraceptives were tested regardless of menstrual cycle phase, but women not using hormonal contraceptives were tested only during the follicular phase days 2—14; White et al. Participants provided written informed consent prior to participation and they were debriefed after completion to explain the study. The current study was a part of a larger study investigating the behavioral and physiological effects of MDMA 0. Because the 0. Participants attended 4. Sessions were separated by at least five days. Sessions were conducted from am to pm. Upon arrival for the session, participants provided urine and breath samples to confirm abstinence from alcohol as measured by an Alco-Sensor III Breathalyzer, Intoximeters Inc. Sessions were rescheduled if the participant tested positive for drugs. At am, baseline pre-capsule measures of heart rate and blood pressure were obtained, and participants completed self-report mood and drug effects questionnaires. At am, participants ingested capsules containing either MDMA or placebo. Physiological and subjective measures reported previously, Kirkpatrick et al. The talking task described below was completed at am to coincide with expected peak drug effects. During times when no measures were scheduled the participants were allowed to relax and watch movies or read. At pm, participants were discharged provided that their heart rate and blood pressure had returned to baseline levels. The talking task was a modification Wardle et al. During an initial orientation session before the study began, participants provided the names of three important people in their lives. At each subsequent experimental session, the research assistant randomly selected one name, and asked the participant to talk about this person for 5 min. Research assistants, who were gender matched to participants, were trained in reflective listening, in which they encouraged the participant to speak freely, mirroring the mood of the speaker by reflecting the emotional state with words and nonverbal communication, and minimizing their own input into the conversation. Speech samples were professionally transcribed and any speech of the research assistants was deleted. As fully described elsewhere in Kirkpatrick et al. Drug conditions were administered in counter-balanced order, under double-blind conditions. Capsules were prepared by The University of Chicago Hospitals investigational pharmacy. MDMA powder 1. Placebo capsules contained only lactose. These MDMA doses were selected based on previous studies indicating that the drug reliably increases positive mood and alters emotional processing at these doses Bedi et al. Doses were given as mg per kg body weight to avoid systematic gender differences in dose and differences related to variations in body weight. We analyzed the data in python 2. For statistical testing, we generally used mixed effects models in which participant was a random effect and drug condition was a fixed effect. Results were considered statistically significant at p less than or equal to 0. Dictionary approaches score text based on the count of words from previously validated categories and are straightforward to interpret. However, these analyses may fail to capture changes in areas outside of the validated categories of the dictionary, such as slang or other specialized terminology. Machine learning techniques, such as cross-validation and variable selection, can make classifications based on characteristics of the data set rather than on predetermined categories. LIWC is a word count program that matches text against an extensive dictionary, and provides the percentage of words in a large set of well-validated categories. We a priori chose to examine 43 specific variables, relating to time frame, affect, social interaction, perception and cognition see Table 1 , Results. The talking task required participants to discuss a person who was important to them. However, past investigations have suggested some aspects of speech may be impaired by MDMA, including ability to coherently focus on single topics Marrone et al. In order to better detect the influence of MDMA on speech about an emotionally salient person, we isolated and analyzed individual phrases describing the topic person. These three categories, which were not mutually exclusive, were rated by individuals who were blinded to study design and condition. We took a bag-of-words approach in which we quantified word occurrence but not word order or context. We counted the number of occurrences of each word in each document and used word occurrences as predictor variables in statistical machine learning models that predicted dosing condition. Our modeling approach used random forests. Random forest is an ensemble classifier that generates a group of classification trees based on predictor variables and then uses the majority vote of the trees to determine membership. Each classification tree is fit using a random subset of predictor variables on a random subset of the observations drawn with replacement. Because it is based on decision trees, the random forest algorithm is well suited for capturing nonlinear interactions between predictor variables. We used individual words as predictors and used recursive feature elimination with random forest ensemble models to select a smaller subset of words that predicted dosing condition. To summarize these models, we estimated variable importance using gini impurity, a standard measure for random forests and other decision trees. When a node in a tree is split using a variable, the two child nodes are more homogeneous in the outcome measure compared to the parent node. This homogeneity is standardly quantified using gini impurity, which measures how often a randomly chosen element in a node would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the node. Gini impurity is computed by summing the probability of each item being chosen times the probability of a mistake in categorizing that item. Summing gini decreases between parent and child nodes for each variable across all trees yields a measure of variable importance in the ensemble model. We used recursive feature elimination Guyon and Elisseeff ; Kuhn and Johnson to build ensembles with decreasing numbers of predictor variables. We iteratively fit random forests, beginning with variables, fitting models, and discarding the 50 variables with the lowest variable importance. We repeated this process times. Out-of-bag accuracy is optimistic and poorly predicts accuracy in new datasets, but it is useful for comparing relative performance of models fit with different numbers of variables. To avoid a large number of comparisons, we focused on the statistically significant results from our dictionary-based speech analysis and reduced their dimensionality using principal components analysis with varimax rotation. We then attempted to predict these components using least angle lasso regression Tibshirani with fold cross-validation to select the lambda penalty parameter and the penalized score test of Voorman, Shojaie, and Whitten to determine statistical significance of associations. They were Thirty participants currently drank alcohol 9. When whole transcripts were analyzed, MDMA altered use of words, compared to placebo. Participants also spoke significantly more about death see Figure 3 , although the scale of this increase was low. No gender differences were detected. Data from four participants were missing placebo condition due to recording failure. MDMA also affected isolated phrases specifically describing the target person. When we categorized phrases about the target person based on psychological, non-psychological, and relationship content, we saw that MDMA decreased proportion and absolute counts; not shown of phrases with psychological content and increased proportion of phrases with non-psychological factual content about the target person. Psychological statements decreased from Nonpsychological statements correspondingly increased from The proportion and absolute number of phrases describing the relationship of the speaker with the target person was not significantly changed. Across all phrases describing the target person, MDMA decreased use of words relating to the body 0. As fully described in Kirkpatrick et al. To explore how these related to speech changes, we first reduced the dimensionality of the significant LIWC categories using principal component analysis PCA. We retained three components from our PCA procedure, which each respectively explaining We then attempted to predict each component using lasso regression and peak self-report visual analog scores Table 2. All three components were significantly predicted by high, feel drug, elated, stimulated, loving, social, and friendly scores. Finally, Social was predicted by confident, insightful, dizzy, and want more drug scores. Given our a priori interest in social effects and the lack of interaction terms in lasso models, we further explored the relationships of feelings confidence and insight to the rotated speech components using random effects models. Our goal was to better understand if the relation between self-report confidence and insight and the Social component was truly unusual to that component. We accordingly fit models in which the self-report ratings were predicted by all three rotated speech components and their interactions, treating participant as a random effect. After lemmatizing and removing proper names, there were unique words in the corpus. Out-of-bag accuracy for different size models varied based on number of predictor variables words used in the model. We created a final model using words and repeats of 5-fold cross-validation with results averaged. This final model had an accuracy of 0. The words identified as important included social words others , public , camaraderie , outgoing and words with both positive goofy , beautiful , cheer , fix and negative emotional valence trouble , dead , Figure 2. We used two complementary techniques to investigate the effects of MDMA on the important social behavior of speech. Using a standardized dictionary approach we found that MDMA altered word choice in specific, validated categories. Using an exploratory data mining approach to look for changes relating to social and emotional functioning, we found that specific emotional words were useful for distinguishing speech on MDMA from speech on placebo. MDMA is thought to have prosocial effects that are unusual or even unique e. Our analysis of transcribed speech about emotionally important others showed evidence of these prosocial effects. Both the dictionary and machine learning analyses of the entire transcriptions indicated that MDMA increased use of social words. MDMA might produce these prosocial effects in part by increasing positive emotional reactions or by blunting anxiety Bedi et al,, ; Hysek et al. In this study, we indeed found many of the effects of MDMA on speech were associated with changes in self-report euphoria and sociability. When we predicted rotated principal components using self-report measures, components were predicted by a largely consistent set of self-report measures relating to euphoric and prosocial feelings. Interestingly, a social component — indicating increased use of words relating to social, sexual, and death— was also predicted by changes in self-reported confidence and feelings of insight, while other components were not. This suggests that the putatively unusual effects of MDMA on social-related speech content may not only involve euphoria-related sociability but also another cognitive phenomenon involving feelings of insight and certainty. Consistent with the findings of Marrone et al , MDMA did not increase talkativeness, as measured by the number of spoken words. Although MDMA is structurally similar to psychostimulants such as amphetamine and methamphetamine, it produces less psychomotor activation, including speech, compared to these drugs Marrone et al. MDMA also appears to differ from psychostimulants in that it can induce the feeling of cognitive impairments e. Whereas Marrone and colleagues studied the effect of MDMA on talking by asking them to recount the plot of a movie, our task involved speaking about a psychologically important target person. Under the influence of the drug, participants in our study described the target person using proportionally fewer phrases with psychological content and more with factual content. Although this initially appears inconsistent with the purported insight-producing effect of MDMA, a closer examination of the phrases describing target individuals suggests the effect may be due to a shift from stating general abstract opinions to mentioning more specific concrete details and episodes. Levels of linguistic abstraction are known to indicate levels of interpersonal closeness Reitsma-van Rooijen et al. Indeed, LIWC analysis of the descriptive phrases found significant increases in words relating to insights and cognitive mechanisms. Overall, these data suggest that MDMA does not only selectively blunt availability of negative emotional memories or enhance positive ones, but may also increase willingness or ability to consider emotional memories, at least in the presence of another person. This appears consistent with clinical observations e. This study has several limitations. We used a bag-of-words approach in which no attention is paid to word order or context within a document. This is computationally appealing, but unlikely to capture more than a small portion of the nuances of word usage. Further investigations should expand to include bigram and trigrams. Our talking task had participants discuss individuals who were psychologically important to them. This did not control for, and may have limited, the range of emotional memories that were recalled. Future studies could elicit memories with specific emotional content or use behavioral paradigms to create emotional experiences, as in the Trier social stress task Kirschbaum et al. These would allow further insights into the effects of MDMA on speech and emotional experience. In conclusion, we found that MDMA altered social aspects of speech during a brief semistructured dyadic interaction, using two different analytic methods. Combined with natural language processing, studying effects of psychoactive drugs on speech content can offer new insights into drug effects on mental states, as well as emotional and psychosocial interaction. The authors thank Jonathan Solamillo for excellent technical assistance, and Emmanuel Semmes for pharmacy support. As a library, NLM provides access to scientific literature. J Psychopharmacol. Published in final edited form as: J Psychopharmacol. Find articles by Matthew J Baggott. Find articles by Matthew G Kirkpatrick. Find articles by Gillinder Bedi. Find articles by Harriet de Wit. Issue date Jun. PMC Copyright notice. The publisher's version of this article is available at J Psychopharmacol. Open in a new tab. Predicting speech as rotated components from PCA using self-report drug effects as predictors. Financial Disclosures The authors report no biomedical financial interests or potential conflicts of interest. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel.

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