This paper is based on my final research during the class of Topic Modelling at UofT

Latent Emotional Dynamics in Psychedelic Microdosing Communities: A Neurosemantic Analysis of Affective Patterns Through LDA-Based Topic Modeling and Computational Psychometrics

Guneet Singh Chatha

Abstract This study evaluates the microdosing report topics discussed on the subreddit r/microdosing since it’s inception on 7th January 2014 to 29th October 2021 to analyse why people are using microdosing and what insights can be drawn from the users. Multiple models were tested such as LDA, LSA and NMF to identify topics discussed on the subreddit where NMF gave us the best results Keywords: topic models, microdosing,

1.Introduction This study analyses seven years of Posts and comments on the subreddit r/microdosing using LDA, LSA, NMF and Biterm to understand people’s opinion on the topic and more insights into what users feel on the microdosing experience. The results from the analysis show that

1. Depression and Anxiety are the most self-treated diseases that people try microdosing
2. 3. for Some common topics observed were dosage protocol, stamets stack and tolerance The common themes on the subreddit were around microdosing psychedelics for feeling something
3. NMF was the best approach for tackling short-text data 2.Background 2.1 Microdosing Microdosing psychedelics is the practice of consuming very low, sub-hallucinogenic doses of a psychedelic substance, such as lysergic acid diethylamide (LSD) or psilocybin-containing mushrooms. According to media reports, microdosing has grown in popularity, yet the scientific literature contains minimal research on this practice. There has been limited reporting on adverse events associated with microdosing, and the experiences of microdosers in community samples have not been categorized and till date only one research has looked into insights from reddit to derive outcomes from self-reported users. Multiple survey based researches in a controlled lab environment have been conducted indicating benefits for mental health and addiction. Typical doses can be small as one twentieth of a typical recreational dose, sometimes even less/ Psychedelics have typically been associated with marked alterations in cognition, affect, perception and neurophysiology. Individuals who take psychedelics describe profound changes in visual and auditory perception, accompanied by vivid imaginative experience and intense emotions. However this is not the case with microdosing as it involves a subthreshold dose. That is individuals aim to identify a dose at which they do not feel high and only gain the positive identifiable acute drug effects. Despite the reported lack of acute effects of microdosing, proponents claim a wide variety of psychological creativity, productivity, social ability, focus and well being. Microdosing is thus a curious phenomenon on the one had advocates deny experiencing the alterations in consciousness that characterise typical recreational doses, yet people claim significant psychological benefits from them. Many models using GTM and linear mixed-effects model have already been conducted in this domain but no study has looked at analysing the entire corpus of experiences as shared on the subreddit r/microdosing. 2.2 Subreddit r/microdosing Reddit is an American social news aggregation, web content rating, and discussion website. Registered members submit content to the site such as links, text posts, images, and videos, which are then voted up or down by other members. Posts are organized by subject into user- created boards called "communities" or "subreddits", which cover a variety of topics such as news, politics, religion, science, movies, video games, music, books, sports, fitness, cooking, pets, and image-sharing. Submissions with more upvotes appear towards the top of their subreddit and, if they receive enough upvotes, ultimately on the site's front page. Although there are strict rules prohibiting harassment, it still occurs, and Reddit administrators moderate the communities and close or restrict them on occasion. Moderation is also conducted by community-specific moderators, who are not considered Reddit employees. The subreddit r/microdosing has boomed in popularity in recent times with posts increasing by more than 200% post the covid lockdown as observed in early 2020. On the subreddit r/microdosing people share their experiences with microdosing and ask questions related to the domain. Our exploratory data analysis covers more features about the subreddit however the general theme by analysing the subreddit flairs show topics such as –
4. Reports of people on multiple substances
5. Questions and answers about the dosage and dosing protocol
6. Research Questions Research Question 1: Can topic modelling show us the range of emotions and feelings that people are dealing with on microdosing Research Question 2: What is the best model for analysing short text shared on reddit posts
7. Methods 4.1 Data Collection Data collection was performed by creating a script that captures all posts and comments on the subreddit. Reddit’s official API could not be used as it lacks a method for downloading archived posts and posts from a specific timeframe, only posts that are categorised using reddit’s new filters such as new, best and hot can be downloaded from the official API. The script created made use of the unofficial Pushshift API to download all posts and comments posted in the subreddit r/microdosing. 4.2 Data Cleaning All posts that did not contain English words were removed and excluded from this analysis. Empty and archived posts without any content were also removed since they shared images and memes associated with the topic. After all posts were stored into a csv file all un-necessary columns were removed and the following column headers were saved into the final post dataframe -
8. Post Title
9. Post self text
10. Post ID For the comment dataset only the comment ID and comment payload text was saved to the final comment dataset. 4.3 Data Pre-processing The dataset was then sorted by time of post /comment creation and concatenated into the comment trees. Comment trees were concatenated to the relevant posts to generate the final dataset. The final dataset was then moved through multiple iterations to remove stop words and lemmatized into the final text set through using parts of speech tagging.
11. Analysis and Results 5.1 Descriptive Statistics The final dataset included a total of 30,960 posts and 340,835 comments. However as all comment trees were merged into the original posts by concatenating with a whitespace the final dataset included a total of 30,960 posts. Multiple descriptive statistics tests with their visualisations have been shown in section 5.2 5.2 Exploratory Data Visualisations Making sense of the dataset Understanding our data through bigrams and trigrams gives us some level of clarity to the top most discussed words in the subreddit. Unigrams by itself don’t provide us with enough context to understand the dataset but looking at bigrams and trigrams we can see that people are talking about their feelings and questions related to dosage and protocol Figure 1.1 – The top 30 Unigrams in the dataset Figure 1.2 – The top 30 Bigrams in the dataset Figure 1.3 – The top 30 Trigrams in the dataset The sudden increase in posts can be observed after covid lockdowns in early 2020 resulting in a dip then a stark increase towards the end of 2020 contributing to the increasing questions regarded to first time users. Figure 1.4 – Time-series of posts
12. Models 5.1 LDA latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is an example of a topic model and belongs to the machine learning field and in a wider sense to the artificial intelligence field. LDA imagines a fixed set of topics. Each topic represents a set of words. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics. We want to maximise topic coherence and minimize the Jaccard similarity so the distance between the lines is the shortest. Figure 5.1.1 – Coherence and Jaccard for LDA As the dataset is sparse and contains short-text LDA is not the best fit for our model. However after multiple iterations the ideal number of topics was found out to be 5. The performance metrics as observed for our LDA model when number of topics was 5 were as follows - Coherence Jaccard Similarity 0.2167 0.5134 The top 10 words with their probabilities in each topic are – For topic 1 like 0.015 Dose 0.011 Feel 0.010 Make 0.008 Work 0.008 Good 0.008 Time 0.007 Know 0.007 Experience 0.006 Effect 0.006 For Topic 2 Dose Feel Like Time Make Help Really Good Well Effect 0.015 0.012 0.010 0.008 0.007 0.007 0.006 0.006 0.006 0.006 For Topic 3 Dose Like Time Feel Experience Trip Effect Good 0.011 0.011 0.010 0.010 0.007 0.006 0.006 0.006 Find 0.006 Work 0.006 For Topic 4 Dose 0.011 Feel 0.011 Like 0.010 Good 0.010 Make 0.007 Time 0.006 Work 0.006 Experience 0.006 Really 0.006 Help 0.006 For Topic 5 Dose 0.011 Like 0.009 Time 0.009 Help 0.008 Feel 0.008 Want 0.008 Know 0.007 Good 0.006 Also 0.006 Make 0.006 5.2 NMF Non-negative matrix factorization is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. NMF is useful when there are many attributes and the attributes are ambiguous or have weak predictability. By combining attributes, NMF can produce meaningful patterns, topics, or themes. Just like we calculated the shortest distance points between coherence and jaccard scores for LDA we tried multiple iterations to assess that the ideal number of topics was found to be 8 for NMF. Figure 5.2.1 – Coherence and Jaccard for NMF Just like we calculated the shortest distance points between coherence and Jaccard scores for LDA we tried multiple iterations to assess that the ideal number of topics was found to be 7 for NMF. The performance metrics as observed were – Coherence 0.1897 Jaccard Similarity 0.3212 From our 7 NMF topics the words with highest value are listed below -
13. Evaluation of Results Metric Jaccard Coherence LDA (5 topic) 0.2167 0.5134 NMF (7 topic) 0.1897 0.3212

• By referring to metrics alone we see that the 7 topic solution for NMF provides the best
• By referring to metrics alone we see that the 7 topic solution for NMF provides the best evaluation
• After interpreting clusters from both models it is clear that NMF has the greatest potential in creating topic clusters for our datasetpotential in creating topic clusters for our dataset

1. 2. 3. 4. 5. 6. Topic 1 : like, help, feel, think, thing, anxiety, make, life, good, really The first topics involves all the feelings associated with microdosing. Words like anxiety, feeling and thinking have to do with human cognition and consciousness. [… Focus, Creativity, no anxiety, and what depression ….] […myself do create a good day for myself and life feels…] [… I really feel good, can think clearly and want to let…] Topic 2 : water, tab, vodka, bottle, distilled, use, dissolve, solution, distil These topics contain insights into volumetric dosing of LSD using vodka or distilled water as a medium for diluting LSD doses. (chlorine degrades LSD structure) [… Volumetric dosing style Finland, our most known vodka.….] […Vodka or distilled water to make volumetric doses…] [… tab needs to dissolve into a solution with distilled …] Topic 3 : microdosing, question, advice, truffle, dosing This topic covers most of the questions and advice for people experimenting with microdosing. The most common flair associated with these topics was “newbie advice” [… Advice on using magic truffles for microdosing.….] […methadone as a short term detox and dosing advice…] [… Microdosing truffles (and microdosing shrooms) …] Topic 4 : day, week, md, tolerance, schedule, protocol The topics here are associated with the dosage schedule. Due to tolerance microdosing cannot be done everyday and people here are discussing the protocol for dosing […daily .2, makes sense to me you'd build tolerance.….] […What protocol would be best for a TBI patient that is…] [… everyone does it with a consistent week schedule…] Topic 5 : capsule, mushroom, dry, scale, cap, powder, grind This topic cluster looks into the proper method of consuming psilocybin using grinded mushroom powders into capsules […and turned everything into this powder before.….] […ground them up, and capsulated them, and how long did these capsule…] [… while making my capsules (with a very fine grind/powder…] Topic 6 : LSD, 1p, dmt, 1cp, drug, try These topics include all isomers of other drugs such as 1p and 1cp LSD and other psychedelic substances such as DMT […Microdose 4-AcO-DMT vs 1P-LSD.….] […the optimal 1cp LSD dosage using Fadiman protocol…] [… Then moved to 1cp and started trying w 10mcg little …]
2. Topic 7: lions, mane, niacin, stack,stamets, flush Paul stamets is a famous mycologist who suggested the stamets stack that also includes inhibitors such as niacin and lions mane for increasing cognition and neurogenesis. […times with Niacin I experienced the flush physically.….] […the stamets stack with the lions mane and niacin and I just wanted…] [… take lions mane every day. Niacin sometimes, not always.…]
3. Discussion Research Question 1: Can topic modelling show us the range of emotions and feelings that people are dealing with Our topic model clearly highlights anxiety and depression are the most common diseases that people are using microdosing for and our bigrams and trigrams reflect that outcome. However this cannot be taken into full consideration as there is no way to isolate microdosing reports from the subreddit. The subreddit has recently added flairs so it’s easy to tag a post as a report. Since most of the people on this subreddit use it as a platform to ask questions and feedback our dataset contains a lot of noise. Research Question 2: What is the best model for analysing short text shared on reddit posts For short-text data that is high in count NMF provides us with the best approach as our LDA model topics were not easily comprehended.
4. Limitations
5. 2. 3. The ideal approach for reddit data would involve running hierarchical topic models. As we are merging comments and subcomments into one master post file the topics might get divided into subtopics and a clear hierarchy is needed as sometimes discussions sway away from the real topic. Biterm library was also attempted however extremely long run times were observed from it. A better way to tackle the research question would have been understanding the topics over time and study them as they changed pre and post covid.
6. Conclusion Topic models provide us with a great overview for understanding both structured and unstructured text. For our analysis NMF yielded better results than LDA due to the text density. We cannot completely understand all the emotions associated with microdosing until we can isolate the noise and focus only on microdosing reports.