Social Media Based Analysis of Opioid Epidemic Using Reddit

Abstract

Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns of opioid abuse at a national scale, understand opinions of the public, and provide insights to support prevention and treatment. Reddit is a community based social media with more reliable content curated by the community through voting. In this study, we collected and analyzed all opioid related discussions from January 2014 to October 2017, which contains 51,537 posts by 16,162 unique users. We analyzed the data to understand the psychological categories of the posts, and performed topic modeling to reveal the major topics of interest. We also characterized the extent of social support received from comments and scores by each post. Last, we analyzed statistically significant difference in the posts between anonymous and non-anonymous users.

Introduction

With dramatic increase of opioid overdose deaths¹, opioid crisis has been declared as a national public health emergency by President Trump on Oct 26, 2017. This has motivated data driven studies recently to explore the patterns and trends to understand opioid abuse. Example studies include disparity and spatial and temporal trend analysis using diagnoses on patient visits², clinical notes based studies to understand aberrant behavior introduced by opioid abuse using natural language processing³, and Electronic Health Records based analysis based on deep learning to classify the levels of opioid-dependence of patients⁴.

Due to potential social stigma, users using opioid may not be willing to openly discuss their concerns.⁵ This could lead to insufficient access to healthcare and negatively impact patients’ education and employment.⁶ Social media platforms allow increased self-disclosure for users to discuss otherwise sensitive topics⁷. Apart from self-disclosure, social media data provides unique opportunities for understanding users’ sentiments and opinions. A systematic review of 98 research studies on the benefits of social media identified following key overarching benefits: (1) increased interactions with others, (2) more available, shared, and tailored information, (3) increased accessibility and widening access to health information, (4) peer/social/emotional support, (5) public health surveillance, and (6) potential to influence health policy.⁸

Social media based analysis on opioid using Twitter has been conducted recently^9-12 Information obtained from Twitter is limited to the constraint on the number of characters, and can be highly noisy due to lack of feedback or monitoring. Forum data consists of people posting their experiences to invite discussions. For example, Reddit is a community based social media for discussion topics with content curated by the community through voting, which can generate more reliable information to provide insights on opioid epidemic. Forum data has been studied for mental health related discussions to identify the effects of anonymity on self-disclosure about stigmatic topics¹³. A qualitative study of 100 posts from an opioid addiction forum on Reddit revealed that the forum generated supportive content for users and that opioid users self-reported their struggles on the forum.¹⁴ Manual assessment of Yelp reviews for US hospitals mentioning opioid medication revealed that negative experiences of opioid use were more commonly described than positive experiences¹⁵.

Reddit has multiple categories on topics, and the “r/opiates subreddit” has seen an increased number of subscribers in recent months. Opioid abusers who have turned to r/opiates as a life-saving mechanism.¹⁶ The subreddit has seen increased activities after the 2016 US elections. The problem of opioid abuse is highly complex and requires solutions at multiple levels.¹⁷ Exploring the reddit forum data can provide insights into the psychology of opioid users. This may help to formulate better directed solutions for combating multiple problems arising from opioid abuse. Every post on Reddit receives feedback in the form of comments and score. This form of feedback can be used to characterize the extent of social support that a post receives¹¹. Users on Reddit are termed as redditors. A specific feature of reddit is the ‘throwaway’ accounts, which are used as proxies of anonymity. Anonymous accounts may be used by users to discuss topics that they feel sensitive about, and the contents generated can be more candid. Thus, the effectiveness of online forums in understanding drug-abuse can be better understood by studying reddit forum data.

In this paper, we aim to address the following questions through analyzing opioid related discussions in r/opioid subredit:

• Q1. What are the major psychological aspects in the content? We use Linguistic Inquiry and Word Count (LIWC) to perform psychological characterization.

• Q2. What are the major topics of discussions? We perform Latent Dirichlet Allocation (LDA) based topic modeling on the posts.

• Q3. What are the factors of the content that drive social support? We use negative binomial regression to identify the linguistic categories that are predictive of the social support.

• Q4. What is the difference between contents of posts by normal users and anonymous users (throwaway accounts)? We analyze statistically significant difference in the posts with Mann-Whitney U-test.

Methods

Data Collection

We collected posts on the category of “subreddit r/opiates” in Reddit from January of 2014 to October of 2017. Data was collected using PRAW (https://praw.readthedocs.io/en/latest/#) which is a Python wrapper for the official Reddit API (https://www.reddit.com/dev/api/). The dataset consists of a total of 51,537 posts by 16,162 unique users.

Analysis Methods

Semantic-based Analysis. We use LIWC2015¹⁸, a library to study emotional, cognitive, and structural components of speeches or texts. LIWC2015 contains a dictionary of various psychological categories built by multiple steps over several years. For a text given as input, LIWC can output the percentage of words belonging to each psychological category.

Topic Modeling. To discover the topics or themes discussed in the posts, we used the Latent Dirichlet Allocation (LDA)¹⁹ topic modeling implemented by the Gensim framework²⁰. To identify the optimal number of topics for our dataset, we use a heuristic approach based on minimizing the rate of change of perplexity²¹. In this method, we apply a 10-fold cross validation on the dataset, to determine the number of topics that minimize the rate of change of perplexity(RPC). The best estimate is to choose the first local minima of the curve. Figure 1 shows the RPC plot for our dataset. We choose K=20 as the optimal number of topics for our dataset.

Figure 1.

Plot of RPC against the number of topics

Statistical Testing

Negative Binomial Regression. We examine whether the attributes of the posts are predictive of the extent of social support that the posts receive. Comments and scores are two ways that posts receive feedback from users. The score of the posts is the difference in the number of upvotes and downvotes (https://www. reddit. com/wiki/faq). The number of comments and the score of a post are the response variables to measure the extent of social support. Table 1 lists the independent variables we used for computing the extent of social support. We use the negative binomial regression to predict the social support as both score and comments are count variables. The variables score (Variance/Mean = 29.33) and comments (Variance/Mean 18.64) are also both over-dispersed. Negative binomial regression is a model of choice for over-dispersed count variables.²²

Table 1.

List of semantic categories and post length were used as independent variables in the prediction task. A total of 58 variables were used.

Independent Variables
i	we	you	relativ	focusfuture	sexual
they	affect	affect	work	time	achieve
negemo	anx	anger	religion	money	focuspast
social	family	friend	netspeak	swear	motion
male	cogproc	insight	score	filler	leisure
discrep	tentat	certain	focuspresent	sad	death
percept	see	hear	space	female	assent
bio	body	health	home	cause	post length
ingest	drives	affiliation	informal	differ
power	reward	risk	nonflu	feel

Mann-Whitney U-Test. The Mann-Whitney U-Test is used to report the statistical significance of the difference between the linguistic attributes between the posts of anonymous and non-anonymous users.

Results

Data Exploration

The posts on r/opiates subreddit became more active after the 2016 US elections. Figure 2 shows that the average time between two consecutive posts has reduced, indicating that users are posting at an increased rate. The time at which users receive response on their posts has reduced. Figure 3 shows that the average response time of the comments on the posts has reduced. CDC identifies four different categories of opioids¹. Figure 4 shows the plot of the mentions of different opioids over time. We observe that illicit (illegally-made) opioid, heroin is mentioned the most in the posts. The mentions of heroin have increased rapidly after 2014-Q2. The deaths due to heroin overdose have an increasing trend in 2014-Q2 according to the CDC report¹. The second-most mentioned opioid is methadone which is a synthetic opioid. The mention of the semisynthetic opioid oxycodone has seen a slight increase.

Figure 2.

Average time elapsed between two consecutive posts

Figure 3.

Average time elapsed until first comment on a post

Figure 4.

Mentions of different opioid categories over time.

Major Psychological Categories

For a high-level understanding of the psychological aspects of the content of the posts, we choose the following major psychological categories from LIWC. The percentage of words observed from each psychological category are listed in Table 2.

Table 2.

Percentage of words and identified unigrams for major psychological categories

Psychological Category	% Words	Identified Unigrams from the top 100
Relativity	15.27	‘time’, ‘now’, ‘day’, ‘back’, ‘go’, ‘going’, ‘last’, ‘never’, ‘high’, ‘still’, ‘first’, ‘days’, ‘way’, ‘little’, ‘around’, ‘long’, ‘since’, ‘right’, ‘always’, ‘years’, ‘went’, ‘hours’, ‘ever’, ‘started’, ‘today’, ‘new’, ‘week’, ‘night’, ‘come’, ‘months’, ‘times’
Cognitive Processes	13.59	’know’, ‘really’, ‘feel’, ‘want’, ‘never’, ‘anyone’, ‘think’, ‘use’, ‘make’, ‘since’, ‘need’, ‘always’, ‘every’, ‘pretty’, ‘using’, ‘something’, ‘used’, ‘anything’, ‘ever’, ‘find’, ‘sure’, ‘trying’, ‘feeling’, ‘try’, ‘lot’, ‘maybe’, ‘thought’, ‘someone’, ‘made’, ‘else’
Social Words	7.19	’people’, ‘anyone’, ‘guys’, ‘said’, ‘say’, ‘help’, ‘love’, ‘give’, ‘someone’, ‘friend’
Drives	6.81	’get’, ‘got’, ‘take’, ‘good’, ‘high’, ‘first’, ‘getting’, ‘took’, ‘work’, ‘help’, ‘love’, ‘taking’, ‘trying’, ‘try’, ‘bad’, ‘friend’, ‘best’
Affect Words	5.20	’good’, ‘well’, ‘pretty’, ‘love’, ‘sure’, ‘pain’, ‘bad’, ‘best’
Biological Processes	3.56	’life’, ‘love’, ‘pain’, ‘drug’
Percept	2.60	’feel’, ‘see’, ‘said’, ‘say’, ‘pain’, ‘feeling’
Informal Speech	1.82	‘dope’

From the top 100 unigrams in our dataset, 81 are identified by LIWC dictionary spanning various psychological categories. We observe some sample posts for the identified unigrams to get a better understanding of the reddit posts.

Relativity. The posts are observed to contain a large use of temporal indicator words to describe their usage of opioids:

Taking about 5-7 Percocet 10mg’s for 7 days straight? What do you think?

I've been taking xanax for almost 10 years

Cognitive Processes. Words related to thought process of a user are commonly seen in large number of the posts:

Anyone know if etizolam will show up on a test?

So what do you guys think about otc codeine as an excuse for hot urine screens.

Social Words. We also find the use of words that are indicative of social interactions and relationships:

Can someone tell me about these? My friend gave me some for free. Can they be smoked?

I know a friend who said they would give me 2 for $25

Drives. Words from the drive category are indicative of drive in the emotions like achievements, power, reward, risk:

Is it just me or does eating 3 perc 10s make me so much higher than snorting a 30

I just got some oxy and I was wondering how much I should take for my first time with opiates?

Affect Words. The posts contain words that express the overall emotional state and feelings of an individual.

Tell me something good as I am in WD from my pain meds

I’m in rehab. Thanks for the concerns. Love you all.

We observe that posts in the dataset have a wide range of psychological categories. Figure 5 shows the different psychological categories over time. The cognitive process and relativity are the psychological categories that appear most frequently over the entire period of the dataset. Thus, we can interpret that users consistently post about their thoughts and also consistently talk about their usage of opioids by using temporal indicator words.

Figure 5.

Major psychological categories observed over time

Topic Modeling

For topic modeling, out of the 20 topics, we limit our discussion to 12 most coherent topics obtained. The word cloud of the major topics of interest related to opioid abuse

From all the topics we observe that there are 5 topics (T1, T6, T8, T9, T12) where users discuss about the different ways in which they abuse opioids. There are 2 topics (T2 and T11) where the discussions are about social effects of opioid addiction like affecting life, family and jobs. Users talk about the withdrawal symptoms when trying to quit opioids (T5). Other topics discussed are, concerns of drug tests (T4), experiences of getting charged for possession illegal opioids (T3), opioid prescriptions received from doctors (T7) and favorite activities to do when high (T10). Figure 7 (a) shows the 12 topics over time. It is observed that large number of posts consistently talk about opioid withdrawal symptoms (T5) and the use of opioids affecting family and life (T11). In Figure 7. we see that the most discussed topics are the social effects of opioid abuse followed by the different ways of abusing opioids. Opioid withdrawal symptoms are less discussed in comparison to the other two types of topics.

Figure 7.

Percentage of posts per topic type

Influence of Psychological Categories on Social Support

Using the independent variables listed in Table 1, we perform a prediction task using negative binomial regression to characterize the extent of social support received by the posts from scores and comments. The results of task are shown in Table 4 and Table 5.

Table 4.

Results of predicting the score of a post using negative binomial regression. Top 15 predictive variables in terms of their estimate are shown.

Predictor	Estimate	Std. Error	z value	Significance
(Intercept)	1.292	0.033	39.745	***
we	0.104	0.006	17.612	***
death	0.103	0.006	17.293	*
home	0.08	0.006	13.82	***
family	0.063	0.008	7.907	*
see	0.051	0.005	9.582	*
swear	0.049	0.007	7.181	*
relig	0.047	0.005	8.832	***
affect	0.045	0.014	3.293	***
reward	-0.038	0.004	-9.098	*
percept	-0.037	0.005	-7.993	***
negemo	-0.036	0.014	-2.601	***
friend	-0.035	0.006	-6.283	*
informal	-0.034	0.005	-7.128	***

Table 5.

Results of predicting the comment of a post using negative binomial regression. Top 15 predictive variables in terms of their estimate are shown.

Predictor	Estimate	Std. Error	z value	Significance
(Intercept)	2.766	0.021	132.311	***
we	0.062	0.004	15.807	*
home	0.039	0.004	10.064	*
affect	0.038	0.009	4.284	***
posemo	-0.033	0.009	-3.695	***
negemo	-0.028	0.009	-3.048	***
see	0.026	0.003	7.869	*
affiliation	-0.023	0.003	-7.146	*
percept	-0.023	0.003	-7.906	*
you	0.022	0.002	13.003	***
money	0.021	0.002	10.967	*
reward	-0.02	0.003	-7.559	*
sexual	0.019	0.004	4.62	*
social	0.018	0.001	16.668	*

Use of 1^st person pronoun(we) garners more scores and comments, indicating that users who discuss personal matters, tend to get more social support. Posts referring to death and family get more scores while posts talking about home get more scores and comments. This indicates that posts on personal issues like family, death and home receive more social support. Overall affect category leads to more score and comments. But more frequent use of words referring to positive and negative emotions leads to less score and comments. The use of 2^nd person pronoun(you) and social words leads to more comments, this may be due to interactivity introduced by the use of these words. The length of posts is not seen in the top predictors of the score or comments. Thus, we conjecture that the length of the content is not a significant predictor of social support.

Difference between Anonymous and Non-anonymous Posts

Users may use anonymous accounts to make posts without leaving a trail of their identity. Out of the total number of unique users in the dataset 3.76% of the users are anonymous and they account for a total of 2.02% of the total posts in the dataset. 60% of non-anonymous users have only a single post, whereas 81% of anonymous users have only a single post. This indicate that anonymous accounts are more used for one-time posting that may contain sensitive information.

Table 6 shows difference of common linguistic attributes in the posts between anonymous and non-anonymous users. We find that people choose to be anonymous in order to discuss negative emotions like anxiety(18.57% diff.) and sadness(2.84% diff.) Also, anonymous posts contain more words related to health(6.5%) and risk(5.04%). More frequent use of 1^st person pronoun (4.22%) indicates that anonymous accounts are preferred in order to discuss personal issues. We observe less use of 2^nd person pronoun (-27.45%) and social words (-7.85%), which indicates that they tend to be less interactive in their posts.

Table 6.

Differences in mean use of linguistic attributes between anonymous and non-anonymous users. Significance test is based on Mann-Whitney U-Tests. (*** = p<.0001; ** = p<0.001; *=p<0.01)

Category	Non-anonymous	Anonymous	% Difference	Significance
insight	2.83	2.64	7.32	***
focuspast	4.92	4.56	7.95	***
risk	0.54	0.51	5.04	***
anx	0.31	0.26	18.57	**
discrep	2.23	2.14	4	*
you	0.93	1.29	-27.45	*
informal	1.78	2.17	-17.8	*
i	7.6	7.3	4.22	*
social	6.24	6.77	-7.95	*
sad	0.41	0.4	2.84	*
health	1.68	1.58	6.5	*
differ	4.04	3.96	2.19	*
friend	0.52	0.51	2.21	*
cogproc	14.86	14.53	2.26	*
cause	1.9	1.89	0.3	*

Also, less use of informal words (-17.8%) is observed in the posts of anonymous users. This may be because anonymous accounts are used to discuss sensitive content, thus users may tend to be more explicit in their description.

We perform the Mann-Whitney U-Tests to compare the difference in social support for anonymous and non-anonymous users. The difference in the scores is found to be statistically significant (p=0.0031, z = -2.732) whereas the difference in the comments is not statistically significant (p=0.292, z = -0.546). Figure 8 shows a comparison between the scores and comments of anonymous and non-anonymous users. The mean number of comments on posts by anonymous users is 18.57 and by non-anonymous users is 18.77. Thus, anonymous and non- anonymous users both receive similar amount of support in terms of number of comments. But the anonymous users receive more support in terms of score. It indicates that redditors do not tend to comment more on the anonymous posts but they would show more support with greater up votes on the posts.

Figure 8.

Comparison of comments and score between anonymous and non-anonymous users

Results and Discussions

Relativity, cognitive processes and social words are the major psychological categories observed across the posts. The cognitive process and relativity are the psychological categories that appear most frequently over the entire period of the dataset. Thus, we can interpret that users consistently post about their thoughts and also consistently discuss their usage of opioids by using temporal indicator words.

Topic modeling on the posts reveals a range of topics including how opioids are administered, opioid use affecting life and withdrawal symptoms due to trying to quit opioids. Majority of the topics are on how to abuse opioids, followed by the social impact of opioid abuse. Opioid withdrawal is less discussed in comparison to topics on abusing opioids. This indicates that manipulating prescription drugs is a popular approach for opioid abuse, which has to be taken into consideration for prevention by hospitals and governments.

Use of 1st person pronoun(we), indicating discussion on personal matters, tends to get more social support both in terms of scores and comments. Posts referring to death, family and home garner more social support. For active surveillance report, posts having higher number of comments and scores are likely to contain discussions on sensitive topics like family, death, home and other personal matters indicated by use of 1st personal pronoun.

Significant differences are observed in the posts of users that choose to be anonymous. Anonymous posts contain greater use of words related to anxiety, sadness, risk and health. They also tend to be less interactive and prefer talking about their own concerns. For public health surveillance, content analysis of anonymous users can help understand sensitive topics related to opioid abuse.

A limitation of the official APIs of Reddit used for collecting data is that it does not provide access to the user demographics like location, age or gender, which could provide deep understanding on the demographic disparity for better public health studies.

Conclusion

Opioid epidemic has become a major national public health problem that demands better understanding, which is limited by the lack of large scale data. Social media, in particular, Reddit, which comes with community curated content, provides a candid medium which makes large scale data available to understand the topics, emotions, psychology and opinions of the public. Our initial study using Reddit demonstrates that information on Reddit provides a meaningful and useful resource to understand opioid epidemic from users’ discussions at large scale. This provides the first step for more in-depth studies to provide insights that may help to combat opioid crisis.

Figure 6.

Word clouds of the major topics of discussions related to opioid abuse

Table 3.

Major topics of discussion with example posts

Topic ID	Topic Description	Sample Posts
T1	Opioid abuse with needles	So my question is what happens to the dope and subs that I shoot, does it just pile up in my veins or is most of it extracted through my liver? When shooting up do you have to press the entire needle into the vein or could you just put the tip of the needle in there and then shoot?
T2	Opioid effect	I feel like morning shots get me more high than if I were to wait until later in the day. Anyone else feel the same way or am just a silly sally? Hi guys I cant feel my left hand after I woke up this morning 4 hours ago. Before I went to sleep I took 20mg methadone and a big line. Could it be from the drugs?
T3	Arrest from opioid abuse	I got arrested and charged with misdemeanor possession of drug paraphernalia. What kind of trouble am I looking at in court? Im cali. So I have this Narcan I got from a friend is It illegal for me to have it? If I got caught with it would I get a pos.of a controlled substance.
T4	Drug tests	Wondering because I know I would test positive for benzo, coc, opi, thc, oxy, amp, bar, mdma. Have to take a drug test Monday morning just did my last shot how likely will I test positive?
T5	Opioid withdrawal	Is it possible? Will the withdrawals be there? Im just looking for a way to quit without withdrawals What kratom doses have u guys used to get off heroin painlessly?
T6	Opioid abuse with solution	I have a syringe with saline in it. Could I mix crushed pills (hydromorphone) in the solution then inject it? Can i just crush mix with water filter and shoot xans? Does it even give a rush?
T7	Opioid prescription	I have back pain and I got a prescription for 10mg/325mg Hydrocodone pills. I go to the doctor about once every two months and get as much as possible, usually 4 oz. 6 or 8 if im lucky
T8	Opioid abuse with plugging	Just picked up a few Fentanyl patches 100/mgc h at $60 each. how long does it take ER morphine to kick in if administered by plugging?
T9	Opioid abuse with smoking	I think I’m going to try smoking my dope instead of snorting. What do you think? Any tips and tricks? Smoked about a quarter g throughout the course of the day.
T10	Activities while high	what do you do when you use? Music, movies, what type? Or do you just lay in the quiet? What is your favorite bands, genres, or specific songs you listen to while high?
T11	Social effect from opioid abuse	I’m tired of being broke, pushing friends and family away, and risking being arrested or dead. What full time jobs do you guys have? Are there any functional addicts who can maintain?
T12	Administering opioid tablets	I had a tooth removal 2 years back and still have some tabs of 5/325 Percocets. I was wondering if the tabs have expired yet and if they are still good to use. I have 14 tablets of 5mg oxycodone, how much would you take each day?