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College students’ mental health concerns are a persistent issue; psychological distress in the form of depression, anxiety and other mental health challenges among college students is a growing health concern. However, very few university students actually seek help related to mental illness. This arises due to various barriers like limited knowledge about available psychiatric services and social stigma. Further, there is dearth of accurate, continuous and multi-campus data on mental well-being which presents significant challenges to intervention and mitigation strategies in college campuses.

Recent advances in HCI and social computing show that content shared on social media can enable accurate inference, tracking and understanding of the mental health concerns of users. There has also been work showing that college students appropriate social media for self-disclosure, support seeking and social connectedness. These facts, coupled with the pervasiveness of social media among college students, motivated us to examine the potential of social media as a “measure” for quantifying the mental well-being in a college population. Specifically, we focused on the following research goals:

  • Building and validating a machine learning model to identify mental health expressions of students in online communities
  • Analysing the lingusitic and temporal characteristics of the identified mental health content
  • Developing an index for the collective mental well-being in a campus, and examining it’s relationship with university attributes like academic prestige, enrollment size and student body demographics

We obtained a list of 150 ranked major universities in the US by crawling the US News website. We also obtained university metadata like gender distribution, tuition/fee during this crawl. Next, we crawled the Wikipedia pages for these 150 universities for extracting the student enrollment, type of university (public/private) and the setting (city/urban/suburban/rural) at each institute. Lastly, we obtained information on the racial diversity at each university from an article on Priceonomics. We study these universities in our work and use the metadata in our analysis.

Geographical distribution of the 150 ranked universities we studied

For social media data, we focus on Reddit. Reddit is known to be a widely used online forum and social media sites among the college student demographic. It’s forum structure allows creation of public online communities (known as “subreddits”), including many dedicated to specific college campuses. This allowed us to collect a large sample of posts shared by university students in one place. Although Facebook is likely more popular/widespread among students, it is challenging to use Facebook in such studies since the content shared is largely private, making it challenging to obtain such large data from it. Further, the semi-anonymous nature of Reddit enables candid self-disclosure around stigmatized topics like mental health.

After a manual search for subreddits for each university, we were able to identify public subreddit pages for 146 of the 150 universities. Next, we focused on correcting the “under-adoption” bias in subreddits. Subreddits which had a small fraction of Reddit users (as compared to university enrollment) were filtered out due to being under-representated. This left us with 109 universities with adequate Reddit representation. We leveraged the data on Google BigQuery (combined with some additional data collection) to get all posts ranging from June 2011 to February 2016. The final dataset used for our analysis included 446,897 posts from 152,834 unique users.

Since Reddit data does not contain any gold standard information on whether a post in a university subreddit is a mental health expression, our first goal was to use an inductive transfer learning approach to build a model to identify such content in a university subreddit. First, we include (as ground truth data) Reddit posts made on various mental health support communities. Prior work has established that, in these communities, individuals self-disclose a variety of mental health challenges explicitly. We use these posts as the “positive” posts and, parallelly, we utilize another set of Reddit posts, made on generic subreddits unrelated to mental health, as “negative” posts. We obtain 21,734 posts for each category, which we use as the positive and negative class for building a classifier. We observed a validation accuracy of 93% and an accuracy of 97% on a test set of 500 unseen, expert-annotated posts from our university subreddit data. We then proceeded to use this classifier for labelling the 446,397 other posts across the 109 university subreddits. Our classifier identified 13,914 posts (3.1%) to be mental health expressions, whereas the rest of the 432,483 posts were marked not about the topic. This corresponded to 9010 unique users out of a total of 152,834.

Next, we looked at the linguistic characteristics of the posts identified to be mental health expressions by conducting a qualitative examination of the top n-grams uniquely occuring in these posts. We found that students appropriate the Reddit communities to converse on a number of college, academic, relationship, and personal life challenges that relate to their mental well-being (“go into debt”, “doing poorly in”, “only one homework”, “up late”, “the jobs i”). The n-grams also indicated that certain posts contained explicit mentions of mental health challenges (“psychiatric”, “depression”, “killing myself”, “suicidal thoughts”), as well as the difficulties students face in their lives due to these experiences (“life isnt”, “issues with depression”, “was doing great”, “ruin”, “cheated”). Some of the top n-grams were also used in the context of seeking support (“need help”, “i really need”, “could help me”).

For the temporal analysis of mental health content, we first study the proportion of posts with mental health expression across the years. The figure below shows the content per year (along with a least squares line fit). We observed that the proportion of posts with mental health expressions has been on the rise — there is a 16% increase in 2015, compared to that in 2011.

We then looked at how this trend varies over the course of an academic year. The plots below show the trend separately for universities following the semester system and the quarter system. Between August and April, for the universities in the semester system, we observed an 18.5% increase in mental health expression; this percentage was much higher: 78% for those in the quarter system, when compared between September and May. On the other hand, we observed a reverse trend in mental health content during summer months, for both semester and quarter system universities.

Trend in the semester months

Trend in the quarter months

Trend in the summer months for quarter system universities

 

 

Trend in the summer months for semester system universities

 

 

 

Lastly, as a part of our third research goal, we formulated an index we refer to as the Mental Well-Being Index (MWI), as a measure of the collective mental well-being in a university subreddit, based on the posts labelled as mental health related by the classifier. We then computed the MWI metric for all 109 subreddits and examined it’s relationship with the university attributes.

 

 

 

 

 

By visualising these relationships (as above), we gleaned several interesting observations. We found:

  • Universities with larger student bodies (enrollment) as well as greater proportion of undergraduates in their student bodies tend to be associated with lower MWI
  • MWI of the 66 public universities we consider, is lower, relative to that in the 43 private universities, by 332%
  • MWI is lower in the 7 rural and 33 suburban universities by 40-266% compared to others, while it is the highest in the 31 universities categorized to be in cities (by 29-77%)
  • Universities with higher academic prestige (or low absolute value rank) and higher tuition tend to be associated with higher MWI
  • MWI tends to be lower in universities with more females (or sex ratio, male to female <= 1) by 850%

Further, although our data shows a marginally lower MWI in universities with greater racial diversity, we did not find statistical significance to support this claim.

Our work here (the complete paper accepted at CHI 2017) further details our analysis in depth. Below is an infographic for our work.

 

 

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