For those of us who straddle between research and practice, it may not be enough to keep abreast of the scientific literature. It’s important, of course–PubTrawlr was founded on that assumption. And yet, there is so much more discourse out that that can help practitioners understand the on-the-ground conditions. And by understanding these, maybe we can design and implement better solutions.
So this month, we’re pulling together four sources: academia, Twitter, Reddit, and the mainstream news. We’ll start with the journals.
Different month, same search process. We pulled all the articles over the last 31 days from Suicide, Suicide & life-threatening behavior, Archives of suicide research, Suicidology online, and Suicidologi, then supplemented this with a global search for any recent mention of the term “suicid*. The end result was 342 articles across 185 unique journals.
Words and Phrases
This set of figures uses a bag-of-words approach, which counts the frequency of single terms or word strings. What shows up in these visuals is consistent with past months, with is probably a function of pulling from the same journals. We see mental health, research methods, and COVID.
I always like to screen for systematic reviews, so here’s a downloadable excel file that lists them.
After using an LDA topic clustering process, I assigned each of the 342 articles to the topic it “best” fits with. This clustered yield some really interesting categories. Take the top four topics. We can see articles on COVID-19, caregiver attitudes, social media, and sexual minorites.
I then visualized the relationship between these topics. The green paths represent positive correlations, while the red paths represent negative correlations. The thickness of the line represents the strength of the relationship. To pick the most obvious relationship, we can see that the anxiety topic had a strong, negative correlation with the caregiver topic. And what’s not on this graph? The COVID-19 topic, which didn’t rise to the top of having a strong relationship with any other topics.
So, the published literature is only part of the story. Let’s look at some more timely discourse. I pulled all the tweets over the last week with the hashtag suicide. This is a limit of Twitter’s API. To get more tweets, I’d have to jump up an account level and shell out some more $$$$, and I’m not quite ready to do that yet. But maybe soon???
The most frequent terms and phrases were about suicide prevention month, which is good! The weird word string in the phrase part of the graph below likely refers to a specific URL.
Tweet Sentimet Analysis
I did a brief sentiment analysis using the NRC lexicon to see what emotions are coming up the most in #suicide tweets. For first-time readers, sentiment analysis looks for the emotions that specific words are conveying. Nothing too surprising in the graph below. Most tweets deal with fear and sadness.
And, using the same LDA process as above, I clustered the tweets. A lot of the topics dealt with awareness and suicide prevention. Interesting, the most frequent topic deals with people sharing their stories. I have a long digression as a caption to this graph.
And our Top Tweets.
The most favorited and most retweeted tweets during the past week are embedded below.
I also attached a table with the top ten tweets by favoriting.
On to Reddit. Someone on the APA Suicidology listserve brought my attention to the r/SuicideWatch Subreddit. This is a forum dedicated to “Peer support for anyone struggling with suicidal thoughts.” The purpose of analyzing this set of data is not to be voyeuristic, but to understand messages people in crisis might exhibit. To look at this, I pulled the most recent 20 threads with comments.
The text analysis below shows a few things that don’t appear in other sources, such as strong language, the situational information (“birthday”), and other coping strategies.
The topic modeling isn’t as useful here, but I did do a more specific sentiment analysis. This also isn’t as helpful since a lot of the words are double coded, and this type of technique (at least how I implemented it), can’t really pick up irony or sarcasm.
Finally, I look at some recent news articles. The methods I used interfaces with google news, and isn’t as comprehensive as it could be, but gives us a high-level picture. What was especially interesting was the spike in news stories over the past week, even though these articles were evenly distributed among publishers.
At first glance, there is not much that distinguishes this word cloud from the others.
However, the topic modeling is extremely informative. We can clearly distinguish the most frequent topics: sexual minorities, suicide awareness.
To Sum Up
Hopefully, this gives you a clear picture of the different messages around suicide being communicated over the past month. If you have any suggestions for additional sources or other feedback, let us know in the comments!
Visit us at APHA!
Hey, we’ll be at the upcoming American Public Health Association conference in Denver, fully masked and ready to talk synthesis. If you’re there, swing on by for swag, snacks, and chatter about how we leverage AI to make science more accessible.