In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. Based on a reader's anxiety profile collected by the network, a personalized dataset can be established to better understand the type of news that impacts a reader's mental health in a negative way. This study can benefit two groups. The first group is the mental health community who can use our approach to understand the impact of news content on those combating anxiety disorders and depression. The second group is for readers of news content in general who might not be aware of the type of news topics they are sensitive to. The insight from the Semantic Network should provide more information about specific triggers of anxiety that were previously unknown.
Cheun, Jacquelyn PhD; Dajani, Luay; and Thomas, Quentin B.
"Personalized Detection of Anxiety Provoking News Events using Semantic Network Analysis,"
SMU Data Science Review: Vol. 2:
3, Article 5.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss3/5
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Applied Behavior Analysis Commons, Applied Statistics Commons, Cognitive Behavioral Therapy Commons, Numerical Analysis and Computation Commons, Numerical Analysis and Scientific Computing Commons, Psychiatric and Mental Health Commons, Statistical Models Commons