Text mining social media to explore concepts and sentiments associated with mental health topics or issues
Method Stream: Text Mining
ECs: Only 14 EC (standard, no new/own data collection. Applicable in case of a clinical internship)
Description:
The ubiquity of social media potentially provides rich opportunities for research to explore real-world opinions and attitudes towards various topics. Topics related to mental health are frequently discussed on social media such as Twitter (X) and Reddit. In this project you will apply text-mining analysis (e.g., topic modeling and sentiment analysis) to explore concepts and sentiments associated with specific (e.g., positive) mental health topics or issues.
You are free in selecting a specific topic of your interest related to mental health. Although Twitter has recently stopped the possibilities for automatically scraping Tweets, we have several previously scraped datasets available, with tweets mentioning for instance narcissism, neuroticism , #mentalhealth, #selfcompassion, and pro-anorexia hashtags,
The main focus of the project is to explore if and what we can learn from using big-data text mining techniques of Twitter data about salient topics and people’s sentiments associated with mental health (issues). If relevant, subsequent analyses could focus for instance on changes in sentiment over time, differences in sentiments between topics, or associations with number of likes or retweets.
Text-mining analyses can be done with the free (point-and-click) Orange Data Mining software, or with R or Python if you want to (learn to) do some more advanced coding.
Who are we looking for?
Students with interest in the topic and who are willing to learn basic text mining analysis.