Analyzing the Shadows: Machine Learning Approaches for Depression Detection on Twitter
Keywords:
Depression, Machine Learning, Major Depressive Disorder, Sentiment Analysis, SVM, Random ForestAbstract
Depression is a leading cause of disability worldwide, affecting approximately 4.4% of the global population. It can escalate from mild symptoms to severe outcomes, including suicide, if not treated early. Thus, developing systematic techniques for automatic detection is crucial. Social media platforms like Facebook, Twitter, TikTok, Snapchat, and Instagram provide users with the means to share personal feelings and daily activities, offering valuable insights into their thoughts and behaviors. This research aims to identify users who publicly disclosed their diagnosis and collect their data from Twitter. We created three different datasets, each varying in the number of tweets stored based on criteria discussed later. We selected six classifiers for analysis: Support Vector Machine (SVM), Logistic Regression, Random Forest, Max Vote Ensemble, Bagging, and Boosting. We conducted two analyses. In the first, textual data was converted into embeddings using the Bag of Words approach before analysis. In the second, a multivariate analysis, we trained algorithms on multi-dimensional data. Our findings revealed that Logistic Regression outperformed other techniques on smaller datasets. However, the Boosting algorithm yielded the best results on a dataset of 3,200 tweets, and the Bagging algorithm excelled when trained on 3,200 tweets of multivariate data. Overall, nearly all algorithms performed well on the 3,200-tweet datasets.
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