Depression is the leading cause of disability worldwide. Almost 75% of people with mental disorders remain untreated in developing countries with almost 1 million people taking their lives each year. In addition, according to the World Health Organization (WHO), 1 in 13 globally suffers from anxiety.

Around 3 million Filipinos live with depression. Arguing that social media is an alternative platform documenting daily life and thought patterns, In this project, we will explore tweets of depressed users to identify changes in social media language and behavior that might signal the onset of a depressive episode. The purpose of this project to create a machine learning model for early detection of depression that is contextualized in the Philippines.

The dataset was scraped from Twitter using the Twint library. The tweets only includes users near Manila, Philippines and English tweets(since there isn't much available tools for Tagalog NLP as of the moment). The dataset contains about 114,728 tweets which roughly 100 self-identify as having been diagnosed with depression (users who publicly tweeted “I was diagnosed with depression” or similar), with manual validation of tweets to check sarcasm/jokes and removed automated tweets.