18,464 Filipino tweets categorized into hate speech and non-hate speech classes. The collection includes 10,000 training samples, 4,232 validation samples, and 4,232 testing samples specifically from the 2016 Philippine Presidential Elections.
Use Cases
- Train a binary text classifier to detect hate speech in Filipino social media posts
- Evaluate model generalization on low-resource languages using the 4,232 validation and 4,232 testing samples
- Analyze political discourse patterns using the election-specific tweet content
Strengths
- 18,464 total labeled tweets partitioned into training, validation, and test sets
- Binary labels identifying content as either hate speech or non-hate speech
- Contextual focus on the 2016 Philippine Presidential Elections