Two categories of SMS messages, spam and scam, received by a personal recipient in the Philippines. The data consists of raw text strings representing fraudulent communications common in the local telecommunications landscape. These messages reflect real-world unsolicited mobile traffic from Philippine network providers.
Use Cases
- Train a text classification model to detect fraudulent SMS using the message text
- Analyze the frequency of specific keywords like 'WIN' or 'JOB' within the Philippine spam context
- Build a regex-based filter to block suspicious sender patterns identified in the message text
- Identify common phone number formats used in Philippine SMS scams
Strengths
- Focuses on SMS messages received within the Philippine mobile network context
- Contains raw text data of unsolicited commercial and fraudulent messages
- Includes regional scam patterns such as fake job offers and lottery wins
- Captures linguistic variations and Tagalog-English (Taglish) code-switching common in local spam