As a data scientist at Facebook working on improving the Marketplace platform and identifying bad sellers, I would consider a range of data and metrics to create an effective model. The goal is to ensure a safe and trustworthy environment for users while minimizing fraudulent or malicious activities. Here are key data points and metrics I would consider:
1. User Ratings and Reviews: Analyzing user feedback and ratings can provide insights into a seller's reputation. Frequent negative reviews or suspicious patterns of positive reviews might indicate a bad seller.
2. Transaction History: Reviewing a seller's past transactions can reveal any unusual or questionable behavior, such as frequent disputes or cancellations.
3. Response Time: Monitoring a seller's responsiveness to inquiries and orders can reflect their commitment to customer service.
4. Listing Behavior: Detecting sudden spikes in listings, frequent relisting of the same items, or listing of prohibited items could be indicative of a bad seller.
5. Profile Completeness: Incomplete or inconsistent seller profiles may suggest fraudulent intentions.
6. Geolocation Data: Analyzing the physical location of sellers and cross-referencing with transaction data can help identify sellers engaging in suspicious activities.
7. Communication Patterns: Identifying sellers who encourage transactions outside the platform or request personal information can help uncover potentially fraudulent behavior.
8. Account Age and Activity: Assessing the age of the seller's account and their level of activity can reveal new or dormant accounts used for fraudulent purposes.
9. Payment Method Diversity: Monitoring the use of multiple payment methods or a consistent preference for a specific method can indicate different motivations behind the transactions.
10. Listing Descriptions and Images: Analyzing the quality and consistency of listing descriptions and images can help detect fake or misleading listings.
11. Flagged Content: Utilizing reports from users who have flagged suspicious activity or items can provide valuable input for identifying bad sellers.
12. Social Graph Analysis: Exploring connections between sellers and potential collusion with other users can help uncover coordinated fraudulent activities.
13. Device and IP Tracking: Tracking the devices and IP addresses used by sellers can help identify patterns of suspicious behavior, such as operating multiple accounts.
14. Cross-Platform Behavior: Integrating data from other Facebook services can provide a holistic view of a seller's behavior across various platforms.
15. Machine Learning Insights: Leveraging machine learning techniques, such as anomaly detection, clustering, and predictive modeling, can help identify patterns of bad behavior that might not be immediately obvious.
In addition to collecting these data points, I would ensure strict privacy and data protection measures to comply with regulations and maintain user trust. The collected data would be used to develop and train machine learning models aimed at identifying bad sellers, reducing their impact on the platform, and ultimately improving the Marketplace experience for all users. Regular model updates and iterations would be crucial to stay ahead of evolving fraudulent tactics.