As a data scientist at TikTok, my role would involve ensuring the app's functionality, user experience, and overall performance. Detecting and addressing weird behaviors within the app is crucial to maintain a positive user experience and platform integrity. Here are some data metrics and considerations that could help identify unusual or wired behaviors in the TikTok app:
1. Engagement Drop or Spike: Monitor sudden drops or spikes in user engagement metrics, such as likes, comments, shares, and video views. Drastic changes could indicate bots or other fraudulent activities.
2. Unusual Device Activity: Track the number of devices used by a single user. A high number of devices linked to one account may suggest fake accounts or automated activity.
3. Abnormal Account Creation Patterns: Look for patterns in account creation, such as a sudden influx of new accounts from the same IP address or similar profile information, which might indicate bot-driven account creation.
4. Suspicious IP Addresses: Monitor IP addresses for unusual patterns, such as multiple accounts using the same IP or a high number of accounts originating from known spam or proxy IPs.
5. Rapid Following/Unfollowing: Keep an eye on users who follow and unfollow accounts at an unusually rapid pace. This behavior is common among bots.
6. Duplicate Content Detection: Implement content similarity algorithms to identify accounts reposting the same videos or content, which could be indicative of automated activity.
7. Comment and Like Patterns: Analyze comment and like patterns to detect repetitive or irrelevant comments and suspiciously high likes on certain posts.
8. User Session Duration: Track the average user session duration. A very short duration could suggest users are not finding engaging content, while an unusually long duration might indicate automated engagement.
9. Geolocation Anomalies: Check for users who claim to be in multiple geolocations in a short span of time, which could point to fake accounts or location spoofing.
10. Unusual Device Characteristics: Monitor device-related data such as device models, operating systems, and app versions to detect patterns of automated activity using specific device characteristics.
11. Spam Reports and User Complaints: Pay attention to user-reported spam accounts or content. High numbers of spam reports could indicate abnormal behavior.
12. Unusual Posting Frequency: Analyze the posting frequency of accounts. Rapid and consistent posting without engagement might be a sign of automated posting.
13. High Follow-to-Follower Ratio: Watch for accounts with a very high follow-to-follower ratio, as this might indicate a focus on mass following to gain followers quickly.
14. Unusually High Video View Counts: Examine videos with abnormally high view counts compared to the user's followers and engagement. This could indicate artificial view inflation.
15. Cross-Platform Behavior: Analyze user behavior across different platforms and social networks. Consistent patterns of strange behavior across platforms might indicate coordinated activity.
To detect these weird behaviors, you would need a combination of real-time monitoring, machine learning algorithms, data analysis, and user feedback analysis. Implementing a robust AI-powered system that learns from historical data and flags anomalies for review by human moderators is crucial. Regularly updating detection algorithms based on new behavior patterns will also be necessary to stay ahead of evolving fraudulent tactics.