The TikTok data team is charged with creating a machine learning model to differentiate between claims and opinions in user-generated video content. This classification is essential for accurate content management and maintaining the platform's integrity.
The data team has initiated the organization of the raw dataset for an in-depth exploratory data analysis (EDA). This process includes a preliminary investigation to pinpoint key variables that could affect classification accuracy and understand the distribution of video content types.
These initial analyses will inform the creation of a more precise and efficient classification model. By identifying significant variables like video duration and view counts, the team can customize the model to predict content types effectively.
The primary variable selected for initial focus is claim_status, which labels videos as either claims or opinions. This variable is crucial for guiding subsequent analysis and modeling processes. Maintaining an equal balance of claims and opinions in the dataset ensures fair representation in the machine learning model's training set.
Engagement metrics such as view counts have been studied to determine their correlation with content types. The results are:
These figures show a significant difference in engagement between claims and opinions, indicating that claims typically attract more views. This could suggest higher user interest or controversy.
The upcoming steps include further exploratory data analysis using these insights, refining data preparation, and initiating model training. This thorough approach ensures the creation of a robust model capable of effectively categorizing video content on TikTok, enhancing content management and user experience on the platform.