Silver Medal in Kaggle Competition – Toxic Content Classification


GOAL

RESULT

DURATION

Compete in and excel at a Kaggle competition focused on developing a model capable of classifying toxic online content from Quora, enhancing digital safety and community engagements.










Achieved a silver medal, placing high among global competitors. The model developed significantly improved the ability to filter and classify toxic content, contributing to safer online environments.











The project, including prep and competition phases, lasted for 3 days. The pressured, competitive environment and last minute registration fostered rapid development and refinement of the classification model.











WHAT CHARACTERISTICS ARE SPECIFIC TO TOXIC CONTENT?


To detect toxic content, the model focused on:


  • Usage of explicit language
  • Patterns of aggressive tone and negative sentiments
  • Contextual understanding of slurs and demeaning language
  • Specific words that imply inauthenticity
  • Frequency of ethnicities


Identifying these characteristics enabled the model to accurately classify and filter toxic content.



WHAT IS THE COMMUNITY HEALTH VALUE OF FILTERING TOXIC CONTENT?


By quantifying the improvement in community interactions and user engagement metrics post-implementation, the value of maintaining a healthier online environment was clear. This not only improved user experience but also fostered a safer and more inclusive digital community.


HOW CAN WE PREDICT IF CONTENT IS TOXIC?


The model utilized a blend of machine learning techniques including Convolution Neural Networks and Attention, along with word embeddings. By training the model on a vast dataset of labeled content, the system could efficiently predict and flag potential toxic interactions, thereby maintaining community standards.