How does Quora quickly mark questions as needing improvement?

Quora uses a combination of algorithms and community feedback to quickly identify questions that need improvement. Here's how this process generally works:

1. Community Feedback: Users can report or downvote questions they believe are poorly written or unclear. This feedback helps Quora's system understand which questions might need improvement.

2. Algorithmic Detection: Quora likely employs algorithms that analyze the content and structure of questions. These algorithms can detect characteristics such as vagueness, ambiguity, or lack of clarity, which are common reasons for marking a question as needing improvement.

3. Machine Learning Models: Quora may use machine learning models trained on data from user interactions and feedback to predict which questions are likely to require improvement. These models can learn patterns from historical data to make predictions about new questions.

4. Automated Checks: The platform might perform automated checks for certain criteria, such as:

   • Length and Completeness: Questions that are too short or lack necessary details might be flagged.
   • Clarity and Specificity: Questions that are ambiguous or overly broad could be marked for improvement.
   • Relevance to Existing Topics: Questions that don't fit well with existing topics or categories might be flagged.

While the exact mechanisms Quora uses to quickly mark questions as needing improvement are not detailed in the search results, it's clear that a combination of user feedback, algorithmic analysis, and machine learning plays a crucial role in this process.