Shadowban a tweet/post with multiple trending hashtags about the-algorithm-ml HOT 1 OPEN

Steps for Implementing Shadowban Detection:

1. Define Non-Shadowban Criteria:

   • Establish clear guidelines for content that cannot be subject to shadowban. This ensures transparency and fairness in the system.

2. Train a Natural Language Processing (NLP) Model:

   • Utilize filtered data clusters and objective features, such as trending hashtags, to train an NLP model. This model learns patterns and characteristics associated with shadowban-worthy content.

3. Integration of Criteria and NLP Model:

   • Integrate the established non-shadowban criteria with the trained NLP model to create an intelligent system (referred to as sys_A). This system effectively segregates data into shadowban and non-shadowban categories.
   • sys_A is proficient in distinguishing between unbanned (pre-trained) and trending content.

Effects:

• The resulting sys_A enables the identification of shadowbanned content based on thorough analysis.
• This system's capabilities allow for the categorization of data into shadowbanned and non-shadowbanned segments, enhancing content moderation efforts.

Conclusion:

• By implementing a comprehensive approach involving clear criteria definition, advanced NLP modeling, and intelligent integration, the detection of shadowban within the system becomes feasible. This approach ensures fairness, transparency, and effectiveness in content moderation processes.

Credit:

• Credit for the conceptualization and suggestion of these steps goes to Vishesh Yadav (Email: [email protected]).

I hope this meets your requirements! If you need further revisions or have any additional requests, feel free to ask.

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