AI-Based Content Recommendation & Scheduling (Multi-Platform & Trend Analysis)

1. Challenge Overview

This challenge focuses on developing an AI-driven content scheduling and recommendation system that helps marketers optimize their social media strategies. The system should analyze historical engagement data, audience behavior, and real-time social trends to recommend the best posting time, format, and platform for maximizing engagement.

Additionally, the model should leverage NLP for trend analysis, detect viral topics, hashtags, and user sentiment, and integrate A/B testing automation to refine posting strategies dynamically. Advanced implementations can include GPT-based caption generation and automated competitor benchmarking to track similar accounts’ performance.

2. Problem Statement

Develop an AI-powered content scheduler that:

• Predicts the best posting time, format, and platform based on historical engagement, audience segmentation, and trending topics.
• Incorporates NLP for trend analysis, identifying emerging viral hashtags, trending keywords, and sentiment analysis from multiple sources (Twitter, LinkedIn, Instagram).
• Implements A/B testing automation, where the AI dynamically adjusts posting parameters (e.g., time slots, content type, hashtags) and evaluates engagement performance over time.
• Stores engagement metrics and recommendations in a structured database (SQL or NoSQL).
• Bounty Points for:
  • Integrating GPT-based caption generation for social posts.
  • Automated competitor benchmarking, tracking engagement strategies of similar accounts.

3. Technical Approach

Participants must implement:

1. Data Collection & Preprocessing
   • Collect historical engagement data (likes, shares, comments, impressions) from LinkedIn, Twitter, and Instagram.
   • Use social media APIs or datasets from Kaggle/Twitter for data retrieval.
   • Optional: Participants can use their own datasets from personal/brand accounts.
2. Trend Analysis & NLP Integration
   • Implement NLP models (BERT, GPT, or TF-IDF) to extract trending hashtags, keywords, and sentiment.
   • Use external sources like Google Trends, Twitter Trends API, and Reddit discussions to detect emerging topics.
3. Content Recommendation Model
   • Develop an ML model (Random Forest, XGBoost, or Deep Learning) to analyze past engagement patterns and suggest optimal posting times/formats.
   • Use time-series forecasting (Prophet, ARIMA, LSTM) to predict engagement trends.
4. A/B Testing Automation
   • Implement automated A/B testing where the AI dynamically tweaks posting parameters and learns from real-time engagement performance.
   • The model should refine future recommendations based on A/B test outcomes.
5. Competitor Benchmarking (Bonus Feature)
   • Track competitor engagement trends using social media scraping techniques.
   • Compare hashtags, post frequency, and content formats used by competitors to optimize recommendations.
6. GPT-Based Caption Generation (Bonus Feature)
   • Implement GPT-based content suggestions that generate relevant captions based on trends and audience sentiment.

4. Data Sources

Participants may use the following data sources:

• Social Media APIs:
  • Twitter API (https://developer.twitter.com/en/docs)
  • LinkedIn API (https://docs.microsoft.com/en-us/linkedin/)
  • Instagram Graph API (https://developers.facebook.com/docs/instagram-api/)
• Pre-existing Datasets:
  • Twitter Engagement Dataset (https://www.kaggle.com/datasets/sbhatti/twitter-sentiment-analysis)
  • Google Trends API (https://pypi.org/project/pytrends/)
  • Reddit Trends Dataset (https://www.kaggle.com/datasets/reddit-top-posts)
• Custom Data Collection: Participants can use personal or brand social media account data as input.

5. Evaluation Criteria

Submissions will be assessed based on:

• Recommendation Accuracy (40%) – Ability to correctly predict optimal posting times, formats, and hashtags based on engagement trends.
• Trend Detection & NLP Performance (20%) – Quality of hashtag/topic extraction and sentiment analysis.
• A/B Testing Adaptability (20%) – Ability to adjust recommendations dynamically based on testing performance.
• Scalability & Competitor Benchmarking (10%) – How well the model integrates competitor tracking and trend comparisons.
• Code Quality & Documentation (10%) – Well-structured, maintainable, and well-documented code with clear setup instructions.

6. Deliverables

Participants must submit:

• Source Code & README – Clearly structured and well-documented code.
• Working Content Recommendation System – Functional AI scheduler with dashboards.
• Evaluation Report – Analysis of performance (engagement prediction, NLP accuracy, A/B test results).
• Demo Video (Optional) – Short video showcasing system functionalities.

7. Implementation Guidelines

• Train an ML model for engagement forecasting (Random Forest, XGBoost, or LSTM).
• Use NLP techniques for topic modeling and sentiment analysis.
• Store engagement data in a database (PostgreSQL, MongoDB).
• Build a user-friendly dashboard (React, Flask, or Streamlit) to visualize trends and recommendations.
• Ensure multi-platform support for LinkedIn, Twitter, and Instagram.
• Implement real-time A/B testing for continuous improvement.