Program Structure

Week 1: Introduction to Social Media Data

• Overview of social media platforms and the types of data they generate.
• Introduction to data collection methods from social media APIs (e.g., Twitter, Facebook, Instagram).
• Tools for scraping social media data: Tweepy, Scrapy, and others.
• Hands-on exercise: Collecting real-time data from Twitter using the Tweepy API.

Week 2: Data Preprocessing and Feature Engineering

• Data cleaning techniques: Removing noise, handling missing data, and normalization.
• Feature engineering: Extracting relevant features such as hashtags, mentions, likes, and shares.
• Text preprocessing for social media posts: Tokenization, lemmatization, stop-word removal, and text vectorization (TF-IDF, Word2Vec).
• Hands-on exercise: Preprocessing text data from social media posts for sentiment analysis.

Week 3: Sentiment Analysis and Opinion Mining

• Introduction to sentiment analysis: Positive, negative, and neutral sentiments.
• Techniques for sentiment analysis: Naive Bayes, SVM, and deep learning approaches (LSTM, BERT).
• Opinion mining: Extracting opinions and subjective information from social media posts.
• Hands-on exercise: Performing sentiment analysis on Twitter data using machine learning models.

Week 4: Trend Prediction and Topic Modeling

• Trend prediction using AI models: Time series forecasting and regression techniques.
• Topic modeling: Latent Dirichlet Allocation (LDA) and other unsupervised learning methods to discover topics in social media data.
• Hashtag analysis for trend prediction: Understanding how hashtags can help identify viral topics.
• Hands-on exercise: Using LDA for topic modeling on social media data.

Week 5: Social Media Behavior Analysis

• Behavioral analysis using AI: Analyzing user interactions and engagement patterns on social media.
• Network analysis: Social network analysis to understand user influence and relationships.
• Identifying influencers and key opinion leaders (KOLs) in social media networks.
• Hands-on exercise: Building a social network graph using user interactions and finding influencers.

Week 6: Ethical Considerations and Privacy Concerns

• Understanding the ethical implications of analyzing social media data.
• Privacy concerns and data protection laws (GDPR, CCPA) in the context of social media analysis.
• Responsible AI use: Ensuring fairness, transparency, and accountability in social media analysis.
• Hands-on exercise: Conducting an ethical review of a social media data analysis project.

Week 7: AI Tools and Platforms for Social Media Analysis

• Exploring AI tools for social media analysis: Google Cloud AI, IBM Watson, Hootsuite, and others.
• Using pre-trained models for text classification, sentiment analysis, and user behavior prediction.
• Hands-on exercise: Using Google Cloud Natural Language API for sentiment analysis and entity recognition on social media data.

Week 8: Final Project

• Design and implement a social media analysis project using AI techniques.
• Project focus: Analyze and predict trends, sentiment, or user behavior for a specific brand or topic.
• Example projects: Analyzing public sentiment towards a product launch or predicting the viral spread of a hashtag.