UTILIZING BIG DATA FOR SOCIAL MEDIA TREND FORECASTING AND INFLUENCE ANALYSIS

Social media has become a vital tool for shaping public opinion, political debate, and consumer choice- making trend predictions and influencer analysis have never been more critical for businesses, policymakers and researchers. This paper introduces a new methodology of combining sentiment analysis, machine learning and network analysis to predict social media trends and identify cultural influencers. We acquire information from Twitter, Instagram, and Facebook using sophisticated NLP and machine learning models such as LSTM for Sentiment Classification, and ARIMA for Trend Prediction. A mixture of sentiment, user engagement and network centrality for the influential user is introduced. We have presented the results, showing the superiority of our hybrid approach against the traditional ones, with the MAE of trend forecasting for 0.145 and the influencer recommendation F1-score of 86.2%. The results demonstrate the capability of integrating sentiment analysis and influencer detection to enhance trend prediction and influence analysis. The methodology and its implementation provide useful tools for Real-time Social Media Analysis. They have applications in marketing, politics and social science by giving stakeholders control over social media dynamics.