Social media comprises of platforms that surpassed their initial goal to connect people just for the sake of socializing and currently provide powerful tools for businesses to reach millions of views worldwide, increasing their chances of gaining new customers. This short paper utilizes the Buzz in Social Media data set available at UCI Machine Learning Repository for identifying the attributes in social media content that have the highest correlation to the amount of repercussion it gained. To achieve such result, several linear regression models are constructed, then ranked based on their respective model fit measure (R-squared) and accuracy when tested against unseen data.