multiBERT: A Classifier for Sponsored Social Media Content

Social media's rise has given birth to a new class of celebrities called influencers. People who have amassed a following on social media sites like Twitter, YouTube, and Instagram are known as influencers. These people have the ability to sway the beliefs and purchase choices of those who follow them. Consequently, companies have looked to collaborate with influencers in order to market their goods and services. But as sponsored content has grown in popularity, it has becoming harder to tell if a piece is an independent opinion of an influencer or was sponsored by a company. This study investigates the use of machine learning models to categorise influencer tweets as either sponsored or unsponsored. By utilising transformer language models, like BERT, we are able to discover relationships and patterns between a brand and an influencer. Machine learning algorithms may assist in determining if a tweet or Instagram post is a sponsored post or not by examining the context and content of influencer tweets and their Instagram post captions. To evaluate data from Instagram and Twitter together, this work presents a novel method that compares the models while accounting for performance criteria including accuracy, precision, recall, and F1 score.