multiBERT: A Classifier for Sponsored Social Media Content

Kshitij Malvankar, Kshitij Malvankar, Enda Fallon, Paul Connolly, Kieran Flanagan

2024

Abstract

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.

Paper Citation

in Harvard Style

Malvankar K., Fallon E., Connolly P. and Flanagan K. (2024). multiBERT: A Classifier for Sponsored Social Media Content. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 706-713. DOI: 10.5220/0012632400003690

in Bibtex Style

@conference{iceis24,
author={Kshitij Malvankar and Enda Fallon and Paul Connolly and Kieran Flanagan},
title={multiBERT: A Classifier for Sponsored Social Media Content},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={706-713},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012632400003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - multiBERT: A Classifier for Sponsored Social Media Content
SN - 978-989-758-692-7
AU - Malvankar K.
AU - Fallon E.
AU - Connolly P.
AU - Flanagan K.
PY - 2024
SP - 706
EP - 713
DO - 10.5220/0012632400003690
PB - SciTePress