Comparing a BERT Classifier and a GPT classifier for Detecting Connective Language Across Multiple Social Media

Josephine Lukito; Bin Chen; Gina M. Masullo; Natalie Jomini Stroud

EMNLP 2024main1 citations

Comparing a BERT Classifier and a GPT classifier for Detecting Connective Language Across Multiple Social Media

Josephine Lukito, Bin Chen, Gina M. Masullo, Natalie Jomini Stroud

Abstract

This study presents an approach for detecting connective language—defined as language that facilitates engagement, understanding, and conversation—from social media discussions. We developed and evaluated two types of classifiers: BERT and GPT-3.5 turbo. Our results demonstrate that the BERT classifier significantly outperforms GPT-3.5 turbo in detecting connective language. Furthermore, our analysis confirms that connective language is distinct from related concepts measuring discourse qualities, such as politeness and toxicity. We also explore the potential of BERT-based classifiers for platform-agnostic tools. This research advances our understanding of the linguistic dimensions of online communication and proposes practical tools for detecting connective language across diverse digital environments.

BibTeX

@inproceedings{lukito-etal-2024-comparing,
    title = "Comparing a {BERT} Classifier and a {GPT} classifier for Detecting Connective Language Across Multiple Social Media",
    author = "Lukito, Josephine  and
      Chen, Bin  and
      Masullo, Gina M.  and
      Stroud, Natalie Jomini",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1067/",
    doi = "10.18653/v1/2024.emnlp-main.1067",
    pages = "19140--19153"
}