Building a Multi-Platform, BERT Classifier for Detecting Connective Language
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.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: political communication, machine learning classifier, connective language
Contribution Types: Model analysis & interpretability, Data analysis
Languages Studied: English
Submission Number: 4013
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