  We propose WIBA, a novel framework and suite of methods that enable the
comprehensive understanding of "What Is Being Argued" across contexts. Our
approach develops a comprehensive framework that detects: (a) the existence,
(b) the topic, and (c) the stance of an argument, correctly accounting for the
logical dependence among the three tasks. Our algorithm leverages the
fine-tuning and prompt-engineering of Large Language Models. We evaluate our
approach and show that it performs well in all the three capabilities. First,
we develop and release an Argument Detection model that can classify a piece of
text as an argument with an F1 score between 79% and 86% on three different
benchmark datasets. Second, we release a language model that can identify the
topic being argued in a sentence, be it implicit or explicit, with an average
similarity score of 71%, outperforming current naive methods by nearly 40%.
Finally, we develop a method for Argument Stance Classification, and evaluate
the capability of our approach, showing it achieves a classification F1 score
between 71% and 78% across three diverse benchmark datasets. Our evaluation
demonstrates that WIBA allows the comprehensive understanding of What Is Being
Argued in large corpora across diverse contexts, which is of core interest to
many applications in linguistics, communication, and social and computer
science. To facilitate accessibility to the advancements outlined in this work,
we release WIBA as a free open access platform (wiba.dev).
