arXiv subjects

Arman Irani

Publications and source records attributed to Arman Irani.

3 recordsLinked to original sources

arXiv2024

A Discourse Analysis Framework for Legislative and Social Media Debates

How can we capture the dynamics of deliberation in a debate? In an increasingly divided and misinformed world, understanding the relationship between who is arguing and what they are arguing about is becoming critical for fostering a meaningful exchange of ideas. Given the vast array of available platforms for people to express their viewpoints and deliberate on issues, how can we develop methods to accurately analyze these processes? Luckily, there is an abundance of debate data available, ranging from: (a) formal proceedings, such as committee hearings in legislatures, to (b) online discussion forums, such as Reddit. Here we introduce DALiSM, a data-driven argument-centric framework, to analyze discourse dynamics in diverse and multi-party spaces at scale. We develop methods to harness and extend the state-of-the-art in computational argumentation for: (a) identifying arguments from long-form raw texts, (b) calculating the intensity of deliberation, and (c) modeling the evolution of discourse over time. We deploy our framework as a comprehensive and interactive dashboard for dynamically viewing the outputs of DALiSM to clearly understand the nature of a discourse event. To showcase the importance and utility of DALiSM, we apply our framework to U.S. congressional committee hearings from 2005 to 2023 (109th to 117th Congresses), and to selected Reddit communities from 2008 to 2023. This case study reveals substantive insights into deliberative behavior in online and offline spaces.

cs.SI↗

arXiv2024

ArguSense: Argument-Centric Analysis of Online Discourse

How can we model arguments and their dynamics in online forum discussions? The meteoric rise of online forums presents researchers across different disciplines with an unprecedented opportunity: we have access to texts containing discourse between groups of users generated in a voluntary and organic fashion. Most prior work so far has focused on classifying individual monological comments as either argumentative or not argumentative. However, few efforts quantify and describe the dialogical processes between users found in online forum discourse: the structure and content of interpersonal argumentation. Modeling dialogical discourse requires the ability to identify the presence of arguments, group them into clusters, and summarize the content and nature of clusters of arguments within a discussion thread in the forum. In this work, we develop ArguSense, a comprehensive and systematic framework for understanding arguments and debate in online forums. Our framework consists of methods for, among other things: (a) detecting argument topics in an unsupervised manner; (b) describing the structure of arguments within threads with powerful visualizations; and (c) quantifying the content and diversity of threads using argument similarity and clustering algorithms. We showcase our approach by analyzing the discussions of four communities on the Reddit platform over a span of 21 months. Specifically, we analyze the structure and content of threads related to GMOs in forums related to agriculture or farming to demonstrate the value of our framework.

cs.SI↗

arXiv2024

WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining

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).

cs.CL↗