RheFrameDetect: A Text Classification System for Automatic Detection of Rhetorical Frames in AI from Open Sources

TL;DR

RheFrameDetect is a system that automatically detects rhetorical frames in AI discussions from open sources, helping track competitive or cooperative attitudes over time using advanced text classification techniques.

Contribution

This paper introduces RheFrameDetect, a novel multi-level text classification system for real-time detection of rhetorical frames in unstructured open-source texts about AI.

Findings

High accuracy in detecting rhetorical frames compared to human annotations

Effective in real-time processing of large volumes of online sources

Demonstrated case studies show practical applicability

Abstract

Rhetorical Frames in AI can be thought of as expressions that describe AI development as a competition between two or more actors, such as governments or companies. Examples of such Frames include robotic arms race, AI rivalry, technological supremacy, cyberwarfare dominance and 5G race. Detection of Rhetorical Frames from open sources can help us track the attitudes of governments or companies towards AI, specifically whether attitudes are becoming more cooperative or competitive over time. Given the rapidly increasing volumes of open sources (online news media, twitter, blogs), it is difficult for subject matter experts to identify Rhetorical Frames in (near) real-time. Moreover, these sources are in general unstructured (noisy) and therefore, detecting Frames from these sources will require state-of-the-art text classification techniques. In this paper, we develop RheFrameDetect, a text classification system for (near) real-time capture of Rhetorical Frames from open sources. Given an input document, RheFrameDetect employs text classification techniques at multiple levels (document level and paragraph level) to identify all occurrences of Frames used in the discussion of AI. We performed extensive evaluation of the text classification techniques used in RheFrameDetect against human annotated Frames from multiple news sources. To further demonstrate the effectiveness of RheFrameDetect, we show multiple case studies depicting the Frames identified by RheFrameDetect compared against human annotated Frames.