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Rhetorical Frames in AI can be thought of as expressions that describe AIdevelopment as a competition between two or more actors, such as governments orcompanies. Examples of such Frames include robotic arms race, AI rivalry,technological supremacy, cyberwarfare dominance and 5G race. Detection ofRhetorical Frames from open sources can help us track the attitudes ofgovernments or companies towards AI, specifically whether attitudes arebecoming more cooperative or competitive over time. Given the rapidlyincreasing volumes of open sources (online news media, twitter, blogs), it isdifficult for subject matter experts to identify Rhetorical Frames in (near)real-time. Moreover, these sources are in general unstructured (noisy) andtherefore, detecting Frames from these sources will require state-of-the-arttext classification techniques. In this paper, we develop RheFrameDetect, atext classification system for (near) real-time capture of Rhetorical Framesfrom open sources. Given an input document, RheFrameDetect employs textclassification techniques at multiple levels (document level and paragraphlevel) to identify all occurrences of Frames used in the discussion of AI. Weperformed extensive evaluation of the text classification techniques used inRheFrameDetect against human annotated Frames from multiple news sources. Tofurther demonstrate the effectiveness of RheFrameDetect, we show multiple casestudies depicting the Frames identified by RheFrameDetect compared againsthuman annotated Frames.

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