  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.
