FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines
This tool supports social science research by providing a multi-perspective analysis of framing in news headlines, but it is incremental as it builds on existing frame corpora and methods.
The paper tackles the problem of revealing framing in news articles, which is important but neglected in information seeking, by presenting FrameFinder, an open tool that extracts and analyzes frames from three perspectives and demonstrates its merits using a gun violence frame corpus.
Revealing the framing of news articles is an important yet neglected task in information seeking and retrieval. In the present work, we present FrameFinder, an open tool for extracting and analyzing frames in textual data. FrameFinder visually represents the frames of text from three perspectives, i.e., (i) frame labels, (ii) frame dimensions, and (iii) frame structure. By analyzing the well-established gun violence frame corpus, we demonstrate the merits of our proposed solution to support social science research and call for subsequent integration into information interactions.