@inproceedings{ae38f6f383ca4045bf9bb84f31da7a2d,
title = "FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines",
abstract = "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.",
keywords = "Computational Framing Extraction, Exploratory Content Analysis, Media Bias, Online News, Text Representations",
author = "Markus Reiter-Haas and Beate Kl{\"o}sch and Markus Hadler and Elisabeth Lex",
note = "Publisher Copyright: {\textcopyright} 2024 Owner/Author.; 2024 Conference on Human Information Interaction and Retrieval : CHIIR 2024 ; Conference date: 10-03-2024 Through 14-03-2024",
year = "2024",
month = mar,
day = "10",
doi = "10.1145/3627508.3638308",
language = "English",
series = "CHIIR 2024 - Proceedings of the 2024 Conference on Human Information Interaction and Retrieval",
publisher = "Association of Computing Machinery",
pages = "381--385",
booktitle = "CHIIR 2024 - Proceedings of the 2024 Conference on Human Information Interaction and Retrieval",
address = "United States",
}
