Abstract
Framing in news is the way in which journalists depict an issue in terms of a ‘central organizing idea.’ Frames can be a perspective on an issue. We explore the automatic classification of four generic news frames: conflict, human interest, economic consequences, and morality. Complex characteristics of messages such as frames have been studied using thematic content analysis. Indicator questions are formulated, which are then manually coded by humans after reading a text and combined into a characterization of the message. We operationalize this as a classification task and, inspired by the way-of-working of media analysts, we propose a two-stage approach, where we first rate a news article using indicator questions for a frame and then use the outcomes to predict whether a frame is present. We approach human accuracy on almost all indicator questions and frames.
Keywords
- Receiver Operating Characteristic
- News Article
- Corporate Reputation
- Human Interest
- News Coverage
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Odijk, D., Burscher, B., Vliegenthart, R., de Rijke, M. (2013). Automatic Thematic Content Analysis: Finding Frames in News. In: Jatowt, A., et al. Social Informatics. SocInfo 2013. Lecture Notes in Computer Science, vol 8238. Springer, Cham. https://doi.org/10.1007/978-3-319-03260-3_29
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DOI: https://doi.org/10.1007/978-3-319-03260-3_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03259-7
Online ISBN: 978-3-319-03260-3
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