GENERATIVE CONTRASTIVE LEARNING FOR STRUCTURAL FRAMING ANALYSIS
Xu, Jialiang
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https://hdl.handle.net/2142/117047
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Title
GENERATIVE CONTRASTIVE LEARNING FOR STRUCTURAL FRAMING ANALYSIS
Framing is the act of selecting “some aspects of a perceived reality and [making] them more salient in a communicating text” . Framing has been widely used in journalism to influence public opinion. However, analysis of news framing has majorly relied on human expert efforts. Efforts have been put into developing automatic framing analysis via computational linguistic approaches. In this work, we propose a novel large-scale, multi-agency news dataset with crowd-sourced political stances and factuality labels to facilitate framing analysis. We propose two ways of conducting framing analyses on this dataset, the first is via learning a “switch” in the embedding space to change the generation trend, and the second utilizes a Generative Adversarial Network under a contrastive learning framework. We further create an interactive demo website to directly display results. Our code and dataset will be released to facilitate future research.
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