Papers by Prakash Ishwar

4 papers
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data (2021.emnlp-demo)

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Challenge: Existing frameworks for analyzing frames in multilingual text documents are available online and via an API.
Approach: They propose a web-based system for analyzing frames in multilingual text documents . framework combines unsupervised and supervised machine learning and leverages a state-of-the-art multilingual language model .
Outcome: The proposed framework can significantly improve frame prediction performance while requiring a small sample of manual annotations.
Multi-Label and Multilingual News Framing Analysis (2020.acl-main)

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Challenge: Recent studies have focused on news framing in English, but few studies have explored how it can be extended to other languages and in multi-label settings.
Approach: They propose a method that leverages dictionary and few annotations to detect frames from just the headline in a low-resource context.
Outcome: The proposed method performs better than translating the entire headline to the source language . it can be scaled up to many languages, even those without existing translation technologies .
Prediction of People’s Emotional Response towards Multi-modal News (2022.aacl-main)

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Challenge: BU-NEmo dataset extends from 320 to 1,297 news headline and lead image pairings and collects 38,910 annotations in a crowdsourcing experiment.
Approach: They extend the U.S. gun violence news-to-emotions dataset from 320 to 1,297 news headline and lead image pairings and collect annotations in a crowdsourcing experiment.
Outcome: The proposed models outperform baseline models on the NEmo+ dataset by large margins across several metrics.
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)

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Challenge: Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event .
Approach: They find that combining lead images and contextual information with text improves news framing . they release the first multimodal news framming dataset related to gun violence in the u.s.
Outcome: The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance .

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