@inproceedings{bhatia-etal-2021-openframing,
title = "{O}pen{F}raming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data",
author = "Bhatia, Vibhu and
Akavoor, Vidya Prasad and
Paik, Sejin and
Guo, Lei and
Jalal, Mona and
Smith, Alyssa and
Tofu, David Assefa and
Halim, Edward Edberg and
Sun, Yimeng and
Betke, Margrit and
Ishwar, Prakash and
Wijaya, Derry Tanti",
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.28",
doi = "10.18653/v1/2021.emnlp-demo.28",
pages = "242--250",
abstract = "When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called {``}frames,{''} and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user{'}s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at \url{http://www.openframing.org}, via an API \url{http://www.openframing.org:5000/docs/}, or through our GitHub page \url{https://github.com/vibss2397/openFraming}.",
}
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<abstract>When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.</abstract>
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%0 Conference Proceedings
%T OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data
%A Bhatia, Vibhu
%A Akavoor, Vidya Prasad
%A Paik, Sejin
%A Guo, Lei
%A Jalal, Mona
%A Smith, Alyssa
%A Tofu, David Assefa
%A Halim, Edward Edberg
%A Sun, Yimeng
%A Betke, Margrit
%A Ishwar, Prakash
%A Wijaya, Derry Tanti
%Y Adel, Heike
%Y Shi, Shuming
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F bhatia-etal-2021-openframing
%X When journalists cover a news story, they can cover the story from multiple angles or perspectives. These perspectives are called “frames,” and usage of one frame or another may influence public perception and opinion of the issue at hand. We develop a web-based system for analyzing frames in multilingual text documents. We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research. Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user’s corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5). The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations. Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general. The system is available online at http://www.openframing.org, via an API http://www.openframing.org:5000/docs/, or through our GitHub page https://github.com/vibss2397/openFraming.
%R 10.18653/v1/2021.emnlp-demo.28
%U https://aclanthology.org/2021.emnlp-demo.28
%U https://doi.org/10.18653/v1/2021.emnlp-demo.28
%P 242-250
Markdown (Informal)
[OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data](https://aclanthology.org/2021.emnlp-demo.28) (Bhatia et al., EMNLP 2021)
ACL
- Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, and Derry Tanti Wijaya. 2021. OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 242–250, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.