Topic Modeling for Frame Analysis of News Media

Published in Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, 2016

Recommended citation: Pashakhin S. Topic Modeling for Frame Analysis of News Media, in: Proceedings of the Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10-12 November 2016 / Eds.: S. I. Balandin, A. Filchenkov, L. Pivovarova, J. Zizka. FRUCT Oy, 2016. P. 103-106. https://fruct.org/publications/abstract-AINL-FRUCT-2016/files/Pas.pdf

MMedia frames have been traditionally extracted via manual content and discourse analysis. Such approach has a limited ability to deal with large text collections and is prone to subjectivity both in terms of text selection and interpretation. We illustrate possibilities and limitations of topic modeling for frame detection applying this method to a collection of 50,000 news items related to the Ukrainian crisis and retrieved from a Russian and a Ukrainian TV channels websites. We conclude that although topic modeling results allow to make assumptions about how topic is framed it is still not as precise as human reading of texts.

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Recommended citation: Pashakhin S. Topic Modeling for Frame Analysis of News Media, in: Proceedings of the Artificial Intelligence and Natural Language AINL FRUCT 2016 Conference, Saint-Petersburg, Russia, 10-12 November 2016 / Eds.: S. I. Balandin, A. Filchenkov, L. Pivovarova, J. Zizka. FRUCT Oy, 2016. P. 103-106.