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Detecting Frames and Causal Relationships in Climate Change Related Text Databases Based on Semantic Features

Abstract The subliminal impact of framing of social, political and environmental issues such as climate change has been studied for decades in political science and communications research. Media framing offers an “interpretative package" for average citizens on how to make sense of climate change and its consequences to their livelihoods, how to deal with its negative impacts, and which mitigation or adaptation policies to support. A line of related work has used bag of words and word-level features to detect frames automatically in text. Such works face limitations since standard keyword based features may not generalize well to accommodate surface variations in text when different keywords are used for similar concepts.

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Created Date 2018
Contributor Alashri, Saud (Author) / Davulcu, Hasan (Advisor) / Desouza, Kevin C. (Committee member) / Maciejewski, Ross (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Subject Computer science / Artificial intelligence / Climate Change / Data Mining / Machine Learning / Natural Language Processing / Semantic Computing
Type Doctoral Dissertation
Extent 120 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis