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Recurrent neural network reveals overwhelming sentiment against 2017 review of US monuments from humans and bots
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Recurrent neural network reveals overwhelming sentiment against 2017 review of US monuments from humans and bots
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Recurrent neural network reveals overwhelming sentiment against 2017 review of US monuments from humans and bots
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Journal Article
Recurrent neural network reveals overwhelming sentiment against 2017 review of US monuments from humans and bots
2020
Overview
In the United States, the conservation of federal lands reflects a social history of public advocacy, public policy, and public comments. US federal agencies solicit public comments to scope for ideas, solve problems, and use the best available science for policy‐making, legislation, and management. Online comment submission has led to staggering numbers of comments that are challenging to summarize. Here, we analyze comments received by the Department of the Interior in response to the proposed executive review of 27 national monuments designated and expanded between 1996 and 2016. We used a deep recurrent neural network (AWD‐LSTM) to classify sentiment of 754,707 comments with higher precision and recall (F1‐score = 0.98) than support vector machine and Naïve Bayes approaches. Over 97% of unique comments opposed the executive review, suggesting overwhelming support for maintaining national monument designations. Using cosine similarity, we also found that duplicates or potential automated software bots comprised over two‐thirds of comments. We offer recommendations for comment submission, collection, and analysis in the current techno‐political climate.
