Abstract
Abstract Human perceptions of nature, once the domain of the social sciences, are now an important part of environmental research. However, the data and tools to tackle this research are lacking or are difficult to apply. Here, we present a collection of text classifier models to identify text relevant to the broad topics of hunting and nature, describing whether opinions are pro‐ or against‐hunting, or show interest, concern or dislike of nature. The methods also include a biographical classification—describing whether the author of the text is a person, nature expert, nature organisation or ‘Other’. The classifiers were developed using an extensive social media dataset, and are designed to support qualitative analysis of big data (especially from Twitter). The classifiers accurately identified biographies, text related to hunting and nature and the stance towards hunting and nature (weighted F‐scores: 0.79–0.99; 1 indicates perfect accuracy). These classifiers, alongside an array of other text processing and analysis functions, are presented in the form of an R package classecol. classecol also acts as a proof of concept that nature‐related text classifiers can be developed with high accuracy.
Highlights
Ecology has become more transdisciplinary to better understand our environment
We present a collection of text classifier models to identify text relevant to the broad topics of hunting and nature, describing whether opinions are pro-or against-hunting, or show interest, concern or dislike of nature
These classifiers, alongside an array of other text processing and analysis functions, are presented in the form of an R package classecol. classecol acts as a proof of concept that nature-related text classifiers can be developed with high accuracy
Summary
Ecology has become more transdisciplinary to better understand our environment. For example, ecosystem services reflect health, economic and cultural values (Kareiva et al, 2011), and journals and societies want to study human relationships with nature (Gaston et al, 2019; Society for Conservation Biology working groups, 2020). Classecol acts as a proof of concept that nature-related text classifiers can be developed with high accuracy. Stance analysis could help recognize the dislike of pangolins in the example above, but this method is often time-consuming to develop as it requires large training datasets alongside complex machine learning models.
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