EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification,
  Environmental Impact Analysis, and Stance Detection
Francesca Grasso Stefano Locci Giovanni Siragusa Luigi Di Caro
Anthropogenic ecological crisis constitutes a significant challenge that all
within the academy must urgently face, including the Natural Language
Processing (NLP) community. While recent years have seen increasing work
revolving around climate-centric discourse, crucial environmental and
ecological topics outside of climate change remain largely unaddressed, despite
their prominent importance. Mainstream NLP tasks, such as sentiment analysis,
dominate the scene, but there remains an untouched space in the literature
involving the analysis of environmental impacts of certain events and
practices. To address this gap, this paper presents EcoVerse, an annotated
English Twitter dataset of 3,023 tweets spanning a wide spectrum of
environmental topics. We propose a three-level annotation scheme designed for
Eco-Relevance Classification, Stance Detection, and introducing an original
approach for Environmental Impact Analysis. We detail the data collection,
filtering, and labeling process that led to the creation of the dataset.
Remarkable Inter-Annotator Agreement indicates that the annotation scheme
produces consistent annotations of high quality. Subsequent classification
experiments using BERT-based models, including ClimateBERT, are presented.
These yield encouraging results, while also indicating room for a model
specifically tailored for environmental texts. The dataset is made freely
available to stimulate further research.