User Engagement Triggers in Social Media Discourse on Biodiversity Conservation

Studies in digital conservation have increasingly used social media in recent years as a source of data to understand the interactions between humans and nature, model and monitor biodiversity, and analyse online discourse about the conservation of species. Current approaches to digital conservation are for the most part purely frequentist, i.e. focused on easily trackable and quantifiable features, or purely qualitative, which allows a deeper level of interpretation, but is less scalable. Our approach aims to evaluate the applicability of recent advances in deep learning in combination with semi-automatic analysis. We present a multimodal neural learning framework that experiments with different combinations of linguistic and visual features and metadata of tweets to predict user engagement from a function of likes and retweets. Experimental results show that text is the single most effective modality for prediction when a large amount of training data is available. For smaller datasets, drawing information from multiple modalities can boost performance. Notably, we find a negative effect of large pre-trained language models when dealing with substantially unbalanced datasets. A qualitative analysis into the triggers of user engagement with tweets reveals that it emerges from a combination of online discourse topic and sentiment, and is often amplified by user activity, e.g. when content originates from an influencer account. We find clear evidence of existing sub-communities around specific topics, including animal photography and sightings, illegal wildlife trade and trophy hunting, deforestation and destruction of nature and climate change and action in a broader sense.