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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\n            <jats:italic>likes<\/jats:italic>\n            and\n            <jats:italic>retweets<\/jats:italic>\n            . 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\n            <jats:italic>animal photography and sightings<\/jats:italic>\n            ,\n            <jats:italic>illegal wildlife trade and trophy hunting<\/jats:italic>\n            ,\n            <jats:italic>deforestation and destruction of nature<\/jats:italic>\n            , and\n            <jats:italic>climate change and action<\/jats:italic>\n            in a broader sense.\n          <\/jats:p>","DOI":"10.1145\/3662685","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T11:17:47Z","timestamp":1720523867000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["User Engagement Triggers in Social Media Discourse on Biodiversity Conservation"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6917-5066","authenticated-orcid":false,"given":"Nina","family":"Dethlefs","sequence":"first","affiliation":[{"name":"University of Hull, Hull, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1937-9837","authenticated-orcid":false,"given":"Heriberto","family":"Cuayahuitl","sequence":"additional","affiliation":[{"name":"University of Lincoln, Lincoln, United Kingdom of Great Britain and Northern Ireland"}]}],"member":"320","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/1075547017735113"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13280-015-0705-1"},{"key":"e_1_3_3_4_2","unstructured":"Jimmy Lei Ba Jamie Ryan Kiros and Geoffrey E. 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