Social media data are being increasingly used in conservation science to study human-nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects' rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.

National parks are key for conserving biodiversity and supporting people's well‐being. However, anthropogenic pressures challenge the existence of national parks and their conservation effectiveness. Therefore, it is crucial to assess how people perceive national parks in order to enhance socio‐political support for conservation. User‐generated data shared by visitors on social media provide opportunities to understand how people perceive (e.g. preferences, feelings, opinions) national parks during nature‐based recreational experiences. In this study, we applied methods from automated natural language processing to assess visitors' sentiment when describing experiences in Instagram posts geolocated inside four national parks in South Africa. We found that visitors' sentiment was positive, and mostly included emotions such as joy, anticipation, trust and surprise, with only a small occurrence of posts with negative feelings. Appreciation of nature, in association with a diverse set of other aspects, such as activities, geographical features and tourist attractions, was used to describe experiences related to nature, wilderness, travelling, holidays and adventures. The type of nature‐based experience described by visitors was park specific, revealing different profiles of parks providing wildlife or scenery experiences. Findings support and highlight the societal role of national parks in providing visitors with opportunities to develop positive connections with nature. Social media data may be used to understand visitors' perceptions, and how the image of national parks is constructed by users in the virtual social environment. This may help inform management for promoting a high‐quality tourism experience, as well as conservation marketing aimed at fostering socio‐political support for national parks and their long‐term conservation effectiveness. A free Plain Language Summary can be found within the Supporting Information of this article.

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As resources for conservation are limited, gathering and analyzing information from digital platforms can help investigate the global biodiversity crisis in a cost-efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human-nature interactions.2. In this study, we introduce novel application methods to automatically collect and analyze textual data on species of conservation concern from digital platforms. An end to end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article.3. The data collected over a one month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location, and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision-making, is generated by the system. 4. The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human-nature interactions in conservation science and practice.

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