How to address data privacy concerns when using social media data in conservation science

Minin, Enrico Di; Fink, Christoph; Hausmann, Anna; Kremer, Jens; Kulkarni, Ritwik

doi:10.1111/cobi.13708

Cited by 89 publications

(52 citation statements)

References 30 publications

(56 reference statements)

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“…As it stands, the pipeline extracts only news articles publicly available and intended for public dissemination and not information of intrinsically private nature such as closed groups. Still, accessing such closed groups without permission raises ethical concerns that may require minimum considering data privacy requirements (Di Minin et al., 2021; Zimmer, 2010).…”

Section: Discussionmentioning

confidence: 99%

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Automated retrieval of information on threatened species from online sources using machine learning

Kulkarni

Minin

2021

Methods Ecol Evol

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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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Section: Discussionmentioning

confidence: 99%

“…An important aspect of digital data is that they allow for relatively low‐cost research on often freely available data. However, attention should be paid to ensure responsible use of these data in accordance with data privacy requirements (Di Minin et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Automated retrieval of information on threatened species from online sources using machine learning

Kulkarni

Minin

2021

Methods Ecol Evol

Self Cite

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“…Researchers should recognize and minimize potential harms to individuals and groups who may find the methods disagreeable without the researchers' knowledge. This includes deidentifying, anonymizing, summarizing, and minimizing collected data sets as soon as possible; evaluating the possible broader consequences of sharing research results and data other than intended conservation outcomes (Di Minin et al 2021); and reevaluating the contexts in which researchers operate in light of their observations, including the exclusion of groups found to oppose the collection of data where reasonably possible.…”

Section: Covert Research Ethicsmentioning

confidence: 99%

Ethics and governance for internet‐based conservation science research

Thompson

Hall

Morrison

et al. 2021

Conservation Biology

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Internet-based research is increasingly important for conservation science and has wideranging applications and contexts, including culturomics, illegal wildlife trade, and citizen science. However, online research methods pose a range of ethical and legal challenges. Online data may be protected by copyright, database rights, or contract law. Privacy rights may also restrict the use and access of data, as well as ethical requirements from institutions. Online data have real-world meaning, and the ethical treatment of individuals and communities must not be marginalized when conducting internet-based research. As ethics frameworks originally developed for biomedical applications are inadequate for these methods, we propose that research activities involving the analysis of preexisting online data be treated analogous to offline social science methods, in particular, nondeceptive covert observation. By treating internet users and their data with respect and due consideration, conservationists can uphold the public trust needed to effectively address real-world issues. KEYWORDS compliance, copyright, cyberethics, data protection, integrity, intellectual property, internet mediated, privacy PALABRAS CLAVE cibernética, cumplimiento, derechos de autor, integridad, mediado por internet, privacidad, propiedad intelectual, protección de datosThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Similarly, (Minin et al, 2021) identified that online privacy had become one of the prime concerns of people using social media. Furthermore, social media users consider using their personal information for corporate purposes to breach their privacy and feel concerned about their data.…”

Section: Social Media Analytics and Ethical Perceptionmentioning

confidence: 99%

Social Media Analytics Lens: A Study on Consumer Perceived Risk to Share Information on Social Media Networks

Majeed

Mahmood

Molnár

et al. 2021

HSSR

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Purpose of the study: This study focuses on understanding the potential unfavourable social media analytics practices (SMA) and how they may lead to the negative perception of consumer self-disclosure on social media sites and causing market discomfort for people. We also examined a framework regarding the SMA practices, and their impact on the customer’s trust to use social media networks. Methodology: Data were collected from 527 social media users with the help of an online questionnaire. Partial Least Squares Structural equation modeling—PLS-SEM was used using SmartPLS to validate the questionnaire and measure the hypothesized relationships between the constructs. Main Findings: The results show that perceived risk mediates the relationship between the perceived ethicality of social media analytics and the marketing discomfort of the consumers. Moreover, we also found that the negatively perceived ethicality of social media analytics leads to marketing discomfort of the consumers. This negative attitude impacts the consumer’s intention to purchase, interest in the product, and intention to be vulnerable toward the company. Applications of this study: These findings have practical utility for all types of organizations that use social media. This study is general for all social media sites. This study also provides direction for future research, such as managing the personal information of the social media users from being used by the organizations without consumer permission and developing ethical standards to use the consumer information for the marketing strategies. Novelty/Originality of this study: Social media analytics (SMA) gathered data from the social media platforms such as Twitter, Instagram, and blogs, representing a massive room to utilize public data, and these lead towards the ethical concerns of the consumers. For this problem, we studied a complete framework to identify the major outcomes of the PESMA on the consumers' self-disclosure and marketing comfort.

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How to address data privacy concerns when using social media data in conservation science

Cited by 89 publications

References 30 publications

Automated retrieval of information on threatened species from online sources using machine learning

Automated retrieval of information on threatened species from online sources using machine learning

Ethics and governance for internet‐based conservation science research

Social Media Analytics Lens: A Study on Consumer Perceived Risk to Share Information on Social Media Networks

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