Identifying opportunities for expert-mediated triangulation in monitoring wildlife trade on social media.

2021 
Wildlife trade has rapidly expanded on social media platforms in recent years, offering an easy means for traders to access international markets. Investigating this trade activity poses a complex challenge to researchers seeking to understand online trade and moderators seeking to disrupt illicit and harmful activity. Current survey methods frequently rely on text-based searches and focus on posts where the advertisement is explicit. However, such approaches risk overlooking a growing volume of relevant content, particularly outside of social media groups. Here, we used posts created by pages advertising West African birds as a case study to explore the availability of information for making inferences about trade activity on social media; specifically, information indicating that trade activity was occurring or that could be used to infer trade routes. We recorded 400 posts from 12 pages that we inferred either promoted or facilitated wildlife trade, of which only 19.7% were explicit advertisements and only 23.8% contained taxa-related terms. In the remaining 341 posts, profile information was the most common indicator of trade activity, but a variety of indicators were identified across imagery, text and comments. We identified multiple types of geographical information that could help infer trade routes and thus the likely legality of trade, although most were relatively rare and sometimes contradictory. Our findings suggest that triangulating multiple types of information from within, across and beyond posts is vital for effectively identifying and interpreting wildlife trade content on social media. We therefore recommend that expert-mediated triangulation should be integrated within the development and use of automated detection systems and the moderation practices of social media companies. This article is protected by copyright. All rights reserved.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    0
    Citations
    NaN
    KQI
    []