Cite this as: Graham, S., Davidson, K. and Huffer. D. 2024 Behind Closed Doors: The Human Remains Trade within Private Facebook Groups, Internet Archaeology 67. https://doi.org/10.11141/ia.67.14
The existence of a thriving trade in human remains online is facilitated by social media platforms. While much of this trade is conducted in fully public forums such as e-commerce platforms, the retail website of bricks-and-mortar stores, public personal and business pages on social media, etc., there also exist numerous private groups using the affordances of various social media platforms to buy, sell, and share photographs of human remains. This article describes a case study of four private Facebook groups featuring people who buy and sell human remains, to explore how the discourses of the trade may be different when not made in public. Using a close-reading approach on the text of posts and threaded conversations, and associated visual similarity analysis of the accompanying photographs, we observe, among other things, a strikingly 'more professional' approach, shibboleths and patterns of behaviour that serve to create group identities. We analyse posts made over a seven-week period across the selected private groups in the run-up to the 2023 holiday season. Given the issues of privacy raised by studying private groups, we also experiment with a locally hosted large language model to see if it could classify discourses meaningfully without the intervention of a researcher having to read the original posts. This case study might also serve as a model for other kinds of research investigating the reception of various archaeological topics that might be discussed and understood differently in private versus public venues.
Corresponding author: Shawn Graham
ShawnGraham@cunet.carleton.ca
Carleton University
Katherine Davidson
Carleton University
Damien Huffer
University of Queensland
Figure 1: Facebook Group 1, tags connected to tags by virtue of appearing in the same conversations. The thicker the lines, the greater the number of co-occurrences. Tags that appear as most central are scaled accordingly larger. Colour indicates 'modules' or subgroups based on similarity of interlinkages
Figure 2: Facebook Group 2 tags connected to tags by virtue of appearing in the same conversations. The thicker the lines, the greater the number of co-occurrences. Tags that appear as most central are scaled accordingly larger. Colour indicates 'modules' or subgroups based on similarity of interlinkages
Figure 3: Facebook Group 3 tags connected to tags by virtue of appearing in the same conversations. The thicker the lines, the greater the number of co-occurrences. Tags that appear as most central are scaled accordingly larger. Colour indicates 'modules' or subgroups based on similarity of interlinkages
Figure 4: Facebook Group 4 tags connected to tags by virtue of appearing in the same conversations. The thicker the lines, the greater the number of co-occurrences. Tags that appear as most central are scaled accordingly larger. Colour indicates 'modules' or subgroups based on similarity of interlinkages
Figure 5: Programming a visual similarity workflow, as represented by the Visual Programming widget on Orange Data Mining
Figure 6: Visual similarity of photographs in posts from the Facebook groups examined in this study, represented via multi-dimensional scaling. Connecting lines represent similarity above a threshold which in this visualisation may be controlled by a slider; the user can then click on data points to manually inspect multiple images
Table 1: Themes identified across all posts collected, with frequency represented in the 'Count' column
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