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Accepted Contribution:
Short abstract:
How do image tracking algorithms represent minoritized bodies? I have used situational mapping to capture the misappropriation of trans bodies across different internet locations, as those resurface through reverse search algorithms. What we see through the software becomes the focus of critique.
Long abstract:
It is common practice to post images of one’s body on social media. For some, this constitutes a routine, a naturalized aspect of networked social existence. For others, the digital publication of images of their bodies represents a contentious practice, a call for solidarity. In particular, trans-identifying content creators post images of their bodies to their followers to normalize the diversity of the trans experience. Their images invite viewers to embrace the presentation of different gender expressions and, potentially, to embrace their own non-normative physique in a society that is otherwise dominated by strict stereotypes of gender presentation. However, these images are downloaded, re-uploaded and misappropriated by various actors, who are not necessarily sympathetic or mindful of the original creator’s intentions. I used reverse-image-searching tools to investigate the misappropriation of images of trans bodies across the web. This method yields an algorithmically-curated mapping of the locations, where these images resurface online. Using situational mapping, I captured the misappropriation of these images by third-party-actors so as to draw attention to the double bind of the online representation of trans bodies. On one hand, trans content creators use social media platforms to achieve the broadest possible visibility. On the other hand, third parties take advantage of the public nature of social media platforms to misappropriate images of trans bodies for their own ends. Ultimately, this paper encourages readers to mind the numerous ways in which the non-normative body is treated online and to question how the attention economy affects different bodies.
Experiments with computer vision: transforming and re-envisioning visual data
Session 2 Friday 19 July, 2024, -