Murder in an Attention Economy: Media, Celebrity and the Prosecution of Luka Magnotta
Bibliographic record
Abstract
David Schmid has observed that hegemonic definitions of fame tend to present it as an inherently positive phenomenon in which fame is achieved by the talented on a meritorious basis. However, he argues that ‘the iconic status of serial killers in contemporary American culture’ provides ‘compelling evidence of the collapse in the difference between fame and notoriety,’ going on to suggest that all too often ‘today the famous are often the visible, rather than the talented’ (2015: 297). Evidence of this collapse is the fame and notoriety of Luka Magnotta, a man who became known for the brutal murder and dismemberment of university student Jun Lin in Montreal in 2012 (McKenna: 2023). Magnotta had been a failed model, stripper and porn star who craved fame and celebrity and the internet provided him with a platform through which he could perform a celebrity masquerade. He developed over 70 Facebook profiles that interacted with each other and that cross-referenced information that he posted on discussion forums, websites, and blogs. These personas worked together to construct a narrative that presented him as an important figure linked to killers and celebrities. However, when his fictitious association with Canadian serial killer Karla Homolka, and the news of the even more unlikely relationship with Madonna, failed to generate the kind of attention that he craved, he began sharing videos of himself online killing cats. The first of these, 1 Boy 2 Kittens, was shared to YouTube and saw him suffocate two kittens in a plastic bag with a vacuum cleaner. The video inspired a vitriolic reaction and an online community quickly formed with members in America, Australia, Great Britain and Canada, all focussed on the shared goal of identifying and capturing Magnotta, fearful that he would graduate to human victims. They cited evidence that many of the world’s most notorious serial killers, Ian Brady, Ed Kemper, Jeffrey Dahmer, Albert DeSalvo, and David Berkowitz had all begun by killing animals. When the footage of the murder on Jun Lin appeared online circulated on ‘Shock Sites’ (Jones: 2010) titled 1 Lunatic 1 Icepick, the community played a pivotal role in identifying and apprehending Magnotta. This paper will explore not only the role that social media played in Magnotta’s constructed celebrity, but also the role that it played in his capture through a community of amateur sleuths connected in the condemnation of his actions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".