A Critical and Semiotic Approach to the Wonderful, Horrible Life Cycle of the Kony 2012 Viral Video
Bibliographic record
Abstract
Is it possible or plausible to represent horror and evil persuasively or authentically in these internet-multi-distributed times? And how can we account for a vast, belligerent reaction of public opinion when the representation of horror or evil is watched by an unprecedented, massive amount of people in North America and elsewhere in the YouTube realm? The unparalleled audience success of an unusually lengthy audiovisual narrative uploaded on YouTube whose subject matter is the quest for justice in East Africa was as remarkable as the diverse audience response of dismay, hope, joy and anger it elicited. The reaction was expressed in traditional print media (e.g. a special issue of The New York Times), in countless blogs and in YouTube – through assorted video-responses and written remarks, many of which were so disparaging that this function was disabled for the Kony 2012 video on YouTube. To try to account for the outpour of supportive viewers and of an increasingly negative response, I analyse its visual rhetoric and also some the critical remarks it triggered. The main strategy of the video consists in what I have described elsewhere as the “index appeal” of popular factuality programming (reality shows, docudramas, talk shows and documentaries), namely, the prevalence of allegedly involuntary signs aimed at producing intense emotions in viewers. Peirce’s semiotic theory of indexicality – as well as of iconic and of symbolic signs – is central to my analytical approach, as well as his critique of dualism. I also revisit a 1948 paper of two seminal figures in the pantheon of communication theory, P. Lazarsfeld and R. Merton. Their functionalist analysis of media effects posits a peculiar “narcotizing dysfunction” to account for the apathy produced in the audience despite the increasing intake of media information by the population. This paradoxical media effect posited by early functionalism, I think, is akin to what is harshly criticized over sixty years later about the significant impact produced by the Kony 2012 video on its vast public. Through the case study of a visual media narrative that gathered an audience as large as a populous nation in less than a week, and an equally impressive array of biting critical views in both traditional and new media, the article aims to account for its remarkable success and its ulterior proclaimed failure as a humanitarian campaign in the streets. I will do so by revisiting the early functionalist critique of mass media effects within the analytical framework of the action of indexical-iconic signs in the age of YouTube.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.005 | 0.001 |
| 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".