Papers › Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
Kiki Kills: Identifying Dangerous Challenge Videos from Social Media
Nupur Baghel, Yaman Kumar, Paavini Nanda, Rajiv Ratn Shah, Debanjan Mahata, Roger Zimmermann
There has been upsurge in the number of people participating in challenges made popular through social media channels. One of the examples of such a challenge is the Kiki Challenge, in which people step out of their moving cars and dance to the tunes of the song, 'Kiki, Do you love me?'. Such an action makes the people taking the challenge prone to accidents and can also create nuisance for the others traveling on the road. In this work, we introduce the prevalence of such challenges in social media and show how the machine learning community can aid in preventing dangerous situations triggered by them by developing models that can distinguish between dangerous and non-dangerous challenge videos. Towards this objective, we release a new dataset namely MIDAS-KIKI dataset, consisting of manually annotated dangerous and non-dangerous Kiki challenge videos. Further, we train a deep learning model to identify dangerous and non-dangerous videos, and report our results.
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