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Is the viral #10YearChallenge a Facebook’s ploy?

The new viral post “10-Year Challenge” has created a controversy with a suspicion that Facebook has something to do with it. Over 5.2 million have responded to the post so far by sharing two of their photos, taken 10 years apart with a hashtag #10yearchallenge or #2009vs2019.

The advertisers spent over $39.4 billion to advertise their content on Facebook last year. Most of them were unable to explain how Facebook’s algorithms worked which were kept in the dark until recently. However, Facebook has begun to be transparent how their algorithms work which customize the users’ news feed.

It has been claimed that Facebook might have created this post with a motive to improvise their facial recognition technology by gathering tremendous amount of data through this post. But, how would this data help them in doing so?

Kate O’Neill, a writer for Wired, gives us a broader perception. Let’s just imagine that a system has to be trained to perform facial recognition considering age as a parameter. To do so, one might require colossal dataset containing people’s pictures which were taken a considerable amount of years apart. This has led to a healthy sense of suspicion that if this was a ploy by Facebook.

“The 10-year challenge is a user-generated meme that started on its own, without our involvement. It’s evidence of the fun people has on Facebook, and that’s it.”, replies Facebook to one of the user’s articles posted on Twitter. Many people acknowledge this reply since Facebook has already access to these photos as 350 million photos are being uploaded every day.

This post gives Facebook the opportunity to use the data for their facial recognition technology which gives them an easier version of how the data can be used to train their system. However, Facebook cannot afford to be in the news for all the wrong reasons again after the Cambridge Analytica scandal. Even Mark Zuckerberg wouldn’t want to face the heat again.

Let us know your opinion in the comment section below.

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