Apple Reveals the Most Popular Emoji in the United States

Apple Reveals the Most Popular Emoji in the U.S.
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Whether you love them or hate them, emoji are incredibly popular. Now, Apple has revealed which specific emoji is the most popular across their operating systems — at least with U.S.-based users.

And the winner is — drum-roll — the “face with tears of joy” emoji. According to Apple, that emoji topped its list of the top 10 most popular emoji used by English speakers in the United States.

It’s followed by the red heart, loudly crying face, and others — but, judging by the graphs, the contest wasn’t even close.

Unfortunately, Apple did not release any hard statistics or numbers to go along with the graph. While it’s presumably an accurate chart in spite of that, it also means that we still don’t have any hard data about how many times the emoji has been used, or during what time period.

If you don’t believe Apple’s stats, you can rest assured that the graph is pretty consistent with publicly available data from Emojipedia and EmojiTracker.

Privacy Concerns

While tracking emoji is a pretty lighthearted topic, Apple revealed the graph in a pretty heavy overview of its differential privacy policy. Maybe it’s not as fun as emoji statistics, but it’s no less important.

The emoji tracking was just one example of how Apple can collect and aggregate anonymous data from its users without compromising the personal privacy of individual users. In other words, it can track how many times the “face with tears of joy” emoji was used while preserving the privacy of the people who used it.

Apple applies this differential privacy policy on its various operating systems, such as macOS and iOS, and it uses the results to make features of those operating systems smarter. Features like QuickType and Emoji suggestions, Lookup Hints and Health Type Usage.

To that end, Apple has come up with its own technique known as “local differential privacy.” And it works like this: when data is aired to Apple, a bit of random noise is added to it. As a result, it’s difficult to tell which specific users contributed to the larger picture.

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