The 2014 World Cup in Brazil has been called the best World Cup of all time, with ridiculously high-scoring matches, last minute upsets and emotional victories and defeats. Amidst the craze, we watched the World Cup excitement play out not only on TV and via social media, but also on Swype keyboards around the world. From Suarez’s bite heard ‘round the world to Howard’s saves during the US vs. Belgium game, we’ve got the highlights.
The 2014 World Cup in Brazil has been called the best World Cup of all time, with ridiculously high-scoring matches, last minute upsets and emotional victories and defeats. U.S. viewership of this World Cup was record-high, with 26.4 million Americans watching the final match. What was also unique about this World Cup, however, was that it seemed like there was just as much action on social media as there was on the field. Facebook, Twitter and Instagram were buzzing with pictures, posts and comments surrounding the drama of the matches in real time – it seemed like a celebratory “GOOOALLLLL” appeared on newsfeeds around the globe before television broadcasters could even get the words out. The World Cup final alone resulted in 280 million Facebook interactions between 88 million people and 618,725 tweets per minute, a new Twitter record. There were 2.1 billion World Cup related Google searches and 32.1 million tweets about the World Cup final. For the first time, World Cup matches truly played out on social media.
Amidst the craze, the World Cup also played out on Swype keyboards around the world. Nuance’s Swype noticed a surge in mentions of certain soccer stars and terms, aptly representing both the global reach of these games and the newly established trend of texting and sharing key match moments. The Swype Living Language platform captured many of these moments in real time, where crowdsourced words and phrases typed by Swype users automatically become a part of the core language model. Over the past few weeks, as people all around the world stared at TVs and computer screens in awe as Howard saved and Messi scored, we compiled a comprehensive list of the top trending words from users from countries all across the globe. Data from Swype users who have opted in to Living Language was used to generate the below findings.
The average overall winner of the most Swype mentions went to Neymar, a key player on the host country’s team who was described as “a prodigy since his early teens” by FIFA.*
What is perhaps most interesting about tracking trends and changes in the Swype Living Language dictionary is discovering the direct correspondence between events happening in real time and words that are added to the dictionary and begin trending. For example, over time, American national hero and team USA goal keeper Tim Howard’s popularity sky-rocketed. Use of his name peaked on July 1 at 6.5 times when the U.S. played Belgium and Howard saved an impressive 16 goals from being scored.
Similarly, the use of “Götze” in German increased roughly 12% around the time of the World Cup final due to his World Cup winning goal. The use of “Suarez” skyrocketed after Suarez bit an opponent, causing an explosion of social media content discussing, and, more likely, mocking, the now infamous bite.
Swype’s Living Language feature enables users to write, tap or speak trending words that live in the dictionary, words and phrases that have been generated through crowdsourced data. As the events around us change (like when a player bites another in a World Cup match) the Living Language dictionary quickly adapts to the words users are typing (“Suarez”). This feature empowers users with an intelligent keyboard that remains up-to-date with what’s being talked about all around the world.
To fuel your keyboard with the power of real-time trending words, sports-related or otherwise, enable the Swype Living Language feature by navigating to Swype’s Settings, selecting ‘Language Options’ and clicking ‘Living Language.’ If you have a newer version of Swype, just go Settings and then ‘My Words.’
*averages are to account for disparity between sample size per language