A Data-Driven Take on Flappy Bird’s Meteoric Success

Much has been written about the meteoric rise and abrupt demise of Flappy Bird, the highly addictive mobile game that seems to have captured the world over the past couple of weeks. Dong Nguyen, an independent game developer in Vietnam, who launched the game obscurely last May, decided to take it down from all app stores after achieving heights previously touched only by major franchises like Candy Crush and Angry Birds, ending the frenzy around the frustratingly difficult game, while adding to the already heightened media spectacle.

What is it about Flappy Bird that made it so successful, and why did it take so long for the game to go “viral“? The app stores are littered with thousands of free casual games that use similarly addictive gameplay. What can we learn about the rise in uptake of this game specifically? And can we perhaps identify a tipping point where engagement around the game crossed a certain threshold, gaining momentum that was impossible to stop?

These were some of the questions that I set to answer earlier this week. At betaworks, we have a unique longitudinal view of mainstream media and social media streams. Our services, at varied scales, span across content from publishers and social networks, giving us the ability to analyze the attention given to events over time. Inspired by Zach Will’s analysis of the Flappy Bird phenomenon through scraped iTunes review data, I wanted to see what else we could learn about the massive adoption of this game, specifically through the lens of digital and social media streams.

My data shows two clear tipping points, where there was significant rise in user adoption of the game. The first, January 22nd, happened when the phrase ‘Flappy Bird’ started trending on Twitter across all major cities in the United States. It would continue trending for the next 6 days, driving increased visibility and further adoption. The second, on February 2nd, was the point in which media coverage of the game quadrupled on a daily basis. Most media outlets were clearly late in the game, covering the phenomenon only after it had topped all app store charts and was already a massive success.

We live in a networked world, where social streams drive massive attention spikes flocking from one piece of content to another. While it is a chaotic system, difficult to fully predict or control, there are early warning signals we can heed, as events unfold.

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This plot shows aggregate unique trending topics locations (in red) , versus unique media sources covering the game on a daily basis (in purple) versus the number of unique users sharing links related to game in social streams (in green).

First, a little explanation. The bar chart above highlights three signals over the period of the past month. All values on the y-axis are normalized to display percentage from the max:

  1. In red, we see the number of unique locations where Flappy Bird trended on Twitter. The higher the value, the more locations around the world where the game trended. Flappy Bird clearly started trending in cities around the world many days before media picked up the story.
  2. In blue, we see unique domains associated with Flappy Bird coverage. In this case, domains consist of anything from stories published by bits.blogs.nytimes.com to personal tumblr pages such as xerendipity.tumblr.com, where users were posting screenshots of the game to their feeds. We plot the daily number of unique domains over time as a way to gauge growing attention given to the game across a diverse set of publications and platforms.
  3. In green, we show the daily audience size. This is a daily count of unique users sharing links with content related to Flappy Bird in social streams. This is obviously sampled, and for the sake of this article, I will not get into the methodology right here. (happy to answer questions for anyone interested)

 

Media (& Social Media) Attention over Time

Throughout the first weeks of January we barely see any coverage of the game. The first story comes from ibtimes.co.uk on Jan. 24th. Described as the most frustrating game ever, it highlights a number of tweets where users complain about the game’s difficulty. This was the common sentiment seen on social networks:

The game’s difficultly was getting users to tweet out to their friends, and enough tweets were making the game a trend on Twitter (more on that below), which then powered a feedback loop: more tweets, more trends, and so on… Another early article comes from nativex.com, positively reviewing the game and its incredible adoption rate. At that point it had already hit the #1 spot in the iTunes App store for free apps.

As people continued to play it, and they kept tweeting out their frustration, their anger and their high scores. Every day there were a growing number of users posting screenshots to Tumblr blogs, Instagram and Twitter accounts. But on February 2nd, something changed. While the amount of media coverage (in purple) grows steadily, the number of users sharing content related to the game began to quadruple on a daily basis (in green).

We see significantly higher levels of engaged users on social networks responding to and sharing stories about the game starting on Feb 2nd.

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In order to better understand why this happened, we can break down the coverage by sources (plot below). At first we see the nativex.com piece (1/24-25) and people mainly posting screenshots of their scores using pic-twit.co as well as links to the game in the iTunes app store. One post on wpcentral.com details Dong’s tweet promise to release the game for the Windows Phone. A gaming blog detailed tips and cheats for the game, a subreddit had hundreds of comments, and my personal favorite, a super geeky blog post calculated Flappy Bird’s size given its velocity-time graph while in free fall. Throughout the period, there were lots and lots of people sharing screenshots of their scores.

On Jan 31st, VentureBeat published a story questioning the game’s success, and the following day published a listicle-style story on why the game is so successful. Over the next few days we see a rapid increase in Instagram shares of scores, likely due to the heightened media coverage, starting on Feb 2nd, when we see posts from Techcrunch  FastCompany and The Atlantic, as well as Huffington Post and entrepreneur.com. This explains the significantly larger population of users posting and responding to these articles.

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Apologies for the ugly annotated plot – didn’t have the time to make it purrty. Also – Thanks Google Javascript Viz Library!

 

Twitter Trending Topics across Geographical Regions

By tracking where the phrase ‘Flappy Bird’ appeared in Twitter’s trending topics lists across geographic locations, we can identify when and where the game became popular. We use a dispersion plot (below) to show these trends across geographic locations over time. The lower a city appears, the earlier Flappy Bird trended in that location. The x-axis denotes days. The first trend happened on January 22nd in Jackson Mississippi, spreading on to Las Vegas, Birmingham, Harrisburg and Baton Rouge. Within a day, it was trending across all major cities in the United States. On January 28th, the game started trending in the UK, then it spread to Canada, later east Asia, Europe and South America (larger screenshots of the dispersion plot can be found in my project site drop). When analyzing trends, this type of behavior isn’t uncommon. We tend to see trends spread within countries first, before hopping over to another country or region.

flappybird_dispersion1

Based on this diagram along with the previous charts, some closing thoughts:

  1. Media coverage came quite late, pretty much after the game had accrued immense popularity.
  2. Looks like the game reached a critical “tipping point” first within users in the United States, and only later in the UK and the rest of the world, as users were tweeting and posting screenshots of their scores.
  3. According to Zach’s analysis of app reviews, January 22nd seemed to have been a turning point for the game, which started generating 100 reviews per hour. This fits well with the Trends data above, which suggests that from the 22nd onwards, there was a significantly higher number of users engaging with the game, as it was trending all across the US.
  4. Teenagers. This is harder to prove since I don’t have all the necessary data. But many of the initial profiles posting about the game on Twitter, Instagram and Tumblr, were teenagers. Here are a few examples from Twitter. In a blog post written by a secondary school teacher in the UK, the author describes how he was introduced to the game by his students. I’d love to see an analysis of the earliest communities playing the game from someone who has the data!

Finally, it is important to highlight this post describing potential bot-related activity around the game’s earlier time in the app store. This is based on oddly worded reviews and sheer number of downloads for a suspiciously new game. If true, this points to a troubling phenomenon, where it is not meritocracy that gets your app ranked at the top of the charts, but rather how well you can manipulate the system. When launching web services, this is called Search Engine Optimization. When setting up social media accounts, it has become common to purchase followers or likes and boost the account’s presence.

So when launching an app, specifically a casual game, is it necessary to manipulate the app store scoring mechanisms in order to even have a chance at success?

I’d love to hear your thoughts or comments.

Gilad | @gilgul

2 Comments

  1. Good analysis, well done. reassuring to hear that the American teenager still dictates the trends. A cross-correlation plot of your time series would be interesting as well

    Reply
  2. To your last paragraph, I’d argue that perhaps “manipulating the system” is now just part of the game…buff.ly/1evvt0r

    Reply

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