Hi I’m Gilad

I love data, analysis and visualization. Chief data scientist at betaworks.

Seeing a Twitter #Hashtag Spread

#CheeringForTheYankeesIsLike is a hashtag created by @mattsly the morning of October 26th. He submitted the following snarky message – ‘Go Phillies. #CheeringForTheYankeesIsLike hoping investment bankers get really huge bonuses of at least 8 figures‘ – hoping to entertain his friends, and possibly get others to participate. Matt had 182 followers at the time, not sizeable by any means on Twitter. Little did he expect that some 9 hours later, 271 different users, most of whom have no connection to him whatsoever, would participate, posting around 500 messages in total.

How did this happen and what prompted this message to spread?


About an hour after Matt sent out his first message, one of his followers, @lizzieohreally, wrote the following message ‘@jaketapper? @abcdude? …Hoping someone w/ more Twitter than I can help popularize #CheeringForTheYankeesIsLike (via @mattsly)‘. Lizzie clearly understood that in order to get many others to play, she would have to get someone with a large set of followers to participate. Lizzie had only around 500 followers at the time, so posted this message in an attempt to seek @jaketapper or @abcdude’s attention.

Sure thing, some twenty minutes later, @abcdude see’s the message and adds his own variation to the meme: ‘#cheeringfortheyankeesislike pulling for Regina George in “Mean Girls.”‘ He enjoys it so much that he promptly posts another message and attaches the hashtag. @abcdude is a new york based correspondent for ABC news. He dubs himself a RedSox fan and a cosmic power broker. Not as cosmic as Lizzie had hoped, but still, he has some 7,000 followers, which could certainly help give the meme some traction. We see a small spike after @abcdude’s participation, and by now, some 3 hours after Matt sent the original message, there have been 34 different messages posted with this unique hashtag.

But it wasn’t until @jaketapper joined in that the conversation really took off. The hashtag came to Jake’s attention after @DetourJazz, whom he follows, participated. Jake reacted by posting:  ‘RT @DetourJazz: #cheeringfortheyankeesislike rooting for “Craterface” in Grease to beat Danny (via @Laura_Martin)’. He then added a new message that he posted to his followers. Jake is a senior White House correspondent for ABC news with over 30,000 followers. Before he took part in this meme, new posts appeared at a frequency of one every 5 minutes. Immediately after he joined, we see a sharp rise in participation, with multiple messages from a variety of users every minute.

Seeing it Spread

1. Graphing the Network – Every user who participated in the meme is represented by a gray circle (Matt, whom first started the meme, is shown in yellow). Edges represents the person who most likely influenced the other to first participate.

2. Seeing the Flow – in this applet, a user is represented by their twitter icon. As the timeline moves forward, each profile lights up when they post a new message with the hashtag. Tthe moment that @jaketapper chose to participate is evident – there’s a clear, sudden spike in participation after his profile picture lights up.

3. Seeing the distance – the following applet highlights the total social distance that this hashtag traveled between users. Each user is represented by a circle, the more influence a user has, the larger their circle is drawn. Edges in this example represent the social ties – when there’s a follower/friend relationship between two users, a line is placed between their representation on the screen. The first column includes only Matt who first used the hashtag. The second row consists of only those people he directly influenced to participate (his followers). While there are a total of 9 columns, it is crystal clear that the most important phase happened in the second and third column, when a core cluster of users chose to participate, and a mini tipping point was reached.

Parsing the Data

#CheeringForTheYankeesIsLike lasted for a total of 9 hours that day, activated 271 different users and included around 500 messages in total. From looking at this meme, it is clear that on Twitter, there’s great advantage to having many followers if one intends to spread a message. It is also clear that having the right followers is key. If it were not for @lizzieohreally who knew to actively pass the message onwards to heavy Twitter users, the meme would never have spread out the way it did. In order to come to these conclusions it was necessary for me to look at social ties in addition to the semantics of the messages posted.

I used the Twitter API to discover the follower/friend relationships between all users who participated in this meme. This is extremely important  data, especially when modeling  the flow of participation and influence within this hashtag. For example, lets look at a simple case where user B follows user A. If user A first participates and is followed by user B participating, user A is rewarded some number of influence points – this is assuming user B saw the hashtag posted by user A, and decided to participate. Additionally, if a user is retweeted or ‘@’ messaged they are rewarded some number of influence points. Real life situations can easily become complicated, as user B might also be following user C, who participated in the meme as well. Now how do we know if user B was influenced by user A or user C? Hard to tell, but we can build an influence model that takes these situations into account, which is exactly what I did.

Translating the semantics and social ties from the dataset into a visual language that made sense was key to helping me understand this hashtag experiment. I am a big fan of visualization as a means to parse large datasets, however dealing with social, implicit data is tricky, and extremely challenging to represent visually. But when done right, these representations can shine a whole new light and hopefully help us better understand some of the dynamics at play.

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