Two Years Later, Urbahn Reflects On The Impact Of His bin Laden Tweet

It has been two years since U.S. forces captured Osama bin Laden. On May 1, 2011, less than an hour before President Obama’s official announcement, people on Twitter were speculating about the subject of the announcement. At 10:24, Keith Urbahn, then-Chief of Staff to Donald Rumsfeld, shifted the entire Twitter conversation when he tweeted, “So I’m told by a reputable person they have killed Osama Bin Laden. Hot damn.”

Within a minute, more than 80 people retweeted the message. Concurrent with Urbahn’s message, conversations shifted to near-exclusive focus on bin Laden.

In the days following this event, our research team investigated the impact and reach of the Tweet in “Breaking Bin Laden: Visualizing the Power of a Single Tweet.” Our team analyzed 14.8 million public Tweets and links posted between news about an unplanned presidential address (9:46 p.m. EST) and Obama’s address (11:30 p.m. EST) to see how dynamics of rumor creation played out during those critical hours on Twitter.

We followed up with Urbahn, who now heads up Javelin, a media and digital agency in Washington D.C., to gain his perspective on that moment and how it influenced his use of the platform.

“The bin Laden tweet gave me a new respect for the power of Twitter. When something has the potential to be retweeted a few thousand times, it gives you pause before you hit the ‘tweet’ button.  Anyone who is in the right place at the right time can be a small part of a news story – or advancing it, even inadvertently,” Urbahn wrote in an email exchange.

“Two years ago, that happened to me and some guy awoken by the sound of helicopters over Abottabad.  You also don’t need to break epic once-in-a-century news stories to have a presence and build a following. Good, creative content – especially that which has a sense of humor and doesn’t take itself too seriously – wins out and tends to break through the din.”

Our research noted that Keith was not first to speculate that the address was related to bin Laden, nor did he have a particularly large presence on Twitter at that time. But the right network effects came into play, and enabled his post to generate enough trust amongst his followers, their followers, and so on.

 

For questions, thoughts or comments, please get in touch with SocialFlow on Twitter @SocialFlow. Many thanks to Keith Urbahn for his contribution to this post, find him on Twitter @keithurbahn.

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つぶやいてください: Twitter’s Networks in Japan

This past summer, Japan witnessed strong political response to the local energy crisis. In March 2011, the Tohoku Earthquake devastated the nation, and since then a primary topic of conversation has been whether Japan should reinstate its nuclear energy program. As many well know, Twitter became a crucial communication technology used widely during the crisis.

Twitter messages and retweets emerging after the 2011 Tohoku earthquake

While Japan has been known for its own local social media platforms (Nico Nico Douga for video sharing, Mixi for social networking and blogging, and 2channel for subculture), in the past few years Twitter has grown immensely in popularity and adoption within the country, with thousands of Japanese “mumbling” (つぶやき) daily. From books chronicling the platform’s spread to television dramas about using the service, Twitter has become a social media phenomenon within the country.

Unfortunately, we don’t know too much about how Japanese individuals, groups, and companies use Twitter. For one, we know that these users are very active: Japan has set records during New Year’s celebrations for volume of tweets per second produced. Conversely, last year, Twitter’s engineering department released a report that showed that – compared to other major cities around the world – Tokyo tweets just as much during the night but less so during the day.


A report by Twitter’s engineering department suggests that Tokyo tweets much less at night than other cities.

To see how true the “Tokyo only tweets at night” hypothesis was, we first looked at language use on Twitter over the period of one week. As you can see below, most languages – except Japanese – exhibit diurnal peaks (meaning one peak in the morning and one peak in the evening). Instead, Japanese has a much less pronounced bump in the morning, sloping up to high activity in the evening. It seems likely that social norms, especially around using the internet at work, may shape these behaviors.


One week of languages on Twitter.

We then randomly sampled users from both New York City and Tokyo to compare them. Below, you can see graphs for a few days of these users’ volume of tweets. Though we would expect to see diurnal peaks, we see that both cities exhibit more tweeting at night. Still, it remains clear that Japanese users tend to use Twitter more at night, and these behaviors of course greatly impact how information can travel across the various networks that make up Japanese Twitter.


Three days of activity from 5000 users each from Tokyo and New York City.

When looking at possible differences in other data in the comparison between Japanese and American Twitter users, we did not find much dissimilarity. For example, looking at followers, American users had a slightly higher frequency of accounts with 0 or 1 followers compared to Japanese users, but both samples showed long-tail trends in their follower counts, ending in the hundreds of thousands of followers.

One distinction we did find was in how users tweet. While it’s to be expected given phone culture in Japan, Japanese users in Tokyo also exhibit a higher percentage of mobile use compared to their New York cohort. Web use is much higher in New York City compared to Tokyo, and Japanese users also illustrate a much more diverse set of third-party platforms.

Percentage of platform use for users in Tokyo and New York City.

Generally, we also saw that many Japanese businesses are unlikely to use Twitter to reach their audiences. A study by Adam Acar shows, for example, that uptake of Twitter by top Japanese companies has only reached 60%, whereas 95% of the top 100 American companies are active on the platform. Also, while 86% of American brands tweeted during the study’s analysis, only 41% of Japanese brands did the same. Still, one large Japanese retailer we examined, Rakuten (@RakutenJP), has shown that occasional Twitter campaigns do lead to greatly – and quickly – increased follower counts.

Rakuten follower counts starting summer 2012 show dramatic increases in followers after campaigns.

Beyond its popular use, Twitter has now established itself as a public sphere, a forum for communication where ordinary citizens (as well as journalists, politicians, and celebrities) can discuss and share information about critical civic topics. Over the summer, we decided to look at how Japanese Twitter users were speaking about and spreading awareness of these crucial issues: in particular, we investigated the conversations and social networks around the antinuclear protests across Japan. From July 10 to July 28, 2012, we looked at tweets containing the word “protest” (抗議) to see what kinds of discussions, markers, and communities would turn up. We knew that each Friday a scheduled anti-nuclear protest was held, and we wanted to see if we could map individuals who chatted about them.


Network graph of topics that emerged from the protest dataset.

The colors in the nework graph represent the topical clusters that emerge from the data. The largest cluster (9.52% of the nodes) revolves around popular journalist Yasumi Iwakami (岩上安身) and his journalistic enterprise, the Independent Web Journal (http://iwj.co.jp/). Iwakami-san was a popular voice in the criticism around the antinuclear and radiation issues in the country. The second-largest cluster (8.94%) revolved around the activist hashtag #紫陽花革命 (“hydrangea revolution”) and involved other key hashtags related to the antinuclear protest, such as #脱原発 (“abandoning nuclear power generation”) and #再稼働反対 (“reoperation opposition,” referring to the reopening of shut-down nuclear plants).

Other related clusters emerged: many people mentioned NHK’s coverage of the protests (5.48%), a large community formed around a general discussion of the protest demonstrations (4.64%; #原発, “nuclear power plant”; #デモ, “demonstration”; #genpatsu, a romanized version of 原発; etc.), and in particular the massive protest in front of the Prime Minister’s residence (4.62% with #首相官邸前抗議, “protest in front of prime minister’s official residence”; 3.7% with #官邸前, “in front of official residence”; 4.02% with “Ustream,” which livestreamed the residence protest) . One meme also appeared (in the 4.02% just mentioned), where @monjukun, a mascot that became well-known for tweeting in easy-to-understand terms against the government’s nuclear policies, became an important individual in the debates.


Close-up of core topical areas from the protest dataset.

However, since the keyword 抗議 (“protest”) is fairly common across any political movement in Japan, we also found other emergent events and conversations. For instance, the Trans-Pacific Partnership Agreement, a copyright agreement between Japan and the US known colloquially as TPP, occupied 5.36% of the discussion. Even more, a cluster emerged around the hashtag #抗議 (“protest”), which was used for a variety of activist purposes on Twitter. In particular, the hashtag was commonly paired with “FAX,” relating to a fair number of calls to spread word about particular protests via phone and fax. “FAX” was especially pertinent in the middle of the dataset, as many Twitter users spoke about protests regarding the suicide of a schoolboy in the city of 大津 (Otsu). Finally, another general topic emerged around #政治 (“politics”), #seiji (the romanized version of 政治), #マスコミ (“mass communication” or “the press”), and #民主党 (the Japanese Democratic Party).

Since we looked at tweets over a long time period, the graphs below illustrate how various topics emerged over the course of the 18 days. The weekly spikes – particularly around the power plant hashtags – match up with the weekly protests held every Friday. What is perhaps most surprising about these spikes is the lack of a conversational spike around July 16th, when tens of thousands of Japanese protestors, in a special day of protest, congregated in Yoyogi Park in Central Tokyo, shown below. The lack of social media coverage of this event suggests that the “tweeting at night” behavior common to Japanese users may have actually affected Twitter’s potential to bring attention to this gathering.


Antinuclear protest in Yoyogi Park.

Regarding the social network underlying these conversations, the dataset covers a broad range of users. It is important to note that most of the users who participated in protest conversations were Japanese-speaking users, and the social networks reflect Japanese users connecting to other Japanese. The interesting aspect of the protest networks is that, generally, the network covers a wide range of participants, generating discussion amongst the core groups involved (ie., journalists, academics, and news critics [right side of graph below]) but also spanning to other strong Japanese Twitter networks, like very active netizens and otaku (media fans) [left side of graph below].


Sample of 10,000 users from “protest” social network.

However, if we look at the network of users that participated in antinuclear protest discussions as a subset of the graph above, these networks were very dense. In other words, the network of users talking about antinuclear issues acted as a sort of echo chamber, where tweet volume might have been high around the topic but where these discussions didn’t spread to other Twitter networks in Japan.


Users who mentioned #genpatsu or #原発 hashtags in their tweets.

Above, you can see that users who tweeted about the nuclear power plant (genpatsu) form a very dense network. While the discussion was vibrant amongst these users, the potential for information to move across networks and spread widely throughout Twitter was not strong. The image literally illustrates a bubble of conversation.

Overall, Japanese Twitter user continues to grow, so we will see many interesting opportunities for Twitter to act as a platform for discussion and marketing in the coming months. Anyone considering to use Twitter for a campaign should consider that particular discussions might become caught in a dense network, so they should strategize about expanding across a diverse set of users. Also, information may travel better at night, when users are more active. Finally, mobile still remains strong in Japan, though the ever-increasing use of smart phones (instead of traditional Japanese “keitai” phones) may challenge companies to design projects that bridge these users.

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Challenge: Choose What Matt & Meng Will Say At #LeanUXNYC

Your challenge: Today Matt Moran, VP of Product Development and Meng He, Senior UX & Design Lead will present at #LeanUXNYC at 4:20PM.  With the audience and attention changing up until the minute they present, we need your vote to optimize their presentation so you can hear exactly what you’re interested in!

You have 4 hours to choose your favorite topics. Tweet your vote using the topic hashtags at @SocialFlow. Go!

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Tweet #420talk1 for: Customer development vs product development #LeanUXNYC
Tweet #420talk2 for:  How to measure what’s important if you’re not a data scientist #LeanUXNYC
Tweet #420talk3 for:  Everything they taught me in school is wrong #LeanUXNYC
Tweet #420talk4 for:  What is UX and Product Development waste? #LeanUXNYC
Tweet #420talk5 for:  Challenging assumptions #LeanUXNYC
Tweet #420talk6 for:  Lean—the silo-breaker #LeanUXNYC
Tweet #420talk7 for:  Daily habits to get lean #LeanUXNYC
Tweet #420talk8 for:  Heros, rockstars, and ninjas suck #LeanUXNYC
Tweet #420talk9 for:  What are your obstacles to lean UX? Ask us anything. #LeanUXNYC
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SocialFlow Selected by AlwaysOn As One Of The OnMedia 100 Companies To Watch

We’d like to share some good news!

SocialFlow was chosen by AlwaysOn as one of the 2013 OnMedia Companies to Watch

Inclusion in the OnMedia Companies to Watch signifies leadership amongst its peers and game-changing approaches and technologies that are likely to disrupt existing markets and entrenched players. 

SocialFlow was specially selected by the AlwaysOn editorial team and industry experts spanning the globe based on a set of five criteria: innovation, market potential, commercialization, stakeholder value, and media buzz.

This award is a testament to the dedicated and hardworking SocialFlow team. We are honored by the recognition by AlwaysOn that SocialFlow is considered a company to watch, and we look forward to continuing to help agencies, brands and publishers yield the most from social media.

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Harlem Shake: anatomy of a viral meme

This entry is cross-posted on Huffington Post.

If you still have not heard of the Harlem Shake you must be living in a cave. Much has been written about the rapid and global spread of this catchy internet meme, yet little is understood about how it spread. In the following post, we look at the meme’s emergence through the lens of Twitter data. A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. What can we learn from data? Who were the initial communities behind this mega-trend? Who were some of the trend-setters, and what did the Jamaica techno-DJ scene have to do with this?

3.-harlemshake_feb_7_8

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The Harlem Shake is a dance style born in New York City more than 30 years ago: “During halftime at street ball games held in Rucker Park, a skinny man known in the neighborhood as Al. B. would entertain the crowd with his own brand of moves, a dance that around Harlem became known as ‘The Al. B.’” Though it started in 1981, the Harlem shake became mainstream in 2001 when G. Dep featured the dance in his music video “Let’s Get It”.

While mining Twitter data, references to Harlem Shake (the original dance) were seen quite often prior to it becoming a popular meme. For example, users would post Tweets referencing the dance in the following manner:

There are numerous examples of Tweets using the phrase in a similar context (here are a few examples), many of them using the * character as an emphasis. Kimberly Ellis, a Scholar of American and Africana Studies, claims that this type of language is  being referenced via cultural memory. And users are very dramatic, hence they place “action items” in tweets:

When someone tweets, “I just passed my final exams! *harlem shakes*,” it’s the equivalent of saying “I just passed my final exams! Look at me dancing!” As you can see, the Harlem Shake of cultural memory is SO energetic, recalling the visual in a tweet makes it all the more hysterical and another shared, cultural moment for African Americans on Twitter.

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While Bauuer’s now infamous track was released on Diplo’s Mad Decent label back in August 2012 (posted to YouTube on August 23rd 2012), it only accrued minor visibility for the first few months. Then February hit, and something changed.

The timeline below highlights the very first days as the meme was taking off. In blue, we see references to the 1980′s dance *harlem shakes*. Note the diurnal pattern, rising and falling steadily on a daily basis. In contrast, the green curve represents Tweets that use the phrase ‘The Harlem Shake’, many of them linking to one of the first three versions of the meme on YouTube.

1.-comparison_feb_4_to_11

On February 2nd, The Sunny Coast Skate (TSCS) group establish the form of the meme in a YouTube video they upload. On the 5th, PHL_On_NAN posts a remix (v2), gaining 300,000 views within 24 hours, and prompting further parodies shortly after. On Feb. 7th, YouTuber hiimrawn uploaded a version titled “Harlem Shake v3 (office edition)” featuring the staff of online video production company Maker Studios. The video becomes is a hit, amassing more than 7.4 million views over the following week, and inspiring a number of contributions from well-known Internet companies, including BuzzFeed, CollegeHumor, Vimeo and Facebook.

In a video interview, Vernon Shaw, Channel Development Coordinator at Maker Studios (produced v3), claims that he spotted the first two versions on Reddit. It was evident that a form was emerging, and after v2 accrued 100k views, it was clear to him that this was the “pre-viral” stage. Vernon attributes Reddit for being first to highlight the remixes, claiming that “you can tell when a trend is about to start by catching it on Reddit first… a day or two ahead of Facebook”.

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Here’s a graph that shows retweets during the first week, as the meme was being established. We can identify dominant profiles who helped make the videos visible on Twitter, key information brokers. Each node represents a Twitter user, and the larger a node, the more Retweets that user generated when posting to the meme. The lighter colored participated earlier, hence we see @baauer, @dipio and @maddecent very early on, posting to Twitter and accruing Retweets. On the bottom right region, we identify influential YouTubers who were key to passing on the meme, such as @kingsleyyy, @KSIOlajidebt, @ConnorFranta, and @Jenna_Marbles. Note the general size of these profiles versus @StephenAtHome (Colbert) or even @YouTube. These influential YouTubers clearly played a prominent role in generating buzz across Twitter, much more than significantly larger accounts such as Stephen Colbert’s or YouTube itself.

2.-HarlemShake_rts_1wk

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Next, instead of mapping out Retweets, we look at the social connections amongst users who were posting to the meme. This gives us the ability to identify the underlying communities engaging with the meme at a very early stage. In the following graph each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7th or 8th to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters – regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common.

3.-harlemshake_feb_7_8

One of the most dense clusters includes @baauer, @diplo, @maddecent and other DJs and musicians. They are clearly a core community who were posting the meme early on. We identified this clearly in the previous Retweet graph. In red and green (top, right) we see regions of the graph highlighting various YouTube communities. These are users whose dominant web identity is their YouTube page. While many of them have Twitter handles, they all link to their YouTube page as a primary identity, while many describe themselves as ‘YouTubers’. We see a dense Brazillian user community (right), Jamaican rappers (top center-left), cape town (bottom) and users from Paris, France (bottom center-left). In the center, there are accounts such as BroBible and theBERRY/theCHIVE who were one of the first new-media outlets to identify the meme as interesting.

The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There’s very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups. 

If we run a similar analysis on the following two days (Feb 9th and 10th) we see different communities emerge, and a much more tightly knit graph structure:

4.-harlem_shake_friendships_9_10

While the same dense cluster of musicians and DJs (in turquoise) still exists, there are substantially more self-identified YouTubers both across the US and the UK. At the same time there’s a significant gamer / machinima cluster that’s also participating, as well as a growing Jamaican contingent, and quite a few dutch profiles (purple – left). Additionally, we see various celebrity and media accounts who caught on to the meme – @jimmyfallon, @mashable and @huffingtonpost.

By capturing the two snapshots, we can also make sense of the evolution of the meme as it becomes more and more visible. At first, loosely connected communities separately humored by the videos. Within days, we see major media outlets jump on board, and a much more intertwined landscape. We see different regions in the world light up, and identify communities of important YouTube enthusiasts who effectively get this content to spread.

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In this case we see a clear network of influential YouTubers across the US and the UK combined with a dense cluster of musicians and DJs who helped make this meme incredibly visible. We also see how it very quickly spread around the world, with dense contingents in Jamaica, South Africa, Brazil, France and the Netherlands. By comparing two snapshots in time, we literally see the difference between an emergent trend amongst loosely connected interest-based communities, to a dense more-connected cluster where digital-media outlets do significant amplification.

Memes have become a sort of distributed mass spectacle. Culture is being created, remixed and reinforced within social networks, and memes are becoming a mechanism that both capture people’s attention, and define what is “cool” or “trendy”. We see more and more companies and brands try to associate themselves with certain memes, as a way to maintain a connection with their audience, gain the cool factor. Pepsi did this with the Harlem Shake and saw an incredibly positive response. As we get better at identifying these trends and trend-setting communities early on, the pressure to participate will rise.

As social networks become globally-intertwined, we’re witnessing a growing number of memes conquer the world at large. These moments are critical points in time, where there are significant levels of attention given towards a specific entity – be it a joke, funny video or a political topic. Piecing together data from social networks can help us identify critical points in time, as well as the underlying communities and trendsetters for the humor-based memes, or the agenda setters for politically-slanted ones.

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The analysis is based on 1.9 million Tweets collected between February 1st and 16th, all referencing variations of the phrase ‘harlem shake’.

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