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Influence et contagion
L'influence et la contagion dans la cyberculture
Curated by luiy
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How Videos Go #Viral part | / #metrics #SNA #contagion

How Videos Go #Viral part | / #metrics #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

This is a big post with a lot of variables and data. So let’s recap on what we’re saying overall. How do viral videos spread socially?

We can see there are 2 broad patterns of content diffusion. One model we call “spike” – the sudden ‘explosion’ of sharing activity – and the other we call “growth”, where popularity is a slower and steadier grower.  The metrics we’ve discussed, such as velocity, variability and social currency, provide a way to identify which kind of virality you’re looking:

 

 

http://www.facegroup.com/blog/how-videos-go-viral.html

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Research: The #Emotions that Make Marketing Campaigns Go #Viral | by @kristintynski | #contagion

Research: The #Emotions that Make Marketing Campaigns Go #Viral | by @kristintynski | #contagion | Influence et contagion | Scoop.it
Heat maps of viral content show what compels us to share.
luiy's insight:

Create content the strikes the correct emotional chords

 

While there is a good deal of evidence to suggest that strong emotions are key to viral sharing, there are a scarce few that indicate which emotions work best.

 

To this end, one of the best ways we’ve found to understand the emotional drivers of viral content is to map the emotions activated by some of the Internet’s most viral content.

 

In order to understand the best emotional drivers to use in the content we create, we looked at 30 of the top 100 images of the year from imgur.com as voted on Reddit.com (one of the top sharing sites in the world). We then surveyed 60 viewers to find out which emotions each image activated for them. We used Robert Plutchik’s comprehensive Wheel of Emotion as our categorization.

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I Flirt and Tweet. Follow Me at #Socialbot

I Flirt and Tweet. Follow Me at #Socialbot | Influence et contagion | Scoop.it
Socialbots are being designed to sway elections, to influence the stock market, even to flirt with people and one another.

Via Ron Sela
luiy's insight:

FROM the earliest days of the Internet, robotic programs, or bots, have been trying to pass themselves off as human. Chatbots greet users when they enter an online chat room, for example, or kick them out when they get obnoxious. More insidiously, spambots indiscriminately churn out e-mails advertising miracle stocks and unattended bank accounts in Nigeria. Bimbots deploy photos of gorgeous women to hawk work-from-home job ploys and illegal pharmaceuticals.

 

Now come socialbots. These automated charlatans are programmed to tweet and retweet. They have quirks, life histories and the gift of gab.

 

Many of them have built-in databases of current events, so they can piece together phrases that seem relevant to their target audience. They have sleep-wake cycles so their fakery is more convincing, making them less prone to repetitive patterns that flag them as mere programs. Some have even been souped up by so-called persona management software, which makes them seem more real by adding matching Facebook, Reddit or Foursquare accounts, giving them an online footprint over time as they amass friends and like-minded followers.

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Ron Sela's curator insight, August 13, 2013 7:01 AM

I Flirt and Tweet. Follow Me at Socialbot.

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The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience

The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience | Influence et contagion | Scoop.it
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. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it. 
luiy's insight:

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 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. 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.

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