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Rescooped by Jean-Michel Livowsky from Influence et contagion!

How Videos Go #Viral part | / #metrics #SNA #contagion

How Videos Go #Viral part | / #metrics #SNA #contagion | Intelligence |

Via luiy
luiy's curator insight, September 26, 2014 9:38 AM

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:

Rescooped by Jean-Michel Livowsky from Influence et contagion!

Competition among #memes in a world with limited attention | #SNA #ABM #prediction

Competition among #memes in a world with limited attention | #SNA #ABM #prediction | Intelligence |
The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Via luiy
luiy's curator insight, February 22, 2014 8:06 AM

Here we outline a number of empirical findings that motivate both our question and the main assumptions behind our model. We then describe the proposed agent-based toy model of meme diffusion and compare its predictions with the empirical data. Finally we show that the social network structure and our finite attention are both key ingredients of the diffusion model, as their removal leads to results inconsistent with the empirical data.



Limited attention

We first explore the competition among memes. In particular, we test the hypothesis that the attention of a user is somewhat independent from the overall diversity of information discussed in a given period. Let us quantify the breadth of attention of a user through Shannon entropy S = −Σi f(i) log f(i) where f(i) is the proportion of tweets generated by the user about meme i. Given a user who has posted n messages, her entropy can be as small as 0, if all of her posts are about the same meme; or as large as log n if she has posted a message about each of n different memes. We can measure the diversity of the information available in the system analogously, defining f(i) as the proportion of tweets about meme i across all users. Note that these entropy-based measures are subject to the limits of our operational definition of a meme; finer or coarser definitions would yield different values.


John Caswell's curator insight, March 2, 2014 8:23 AM

Very intetesting! Attention spans!

Rescooped by Jean-Michel Livowsky from Influence et contagion!

The evolution of #memes on Facebook | #SNA #contagion

The evolution of #memes on Facebook | #SNA #contagion | Intelligence |

Via luiy
luiy's curator insight, June 22, 2014 10:36 AM

A meme is an idea that is readily transmitted from person to person. But we humans are not perfect transmitters. While sometimes we repeat the idea exactly, often we change the meme, either unintentionally, or to embellish or improve it. 


Take for example, the meme: 

“No one should die because they cannot afford health care, and no one should go broke because they get sick. If you agree, post this as your status for the rest of the day”. 


In September of 2009, over 470,000 Facebook users posted this exact statement as their status update. At some point someone created a variant by prepending "thinks that'' (which would follow the individual's name, e.g., “Sam thinks that no one…”), which was copied 60,000 times. The third most popular variant inserted "We are only as strong as the weakest among us'' in the middle. “The rest of the day” at one point (probably in the late evening hours) became “the next 24 hours”. Others abbreviated it to “24 hrs”, or extended it to “the rest of the week”.



Modeling memes as genes


So can memes really be modeled as genes? After all, Richard Dawkins originally coined the word "meme” to draw the analogy to genes when describing how ideas or messages replicate and evolve[1]. How would one test the hypothesis that memes undergo a process akin to biological evolution? First, tracing biological evolution is notoriously difficult because one must discern the lineage of specific genetic sequences through generations, without having the genetic sequence of many intermediate instances. But when studying Facebook memes, we have a very unique opportunity* to actually trace when copies and mutations occurred, and these are the two basic ingredients in the evolutionary process.

Rescooped by Jean-Michel Livowsky from e-Xploration!

Looking at Culture through a Big Data Lens I #patterns #memes

Looking at Culture through a Big Data Lens I #patterns #memes | Intelligence |
I’m excited about all the (possible) breakthroughs we see happening in cultural research.

Via luiy
luiy's curator insight, December 26, 2013 9:06 PM

Predicting by looking at narratives

Other Big Data cultural research examples will be presented tomorrow at a conference organized by the Dutch Meertens Institute “Patterns in narrative texts“. The data that will be discussed range from narrative journalistic texts to orally transmitted folktales. In the study of history, diachronic corpora can be mined to discover how historical events are reflected in language use. In folk narrative research, patterns of interest include the stability and variability of ‘narrative building blocks’ (motifs, memes) in oral transmission, and geographical dispersion of folk beliefs in the supernatural. Establishing links between narrative texts is a common factor in all this research.


One of the pieces of research that will be discussed is “Mining the Twentieth Century’s History from the TIME Magazine Corpus”. Mike Kestemont & Folgert Karsdorp are going to explain how to predict Times’s Person of the Year. In their research they have paid special attention to the intriguing interplay between this list of influential personalities and the manner in which they are discussed in the magazine’s own archive. They will have a lot to explain, looking at their top-10 list for 2013, since they’ve missed the person that has won this year, Pope Fransicus. But still the researchers have a hit-rate of more than 20%.