Non-Equilibrium Social Science
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Non-Equilibrium Social Science
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Rescooped by NESS from Influence et contagion!

Competition among memes in a world with limited attention

Competition among memes in a world with limited attention | Non-Equilibrium Social Science |
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 NESS from Influence et contagion!

Study maps Twitter’s information ecosystem

Study maps Twitter’s information ecosystem | Non-Equilibrium Social Science |
New research outlines the six types of communities on the social network and what that means for communication

Via luiy
António F Fonseca's curator insight, March 1, 2014 7:59 AM

What community do you belong to?

Eli Levine's curator insight, March 1, 2014 4:24 PM

Indeed, we each live in our own world, not in the real world per se.


Some, however, have a more accurate understanding of the real world and are willing to acknowledge their shortcomings.


The others, who are less inclined to explore and are more focused on their own self-production, just happen to be known as conservative in our culture.  Hence, they area always hindered from perceiving the real world in the strictest of senses, and are not likely to change in light of new information received from the outside world.


Non-adapting humans will equal a dead and dying species.  It's a shame, though, that we can be dragged down by them for our lack of effective effort and action.




Think about it.

Fàtima Galan's curator insight, March 3, 2014 2:44 AM

"The topographical "maps" of these communities, generated by Pew using the data visualization tool NodeXL, aren’t just maps of relationships. They represent the channels of information in Twitter’s vast ecosystem, the roads and throughways, stoops and street corners in each topical neighborhood where users congregate and swap news and anecdotes."

Rescooped by NESS from Influence et contagion!

What Fuels the Most Influential Tweets?

What Fuels the Most Influential Tweets? | Non-Equilibrium Social Science |
The number of followers you have and the exact wording matter less than you think. What makes a difference is having the right message for the right people.

Via luiy
luiy's curator insight, February 22, 2014 7:58 AM

"Influence" doesn't necessarily mean what you think it does. In the age of the social-media celebrity, a glut of Twitter followers or particularly pugnacious sampling of pithy updates are often the hallmarks of an influencer. But new research suggests that influence is situational at best: as people compete for the attention of the broader online ecosystem, the relevance of your message to the existing conversation of those around you trumps any innate "power" a person may have.


.... According to co-author Vespignani, having millions of followers does not denote an important message. Rather, the messages with the most immediate relevance tend to have a higher probability of resonating within a certain network than others. Think of it as "survival of the fittest" for information: those tweets that capture the most attention, whether related to a major geopolitical or news event or a particular interest, are likely to persist longer. This competition sounds bad, but it's generally good for messages in general: thousands of tweets about Japan's 2011 earthquake or the ongoing conflict in Syria don't cancel each other out, but help refocus the attention of the wider Twitter audience on those issues, which in turn provides an added lift to individual messages over other off-topic ones.