Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Rescooped by Ashish Umre from Influence et contagion!

The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Social Foraging |
Before I wrote this article, I went through two stages. In the first stage, I cruised the academic journals for interesting papers. Once I found a ...

Via luiy
luiy's curator insight, December 18, 2014 9:32 AM

Can the pattern of neurons firing in my brain predict how much this article will be retweeted on twitter?


A recent study conducted by Emily Falk, Matthew Lieberman, and colleagues gets us closer to answering these important questions. The researchers recruited undergraduate participants and randomly assigned them to two groups: the “interns” and the “producers.” The 20 interns were asked to view ideas for television pilots and provide recommendations to the 79 producers about which shows should be considered for further development and production. All of the interns had their brains scanned by fMRI while they viewed the videos, and they were then videotaped while they discussed the merits of each pilot show idea. The producers rated which ideas they would like to further recommend. How was neural activity related to the spread of ideas?

Rescooped by Ashish Umre from Influence et contagion!

Mapping Twitter Topic Networks: From Polarized Crowds to Community #Clusters | #politics #SNA #influence

Mapping Twitter Topic Networks: From Polarized Crowds to Community #Clusters | #politics #SNA #influence | Social Foraging |
People connect to form groups on Twitter for a variety of purposes. The networks they create have identifiable contours that are shaped by the topic being discussed, the information and influencers driving the conversation, and the social network structures of the participants.

Via luiy
luiy's curator insight, March 14, 2014 8:32 AM

Polarized Crowds: Political conversations on Twitter

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation.


Conversational archetypes on Twitter

The Polarized Crowd network structure is only one of several different ways that crowds and conversations can take shape on Twitter. There are at least six distinctive structures of social media crowds which form depending on the subject being discussed, the information sources being cited, the social networks of the people talking about the subject, and the leaders of the conversation. Each has a different social structure and shape: divided, unified, fragmented, clustered, and inward and outward hub and spokes.

After an analysis of many thousands of Twitter maps, we found six different kinds of network crowds.

Scooped by Ashish Umre!

Leveraging Social Media to Help During Emergencies

Leveraging Social Media to Help During Emergencies | Social Foraging |

Twitter is being used increasingly by people who find themselves in the middle of a disaster to report what’s happening, who needs help, and the extent of damage. But when a disaster strikes, the volume of tweets can be overwhelming for anyone trying to monitor them.


The Commonwealth Scientific and Industrial Research Organization(CSIRO), Australia’s science agency, has come up with a systematic way of sifting through the 140-character messages and feeding important details to crisis coordination centers, which in Australia organize assistance from government agencies.


“Twitter provides a new source of data from which crisis coordinators can obtain awareness about developing situations,” says Mark Cameron, the project’s leader.


Cameron, along with researchers Andrew Lampert, Bella Robinson, and Jie Yin, wrote “Using Social Media to Enhance Emergency Situation Awareness,” published in the November/December issue of IEEE Intelligent Systems magazine.

Tannah Gravelis's curator insight, August 22, 2014 5:13 AM

This article looks into the way in which social media can be utilised by governments to enhance the way in which it can serves communities - in this instance, an emergency situation. It's a nice article that shows how governments are beginning to realise the potential this new medium has to assists in situations where instant communication is a necessity.


Rank = 4

Rescooped by Ashish Umre from Influence et contagion!

How to Burst the "Filter Bubble" that Protects Us from Opposing Views

How to Burst the "Filter Bubble" that Protects Us from Opposing Views | Social Foraging |
Computer scientists have discovered a way to number-crunch an individual’s own preferences to recommend content from others with opposing views. The goal? To burst the “filter bubble” that surrounds us with people we like and content that we agree with.

Via luiy
luiy's curator insight, April 11, 2014 1:19 PM

The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world.


Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm.


This is an insidious problem. Much social research shows that people prefer to receive information that they agree with instead of information that challenges their beliefs. This problem is compounded when social networks recommend content based on what users already like and on what people similar to them also like.


This is the filter bubble—being surrounded only by people you like and content that you agree with.


And the danger is that it can polarise populations creating potentially harmful divisions in society.

Rescooped by Ashish Umre from Influence et contagion!

Competition among #memes in a world with limited attention

Competition among #memes in a world with limited attention | Social Foraging |
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!