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Influence et contagion
L'influence et la contagion dans la cyberculture
Curated by luiy
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Determinants of #Meme Popularity | #influence #twitter

Determinants of #Meme Popularity | #influence  #twitter | Influence et contagion |

Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior and analytically show, using techniques from mathematical population genetics, that competition between memes for the limited resource of user attention leads to a type of self-organized criticality, with heavy-tailed distributions of meme popularity: a few memes "go viral" but the majority become only moderately popular. The time-dependent solutions of the model are shown to fit empirical micro-blogging data on hashtag usage, and to predict novel scaling features of the data. The presented framework, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.


Determinants of Meme Popularity
James P. Gleeson, Kevin P. O'Sullivan, Raquel A. Baños, Yamir Moreno

Via Complexity Digest
luiy's insight:

In summary, despite its simplicity, the model matches the empirical popularity distribution of hashtags on Twitter remarkably well; this is consistent with random-copying models of human decision-making [28] where the quality of the product—here, the “interestingness” of the meme—is less important than the social influence of peers’ decisions[29]. The generalization of the model (as shown in the SM) to incorporate (i) heterogeneous user activity rates and (ii) a joint distribution p jk of the number of users followed j and the number of followers k, remains analytically tractable and confirms the robustness of our main finding: that competition between memes for the limited resource of user attention induces criticality in the vanis hing-innovation limit, giving power-law popularity distributions and epochs of linear-in-time popularity growth. We believe that theanalytical results and potential for fast fitting to data will render this a useful null model for further investigations of the entangled effects of memory, network structure, and competition on information spread through social networks [30].

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#Clustering #memes in social media streams | #algorithms #sna

#Clustering #memes in social media streams | #algorithms #sna | Influence et contagion |
luiy's insight:

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter.


A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion.


The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets.

The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics.

Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

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Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki

Evolution of Online User Behavior During a Social Upheaval | #datascience #diregeziparki | Influence et contagion |
luiy's insight:

Social media represent powerful tools of mass communication and information diffusion. They played a pivotal role during recent social uprisings and political mobilizations across the world. Here we present a study of the Gezi Park movement in Turkey through the lens of Twitter. We analyze over 2.3 million tweets produced during the 25 days of protest occurred between May and June 2013. We first characterize the spatio-temporal nature of the conversation about the Gezi Park demonstrations, showing that similarity in trends of discussion mirrors geographic cues. We then describe the characteristics of the users involved in this conversation and what roles they played. We study how roles and individual influence evolved during the period of the upheaval. This analysis reveals that the conversation becomes more democratic as events unfold, with a redistribution of influence over time in the user population. We conclude by observing how the online and offline worlds are tightly intertwined, showing that exogenous events, such as political speeches or police actions, affect social media conversations and trigger changes in individual behavior.

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The six types of Twitter conversations | #patterns #archetypes

The six types of Twitter conversations | #patterns #archetypes | Influence et contagion |
Have you ever wondered what a Twitter conversation looks like from 10,000 feet?

Via Pierre Levy
luiy's insight:

Have you ever wondered what a Twitter conversation looks like from 10,000 feet? A new report from the Pew Research Center, in association with the Social Media Research Foundation, provides an aerial view of the social media network. By analyzing many thousands of Twitter conversations, we identified six different conversational archetypes. Our infographic describes each type of conversation network and an explanation of how it is shaped by the topic being discussed and the people driving the conversation.

Kamian's curator insight, March 26, 2014 11:57 PM

Me encantan estas clasificaciones, ayudan a comprender y diferenciar rapidamente las diferentes dinámicas sociales y arquitecturas que se van conformando en las redes sociales.

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

Competition among #memes in a world with limited attention | #SNA #ABM #prediction | Influence et contagion |
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.
luiy's insight:

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!

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Study maps Twitter’s information ecosystem | #clusters #memes #SNA

Study maps Twitter’s information ecosystem | #clusters #memes #SNA | Influence et contagion |
New research outlines the six types of communities on the social network and what that means for communication
luiy's insight:

Fil Menczer, a professor at the University of Indiana Bloomington School of Informatics and Computing, has researched the potential applications of this type of analysis for years. Menczer’s research touches on every aspect of Twitter’s role as a mirror for human communities, like examining the relationship between social data and the stock market, the spread of infectious diseases and how political campaigns manipulate data to spread misleading information. In a 2012 paper on the spread of memes on Twitter, Menczer and his team sought to demystify how information spreads on unrelated topics, yielding similar network structures to those uncovered by Pew.




One of the major lessons of network analysis, both Pew and Menczer emphasize, is that the Twitter commons hasn’t necessarily made society as democratic as techno-utopians would have you believe. Twitter isn’t a wide-open space, free of boundaries or obstacles: It’s a "mirror," as Menczer says, for the social structures of the real world.

“One of the presumptions about the rise of social media is that it’s changed everything,” says Himelboim. “In fact, if you look at the broadcast networks and brand clusters (two archetypes described by Pew), big, important and powerful institutions that wield tremendous influence offline still do on the Internet. This is really a reality check against those louder voices who claim the world has somehow been transformed."


“It makes you wonder about polarization in political discourse: Is this something that social media is responsible for?” asks Menczer. “Is more polarization easier because of social media, or are we observing what was already there with new technology? Or, even simpler: Would our discourse be better if Twitter and Facebook just didn’t exist?” 



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."

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#contagion : Social Contagion and Cascade Behaviors on Twitter

It has been found in a variety of face-to-face networks that diffusion of information, behaviors and sentiments extend up to two to four degrees of distance from the original source.
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Rescooped by luiy from Science News!

How Could Twitter Influence Science (And Why Scientists Are on Board) - Forbes

The recent revelation that Twitter and social media can influence science is good news for people who want to understand the broader impacts of a more social world. But does the evidence stack up?

Via Sakis Koukouvis
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The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Influence et contagion |
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 ...
luiy's insight:

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?

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Rescooped by luiy from War, Cyberwar, Geopolitics!

US #military studied how to #influence Twitter users in #Darpa-funded research

US #military studied how to #influence Twitter users in #Darpa-funded research | Influence et contagion |
Defense Department spent millions researching users, including studies on Occupy and Middle East residents, and how to better spread propaganda

Via Pierre Levy
luiy's insight:

The activities of users of Twitter and other social media services were recorded and analysed as part of a major project funded by the US military, in a program that covers ground similar to Facebook’s controversial experiment into how to control emotions by manipulating news feeds.


Research funded directly or indirectly by the US Department of Defense’s military research department, known as Darpa, has involved users of some of the internet’s largest destinations, including Facebook, Twitter, Pinterest and Kickstarter, for studies of social connections and how messages spread.

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How to Burst the "Filter Bubble" that Protects Us from Opposing Views | #algorithms #homophily

How to Burst the "Filter Bubble" that Protects Us from Opposing Views | #algorithms #homophily | Influence et contagion |
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.
luiy's insight:

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.

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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 | Influence et contagion |
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.
luiy's insight:

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.

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What Fuels the Most Influential Tweets? | #influence #SNA #datascience

What Fuels the Most Influential Tweets? | #influence #SNA #datascience | Influence et contagion |
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.
luiy's insight:

"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.

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What Would Ashton Do—and Does It Matter? - Harvard Business Review

What Would Ashton Do—and Does It Matter? - Harvard Business Review | Influence et contagion |
Whenever I give a lecture, I conduct a simple experiment. First I ask the members of the audience to raise a hand if they follow the actor Ashton Kutcher on Twitter. Usually most people’s hands go up—no big surprise. For several years Kutcher has been aggressively amassing followers, even renting billboards urging people to follow “aplusk,” his Twitter handle. In 2009 he became the first user to acquire 10 million followers; by early 2013 the total was 13.7 million. Kutcher would seem the very definition of a social media “influencer.” But then I ask the audience another question: How many have ever done something because Kutcher suggested it? Most often nobody raises a hand. So I have to wonder: If Kutcher is the quintessential influencer but no one does what he suggests, in what way is he influential?
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Rescooped by luiy from Social Media and its influence!

Twitter hacked, at least 250,000 users affected: what you can do to protect yourself

Twitter hacked, at least 250,000 users affected: what you can do to protect yourself | Influence et contagion |
Twitter is the latest web property to admit that intruders seem to have been wandering around its network for some time.

Paul Ducklin investigates and offers some advice on what to do next...

Via Gust MEES
Gust MEES's curator insight, February 1, 2013 8:51 PM

                           ===> BEWARE the MALWARE!!! <===

Gust MEES's curator insight, February 1, 2013 8:55 PM

                       ===> BEWARE of the MALWARE!!! <===

Rescooped by luiy from Social Media Content Curation!

Social Media Influence Measurement Tools: Klout vs PeerIndex

Social Media Influence Measurement Tools: Klout vs PeerIndex | Influence et contagion |

For the purposes of this test, I took the 50 accounts from the list of independent travel influencers.
I then created a spreadsheet in Excel and imported the following data for all 50 people on the list:
- Klout Score
- PeerIndex Score
- Number of Tweets
- Number of Followers
- Number of people following
- Ratio of followers to following
- Number of Twitter lists

I then plotted the data for Klout and PeerIndex scores vs the various metrics and put a trendline on the data with a corresponding R^2 value. A value of 1.00 would be a perfect correlation...
[read full story]

Via Giuseppe Mauriello
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