Influence et contagion
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Influence et contagion
L'influence et la contagion dans la cyberculture
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GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience

GALLERY SocioPatterns project. SocioPatterns.org | #SNA #datascience | Influence et contagion | Scoop.it
A gallery that offers a collection of visualizations, pictures, movies and other media created and/or recorded in the context of the SocioPatterns project.
luiy's insight:

Dynamical Contact Patterns in a Primary School

 

This movie represents the dynamical contacts network measured during one day of activity in a primary school. Nodes represent individuals, and edges indicate face-to-face contacts. Every frame shows the contact network over a time window of 20 minutes. Nodes are arranged in groups that correspond to the school classes, with the teacher node at the center. Nodes are color-coded according to the grade  and teachers are shown in black. This movie is included in the supplementary information of our PLoS ONE paper. The network visualization was created by Alain Barrat and André Panisson using Gephi. The cumulative social network of interaction is available from the corresponding dataset page.

 

- See more at: http://www.sociopatterns.org/gallery/#sthash.ae3X56fs.dpuf

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Can #Ebola Be Stopped By Treating It Like A Terrorist Network? | #algorithms #health

Can #Ebola Be Stopped By Treating It Like A Terrorist Network? | #algorithms #health | Influence et contagion | Scoop.it
A Florida defense contractor is using the data mining tools of counterterrorism to take aim at Ebola.
luiy's insight:

Six months after its latest resurgence, the Ebola virus shows no signs of letting up. "We desperately need new strategies adapted to this reality," said Dr. Joanne Liu, international president ofDoctors Without Borders in a grim statement last week. One hope is that data, which can spread faster than disease, could give humans a technological leg up on the spread of the epidemic. The problem with this data is that it's massive and often unstructured.

 

Can scientists and medical professionals make sense of the mess in time for it to make a difference?

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Detecting #Emotional #Contagion in Massive Social #Networks

Detecting #Emotional #Contagion in Massive Social #Networks | Influence et contagion | Scoop.it
PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.
luiy's insight:

Happiness and other emotions have recently been an important focus of attention in a wide range of disciplines, including psychology, economics, and neuroscience [1], [2], [3], [4]. Some of this work suggests that emotional states can be transferred directly from one individual to another via mimicry and the copying of emotionally-relevant bodily actions like facial expressions [5]. Experiments have demonstrated that people can “catch” emotional states they observe in others over time frames ranging from seconds to months [6], [7], and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of “service with a smile” on customer satisfaction and tipping [8].

 

Longitudinal data from face-to-face social networks has established that emotions as diverse as happiness [9], loneliness [10], and depression [11] are correlated between socially-connected individuals, and related work suggests that these correlations also exist online [4], [12], [13], [14], [15]. However, it is difficult to ascertain whether correlations in observational studies result from influencing the emotions of social contacts (contagion) or from choosing social contacts with similar emotions (homophily) [16].

 

Here, we propose an alternative method for detecting emotional contagion in massive social networks that is based on instrumental variables regression, a technique pioneered in economics [23]. In an experiment we would directly control each user's emotional expression to see what impact it has on their friends' emotional expression. However, since this is infeasible in our massive-scale setting, we identify a source of variation that directly affects the users' emotional expression (this variable is called an “instrument”). For this instrument, we use rainfall. Importantly, rainfall is unlikely to be causally affected by human emotional states, so if we find a relationship it suggests that rainfall influences emotional expression and not vice versa. We then measure whether or not the changes induced by the instrument predict changes in the friends' emotional expression. Instead of changing the user's emotion directly with an experimental treatment, we let rainfall do the work for us by measuring how much the rain-induced change in a user's expression predicts changes in the user's friends' expression.

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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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Detecting Automation of Twitter Accounts Are You a Human, #Bot, or #Cyborg? | #contagion

luiy's insight:

We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts:

 

1) an entropy-based component,

 

2) a spam detection component,


3) an account properties component, and


4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot, or cyborg.


Our experimental evaluation demonstrates the efficacy of the proposed classification system

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How Advanced #Socialbots Have Infiltrated Twitter | #influence #diffusion

How Advanced #Socialbots Have Infiltrated Twitter | #influence #diffusion | Influence et contagion | Scoop.it
Automated bots can not only evade detection but gather followers and become influential among various social groups, say computer scientists who have let their bots loose on Twitter.

 

If you have a Twitter account, the chances are that you have fewer than 50 followers and that you follow fewer than 50 people yourself. You probably know many of these people well but there may also be a few on your list who you’ve never met.

 

So here’s an interesting question: how do you know these Twitter users are real people and not automated accounts, known as bots, that are feeding you links and messages designed to sway your opinions?

 

You might say that bots are not very sophisticated and so easy to spot. And that Twitter monitors the Twittersphere looking for, and removing, any automated accounts that it finds. Consequently, it is unlikely that you are unknowingly following any automated accounts, malicious or not.

 

If you hold that opinion, it’s one that you might want to revise following the work of Carlos Freitas at the Federal University of Minas Gerais in Brazil and a few pals, who have studied how easy it is for socialbots to infiltrate Twitter.

 

Their findings will surprise. They say that a significant proportion of the socialbots they have created not only infiltrated social groups on Twitter but became influential among them as well. What’s more, Freitas and co have identified the characteristics that make socialbots most likely to succeed.


Via Ashish Umre
luiy's insight:

The worry is that automated bots could be designed to significantly influence opinion in one or more of these areas. For example, it would be relatively straightforward to create a bot that spreads false rumors about a political candidate in a way that could influence an election.


...but with an estimated 20 million fake Twitter accounts already set up, Twitter’s researchers have plenty of data to work with.

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#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion

#Predicting Successful #Memes using Network and Community Structure | #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

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António F Fonseca's curator insight, April 2, 2014 6:01 AM

Another paper about popularity prediction.

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How Gangnam Style" Went #Viral | #SNA #contagion #datascience

How Gangnam Style" Went #Viral | #SNA #contagion #datascience | Influence et contagion | Scoop.it
Data scientists trace how the most-viewed video in YouTube history spread across the Internet
luiy's insight:

When South Korean pop star Psy released his “Gangnam Style” video in 2012 it spread like wildfire. Researchers at Indiana University Bloomington tracked the spreading meme by following how Twitter users shared the video with friends and strangers alike. By the time 200 tweets had linked to the video among the subset of Twitter users studied, “Gangnam Style” had already reached 86 different communities of users (blue nodes). After 3,000 tweets the meme had spread to nearly 1,000 different communities (green). “Gangnam Style” soon became the most-viewed video in YouTube history; by late 2013, the video had amassed more than 1.8 billion views.

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#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion

#Virality Prediction and Community Structure in Social Networks | #SNA #memes #contagion | Influence et contagion | Scoop.it
How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily.
luiy's insight:

Our method aims to discover viral memes. To label viral memes, we rank all memes in our dataset based on numbers of tweets or adopters, and define a percentile threshold. A threshold of θT or θUmeans that a meme is deemed viral if it is mentioned in more tweets than θT% of the memes, or adopted by more users than θU% of the memes, respectively. All the features are computed based on the first 50 tweets for each hashtag h. Two baselines are set up for comparison. Random guessselects nviral memes at random, where nviral is the number of viral memes in the actual data.Community-blind prediction employs the same learning algorithm as ours but without the community-based features. We compute both precision and recall for evaluation; the former measures the proportion of predicted viral memes that are actually viral in the real data, and the latter quantifies how many of the viral memes are correctly predicted. Our community-based prediction excels in both precision and recall, indicating that communities are helpful in capturing viral memes (Fig. 5). For example, when detecting the most viral memes by users (θU = 90), our method is about seven times as precise as random guess and over three times as precise as prediction without community features. We achieve a recall over 350% better than random guess and over 200% better than community-blind prediction. Similar results are obtained using different community detection methods or different types of social network links (see SI).

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There Are No Super #Influencers: The Reality about Influencers from the world of #NetworkScience

There Are No Super #Influencers: The Reality about Influencers from the world of #NetworkScience | Influence et contagion | Scoop.it
The influence of influencers is overhyped. We all want to believe that there are these super-hero influencers that can make dramatic changes to organizations, countries, and societies. The idea has...
luiy's insight:

The idea has been spread in pop-culture in books like Malcolm Gladwell’s the Tipping Point. Recent developments in Network Sciencehave shown that our understanding of influencers – the super-connected individuals in our organizations and society – is more or less wrong.

 

So what is the truth behind influencers? The science is still figuring it out, but here is what we have learned so far.

 

It’s all about Micro-Influencers

The super-connected influencer do not exist, instead there are micro-influencers – those that have slightly more influence than the rest of the population influencing those around them to spread their ideas and messages about certain topics. (I would consider my friend Andrew a micro-influencer, he got our whole group of friends drinking high-quality craft beer after he himself jumped into the cult of American craft beer drinking).

 

We use to think that the human social network was constructed like our airport network (also called scale-free networks), there are hubs in which most traffic can get to most places, thus have huge influence on the flow of information.

 

The truth is that there are no Chicago O’Hare, or London Heathrow individuals.  Why? Because the human network does not work like the airport transportation network. The human capacity to manage relationships is finite. Unlike our major airports, we cannot just construct another terminal in ourselves to deal with more traffic. We have a limited number of relationships we can actively manage and the reach of our direct influence is limited by the relationships we manage.

 

The average number of friends people have on Facebook is around 200 – but there are some Facebook users who have 2000 friends (the max for an individual account), which is only 1 magnitude greater, not 10 or 20 times greater like we would expect if our human networks were more like airports: like the difference between Colorado Springs Airport traffic and Chicago O’Hare.

 

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Claude Emond's curator insight, January 16, 2014 6:54 PM

very interesting scoop.it by Luis about the «myth» of super influencers in the cyberspace. How collective intelligence really works !

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Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA

Mapping the Spread of Viruses / #Contagions via Contact Tracing | #SNA | Influence et contagion | Scoop.it
The Spread of a Contagion through a Human Network
luiy's insight:

Mapping the Spread of #Contagions via Contact Tracing

A contagion passed by human contact, such as SARS or TB, spreads through human networks based on how infectious and susceptible each party is. Multiple contacts with infectious others play a role in the probability of infection. Contagions that flow through human-based networks can be bad(disease, gossip), good(ideas and information) or neutral(money and investments).

 

Public health officials perform contact tracing to map the spread of the infection and manage its diffusion. The network map above, created at the epidemiology unit of The Centers for Disease Control [CDC], shows the spread of an airborne infectious disease. The map was created using actual contact data from the community in which the outbreak was happening.

 

Black nodes are persons with clinical disease (and are potentially infectious), pink nodes represent exposed persons with incubating (or dormant) infection and are not infectious, green represent exposed persons with no infection and are notinfectious. The infection status is unknown for the grey nodes.

 

Unfortunately the 'social butterfly' in this community, the black node in the center of the graph, is also the most infectious -- a super spreader.

 

Current procedures focus on inoculating the vulnerable -- often the very young and the very old. Network analysis tells us that it may be smarter, and more efficient, to focus on the spreaders -- those with many contacts to many groups.

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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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Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA

Truthy: Information Diffusion in Online Social Networks | #influence #virality #SNA | Influence et contagion | Scoop.it
luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing.

 

Our work to date includes a number of core research themes:

 

1. We study how individuals’ limited attention span affects what information we propagate and what social connections we make, and how the structure of social networks can help predict which memes are likely to become viral.

 

2. We explore social science questions via social media data analytics. Examples of research to date include analyses of geographic and temporal patterns in movements like Occupy Wall Street, societal unrest in Turkey, polarization and cross-ideological communication in online political discourse, partisan asymmetries in online political engagement, the use of social media data to predict election outcomes and forecast key market indicators, and the geographic diffusion of trending topics.

 

3. Truthy is an ensemble of web services and tools to demonstrate applications of our data mining research, from visualizing meme diffusion patterns to detecting social bots on Twitter.

 

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BeerBergman's curator insight, September 3, 2014 5:45 PM

"luiy's insight:

The focus of this research project is understanding how information propagates through complex socio-technical information networks. Leveraging large-scale public data from online social networking platforms, we are able to analyze and model the spread of information, from political discourse to market trends, from news to social movements, and from trending topics to scientific results, in unprecedented detail.

 

We study how popular sentiment, user influence, attention, social network structure, and other factors affect the manner in which information is disseminated. Additionally, an important goal of the Truthy project is to better understand how social media can be abused, for example by astroturfing."

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Experimental evidence of massive-scale #emotional #contagion through social networks | #datascience

Experimental evidence of massive-scale #emotional #contagion through social networks | #datascience | Influence et contagion | Scoop.it
luiy's insight:

Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.

 

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People who were exposed to fewer emotional posts (of either valence) in their News Feed were less expressive overall on the following days, addressing the question about how emotional expression affects social engagement online. This observation, and the fact that people were more emotionally positive in response to positive emotion updates from their friends, stands in contrast to theories that suggest viewing positive posts by friends on Facebook may somehow affect us negatively, for example, via social comparison (6, 13). In fact, this is the result when people are exposed to less positive content, rather than more.

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

The evolution of #memes on Facebook | #SNA #contagion | Influence et contagion | Scoop.it
luiy's insight:

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.

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GLEAMviz.org – The GLEAM Simulator system | #dataviz #complexity #prediction

GLEAMviz.org – The GLEAM Simulator system  | #dataviz #complexity #prediction | Influence et contagion | Scoop.it
luiy's insight:

The GLEAM Simulator system consists of the GLEAM Server and the GLEAMviz Client application.

 

The GLEAM Server uses GLEAM as the engine to perform the simulations. This server runs on high-performance computers managed by the GLEAM project.

 

The GLEAMviz Client is a desktop application through which users interact with the GLEAM Server. It provides a simple, intuitive and visual way to set up simulations, develop disease models, and evaluate simulation results using a variety of maps, charts and data analysis tools.

 

 

 Visualisation and analysis

 

GLEAMviz offers three types of visualization. The first shows the spread of the infection on a zoomable 2D map while charts show the number of new cases at various levels of detail.

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#Algorithm Distinguishes #Memes from Ordinary Information | #contagion

#Algorithm Distinguishes #Memes from Ordinary Information | #contagion | Influence et contagion | Scoop.it
Network theorists have developed a way to identify the top memes in science and study how they evolved 
luiy's insight:

Memes are the cultural equivalent of genes: units that transfer ideas or practices from one human to another by means of imitation. In recent years, network scientists have become increasingly interested in how memes spread.

This kind of work has led to important insights into the nature of news cycles, into information avalanches on social networks and into the role that networks themselves play in this spreading process.

 

But what exactly makes a meme and distinguishes it from other forms of information is not well understood. Today, Tobias Kuhn at ETH Zurich in Switzerland and a couple of pals say they’ve developed a way to automatically distinguish scientific memes from other forms of information for the first time. And they’ve used this technique to find the most important ideas in physics and how they’ve evolved in the last 100 years.

 

The word ‘meme’ was coined by the evolutionary biologists Richard Dawkins in his 1976 book The Selfish Gene. He argued that ideas, melodies, behaviours and so on, all evolve in the same way as genes, by means of replication and mutation, but using human culture rather than biology as the medium of evolution.

  

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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 | Scoop.it
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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Creating Social #Contagion Through #Viral Product #Design: A Randomized Trial of Peer #Influence in Networks

Creating Social #Contagion Through #Viral Product #Design: A Randomized Trial of Peer #Influence in Networks | Influence et contagion | Scoop.it
luiy's insight:

We examine how firms can create word-of-mouth peer influence and social contagion by designing viral features into their products and marketing campaigns. To econometrically identify the effectiveness of different viral features in creating social contagion, we designed and conducted a randomized field experiment involving the 1.4 million friends of 9,687 experimental users on Facebook.com. We find that viral features generate econometrically identifiable peer influence and social contagion effects. More surprisingly, we find that passive-broadcast viral features generate a 246% increase in peer influence and social contagion, whereas adding active-personalized viral features generate only an additional 98% increase. Although active-personalized viral messages are more effective in encouraging adoption per message and are correlated with more user engagement and sustained product use, passive-broadcast messaging is used more often, generating more total peer adoption in the network. Our work provides a model for how randomized trials can identify peer influence in social networks.


Article in:  http://icos.umich.edu/sites/icos6.cms.si.umich.edu/files/lectures/VPDFinal1110.pdf

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Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion

Twitter Trends Help Researchers Forecast Viral #Memes | #SNA #contagion | Influence et contagion | Scoop.it
Researchers are forecasting which memes will spread far and wide
luiy's insight:

What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.

 

“We didn't expect to see that the viral memes were going to behave very differently from nonviral memes at their beginnings,” says Lilian Weng, a graduate student in informatics at Indiana University Bloomington. Those differences allowed Weng and her colleagues to forecast memes that would go viral with an accuracy of better than 60 percent, the team reported in a 2013 study.

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Building A Social Network Of #Crime | #SNA #influence

Building A Social Network Of #Crime | #SNA #influence | Influence et contagion | Scoop.it
Can software distill mayhem into a database?
luiy's insight:

ORCA (Organizational, Relationship, and Contact Analyzer) started by linking people who had been arrested together—the most objective way a record shows that people have, at the very least, been at the same place at the same time. From there, it categorized those who had admitted a gang affiliation. And then, based on social links, it gave the others a numerical probability of a particular affiliation. ORCA further analyzed clustered nodes within the network to identify groups and subgroups—a crew occupying a street corner, for example. By zeroing in on people connected across many groups and subgroups, ORCA singled out the most influential ones.

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Selection Effects in Online #Sharing: Consequences for Peer Adoption | #contagion

luiy's insight:

Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt. 


We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. 
We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.

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The Dark Corners of the Internet | #SNA #dataviz

The Dark Corners of the Internet | #SNA #dataviz | Influence et contagion | Scoop.it
The way information spreads through society has been the focus of intense study in recent years. This work has thrown up…
luiy's insight:

The way information spreads through society has been the focus of intense study in recent years. This work has thrown up some dramatic results; it explains why some ideas become viral while others do not, why certain individuals are more influential than others and how best to exploit the properties of a network to spread information most effectively.

 

But today, Chuang Liu at Hangzhou Normal University in China and a few pals have a surprise. They say that when information spreads, there are always blind spots in a network that never receive it. And these unreachable dark corners of the network can be numerous and sizeable.

 

Until now theorists have predicted that information can always spread until it saturates a network to the point where everybody has received it. These predictions are come from models based on our understanding of diseases and the way they percolate through a population. The basic assumption is that information spreads in the same way.

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Marco Valli's curator insight, January 11, 2014 6:36 AM

A different view on information spread and diffusion on a network. A simple model, accounting for the key difference between "viruses" and "information", both from the sender and the receiver point of view.