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Influence et contagion
L'influence et la contagion dans la cyberculture
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Multilayer Networks tutorial | #SNA #models

These are the slides for a tutorial talk about "multilayer networks" that I gave at NetSci 2014. I walk people through a review article that I wrote with my …
luiy's insight:

Classifying Multilayer Networks

 

•  Special cases of multilayer networks include: mulplex networks, interdependent networks, networks of networks, node-­‐colored networks, edge-­‐colored mulgraphs, …

 

• To obtain one of these special cases, we impose constraints on the general structure defined earlier.

 

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Other Types of  Multilayer Networks

 

•  k-­‐partite graphs

– Bipartite networks are most commonly studied

 

• Coupled-­‐cell networks

– Associate a dynamical system with each node of a multigraph. Network structure through coupling terms.

 

• Multilevel networks – Very popular in social statistics literature (upcoming special issue of Social Networks)

– Each level is a layer

– Think ‘hierarchical’ situations. Example: ‘micro-­‐ level’ social network of researchers and a ‘macro-­‐ level’ for a research-­‐exchange network between laboratories to which the researchers belong.

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The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience

The Harlem Shake Story: Birth of a #Meme | #SNA #virality #datascience | Influence et contagion | Scoop.it
A series of remixed videos along with a number of key communities around the world triggered a rapid escalation, giving the meme widespread global visibility. Who were the initial communities behind this mega-trend? SocialFlow took a look at 1.9 million tweets during a two-week period that included the words ’harlem shake’, or some versions of it. 
luiy's insight:

Social Flow looked at the social connections amongst users who were posting to the meme. This gave them the ability to identify the underlying communities engaging with the meme at a very early stage. In the graph above each node represents a user that was actively posting and referencing the Harlem Shake meme on Feb 7 or 8 to Twitter. Connections between users reflect follow/friendship relationships. The graph is organized using a force directed algorithm, and colored based on modularity, highlighting dominant clusters - regions in the graph which are much more interconnected. These clusters represent groups of users who tend to have some attribute in common. The purple region in the graph (left side) represents African American Twitter users who are referencing Harlem Shake in its original context. There's very little density there as it is not really a tight-knit community, but rather a segment of users who are culturally aligned, and are clearly much more interconnected amongst themselves than with other groups.

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Handbook of Network Analysis | #SNA #taxonomy

Handbook of Network Analysis | #SNA #taxonomy | Influence et contagion | Scoop.it

Taxonomy of Networks

luiy's insight:

This is the Handbook of Network Analysis, the companion article to the KONECT (Koblenz Network Collection) project. This project is intended to collect network datasets, analyse them systematically, and provide both datasets and the underlying network analysis code to researchers. This article outlines the project, gives all definitions used within the project, reviews all network statistics used, reviews all network plots used, and gives a brief overview of the API used by KONECT.

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graph-tool: Efficent network analysis with #python | #SNA #tools

graph-tool: Efficent network analysis with #python | #SNA #tools | Influence et contagion | Scoop.it
graph-tool: Efficent network analysis with python
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An extensive array of features is included, such as support for arbitrary vertex, edge or graph properties, efficient "on the fly" filtering of vertices and edges, powerful graph I/O using the GraphML, GML and dot file formats, graph pickling, graph statistics (degree/property histogram, vertex correlations, average shortest distance, etc.), centrality measures, standard topological algorithms (isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.), generation of random graphs with arbitrary degrees and correlations, detection of modules and communities via statistical inference ,,,,,, 

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A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA

A Look Inside Those 1.1 Million Open-Internet Comments | #datascience #complexity #SNA | Influence et contagion | Scoop.it
These cluster maps give us a two-dimensional look at the complex arguments Americans posted on the topic of net neutrality. One theme in the comments had to do with the American dream.
luiy's insight:

How To Read This Cluster Map

 

- Similar nodes typically cluster together and clusters are grouped by color

- Each node represents a news story; a node sized by degree represents number of connections (i.e., similarity) to other nodes

- Connections represent similar language used across nodes

- A node bridging two clusters can indicate a story that synthesizes multiple topics

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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 | Scoop.it
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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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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News Information Flow Tracking, Yay! (NIFTY) : System for large scale real-time tracking of #memes | #datascience #algorithms

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Twitter #bots in class. You're here because of a robot | #datascience #agents #influence

Twitter #bots in class. You're here because of a robot | #datascience #agents #influence | Influence et contagion | Scoop.it
Note: This post is co-written with Piotr Sapieżyński Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hun...
luiy's insight:

A large part of our motivation for investigating Twitter bots in class is that the amount of manipulation that humans are experiencing on line is ever increasing. Think, for example, about how Facebook’s time-line filtering algorithm shapes the world view of hundreds of millions around the globe. And that’s just the most main stream example.

 

Social influence

 

As the course progressed, we focused on creating bots that could use machine learning to recognize “good” content for tweeting and retweeting. Bots that are able to detect topics within their tweet-stream … and distinguish between real, human accounts and robots among their followers.

However, the question remained: Can those thousands of followers  be converted to influence on Twitter? For the class’ final project, we decided to put that to the test.

The overall goal was to for each team to build a convincing bot, get human followers, and  at a specified time, for everyone work together to make specific hashtags trend on twitter. So how to achieve that goal? Here’s an overview of what each team has worked on:

 

- Build convincing avatars and use the high follower-counts as part of the disguise. 

 

- Use machine learning to tell who’s a bot and who’s not (in order to focus only on humans and ignoring bots). 

 

- Use natural language processing & machine learning to discover quality content to re-tweet and tweet. 

 

- Use network theory, to explore the network surrounding existing followers, making sure that bot actions reach entire communities.

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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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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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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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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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Strongly Connected Component | #SNA #datascience

Strongly Connected Component | #SNA #datascience | Influence et contagion | Scoop.it
luiy's insight:

Graph connectivity is of special interest in networking, search, shortest path and many other applications.

 

Strongly connected directed graph has a path from all vertices to all vertices.

 

Strongly connected components (SCC) are the strongly connected subgraphs.

 

 - abe, fg, cd and h are the strongly connected subgraphs of G.

 

 

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#Google matrix analysis of directed networks | #datascience #algorithms

#Google matrix analysis of directed networks | #datascience #algorithms | Influence et contagion | Scoop.it
luiy's insight:

This review describes matrix tools and algorithms which facilitate classification and information retrieval from large networks recently created by human activity. The Google matrix formed by links of the network has typically a huge size. Thus, the analysis of its spectral properties including complex eigenvalues and eigenvec- tors represents a challenge for analytical and numerical methods. It is rather surprising, but the class of such matrices, belonging to the class of Markov chains and Perron-Frobenius operators, was practically not inves- tigated in physics. Indeed, usually the physical prob- lems belong to the class of Hermitian or unitary ma- trices. Their properties had been actively studied in the frame of Random Matrix Theory (RMT) (Akemann et al., 2011; Guhr et al., 1998; Mehta, 2004) and quantum chaos (Haake, 2010). The analytical and numerical tools developed in these research fields allowed to understand many universal and peculiar features of such matrices in the limit of large matrix size corresponding to many-body quantum systems (Guhr et al., 1998), quantum comput- ers (Shepelyansky , 2001) and a semiclassical limit of large quantum numbers in the regime of quantum chaos (Haake, 2010). In contrast to the Hermitian problem, the Google matrices of directed networks have complex eigenvalues. 

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Top 10 #algorithms in data mining | #datascience

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This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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#freeBook: Social Media Mining | #datascience #SNA #influence

#freeBook: Social Media Mining | #datascience #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

The Social Media Mining book is published by Cambridge University Press in 2014. Please see Cambridge’s page for the book for more information or if you are interested in obtaining an examination copy.

 

Download a complete pre-publicaiton draft of the Social Media Mining book in PDF format. The reader is allowed to take one copy for personal use but not for further distribution (either print or electronically). The book is available for purchase from Cambridge University Press and other distribution channels.

 

You can also download each chapter below:

 

• Chapter 1. Introduction to social media mining

 

Part I: Essentials
• Chapter 2. Graph essentials
• Chapter 3. Network measures
• Chapter 4. Network models
• Chapter 5. Data mining essentials

 

Part II: Communities and Interactions
• Chapter 6. Community analysis
• Chapter 7. Information diffusion in Social Media

 

Part III: Applications
• Chapter 8. Influence and homophily
• Chapter 9. Recommendation in social media
• Chapter 10. Behavior analytics

 

Download the Bibliography

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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 | Scoop.it
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 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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#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, 6:01 AM

Another paper about popularity prediction.

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Time varying networks and the weakness of strong ties | #patterns #rumor #SNA

Time varying networks and the weakness of strong ties | #patterns #rumor #SNA | Influence et contagion | Scoop.it

In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.

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Multiplex networks and interest group #influence reputation: An exponential random graph model I #SNA #datascience

Multiplex networks and interest group #influence reputation: An exponential random graph model I #SNA #datascience | Influence et contagion | Scoop.it
luiy's insight:

This article argues that the embeddedness of interest groups in multiplex networks is an important explanation for variation in interest group influence reputations. Interest groups participate in and learn about the political process through their communication with other groups, collaboration in coalitions, and advocacy in issue areas. As a group engages in communication, collaboration, and issue advocacy, its performance of multiple roles is visible to other interested observers that use this information to make judgments about the group’s contribution (positive or negative) to policy debates. Thus, examining the multiple ways in which interest groups are connected and disconnected helps to account for how their representatives see and think about the community of which they are a part, as well as how they are seen by that community. 

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