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antropologo.net, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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How People Consume #Conspiracy Theories on Facebook | #sna #controverses

How People Consume #Conspiracy Theories on Facebook | #sna #controverses | e-Xploration | Scoop.it
… in much the same way as mainstream readers consume ordinary news, say computer scientists.
luiy's insight:

And that raises an interesting question. How do conspiracy theories spread through the Internet and do people treat these ideas in a way that is fundamentally different to conventional stories from established news organizations?

 

To find out, Alessandro Bessi and pals at the Institute for Advanced Studies in Lucca, Italy, examined the way people on Facebook consume conspiracy theories versus the way they consume mainstream news. And they say there are remarkable similarities but also some interesting differences that may help to better understand the way that false information spreads around the web.

 

The team began by studying over 270,000 posts created on 73 different Facebook pages. They classified these pages according to the kind of information they contained, whether conspiracy news or mainstream scientific news. They also counted the number of likes each post received, a total of almost 10 million, the number of shares, as well as the individuals who contributed.

 

Having divided up the posts, they found that around 60,000 involved mainstream scientific news and over 200,000 involved alternative conspiracy news. And while the scientific news received 2.5 million likes, the alternative news had over 6.5 million likes.

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#Naoyun – Visualize Live Twitter Activity | #SNA #gephi

#Naoyun – Visualize Live Twitter Activity | #SNA #gephi | e-Xploration | Scoop.it
luiy's insight:

The TwitterStreamer is the main class that manage the Twitter API . This class also load a class that extends TwitterGephiStreamer. Each time the Twitter Api get a new Status, the TwitterStreamer call the newStatus method from the TwitterGephiStreamer class.

On this method, there is the « Network Logic ». I called « Network Logic » all the processes and the rules to create a network from Twitter status.

 

For the moment, Naoyun have 3 network logic :

 

- TwittFullGrapher : Makes a complete graph by representing users, hashtags, tweet, media, links and their connection. The « Smart » version implemented in Naoyun won’t represent tracked hashtag to improve the visibility of the graph.


- TwitterUserNetwork : Represent only the relation between users.


- GeoTwitt : Just display Twitt with Geo localisation

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( #Fake ) friends with (Real) benefits?? | #sna #socialmedia

( #Fake ) friends with (Real) benefits?? | #sna #socialmedia | e-Xploration | Scoop.it
I paid $5 for 4,000 Twitter followers, and here’s what I found
luiy's insight:

The Experiment

 

At the start, I used Twitter’s API to get a list of my 2,600 existing Twitter followers. Then I set about figuring out where to buy more.

Google conveniently auto-completed my search for “buy twitter” with a number of useful suggestions, including: “buy twitter followers,” “buy twitter followers cheap” and “buy twitter followers reviews.” I was certainly not the only one searching for this.

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La "boîte à outils" du cartographe de l’information et des réseaux | #SNA #gephi #tools

La "boîte à outils" du cartographe de l’information et des réseaux | #SNA #gephi #tools | e-Xploration | Scoop.it
luiy's insight:

La "boîte à outils" du cartographe de l’information et des réseaux s’est sérieusement étoffée depuis quelques mois. De quoi équiper un peu plus encore une activité qui connaît quelques succès aujourd’hui, et dont on commence à comprendre le rôle essentiel pour les organisations et les territoires (en rappelant, comme à chaque fois, que le travail du cartographe d’informations commence là où finissent les données et finit là où commence l’interprétation des phénomènes). La nouveauté, cette fois-ci, est qu’il s’agit de deux "plateformes" en ligne et non plus seulement d’un plug-in ou d’une application isolée. Et, dans les deux cas, elles viennent enrichir les contextes d’utilisation de GEPHI (pour la 5e année en 2013 au Google Summer of Code, le fameux Gsoc). La preuve, si besoin était, que Gephi n’est pas une "application" mais un écosystème d’innovation permanente constituée d’une multitude d’acteurs.

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The Making of “History of #Rock in 100 Songs” | #dataviz #sna #gephi

The Making of “History of #Rock in 100 Songs” | #dataviz #sna #gephi | e-Xploration | Scoop.it
[This is a guest post by Susie Liu*, about the visualization project “History of Rock in 100 Songs”]
 
 
Figure 1. History of Rock in 100 Songs screenshot, live site here
 
The world of Rock ‘n’ Roll has always been very confusing to me.

Via ABoudières
luiy's insight:

When I found the Guardian’s list of 100 songs representative of rock music, I was inspired to use skills from my day job, creating data visualizations, to learn more about Rock ‘n’ Roll. With John as a domain expert to sanity check my work and a quick Google search of music APIs, we were ready to begin.

 

The original Guardian dataset only had the song title, artist, and release year of the song. I looked up more band information, such as its members and lifetime, using Last.fm and more song information, such as length and energy, using Echo Nest.

 

Exploring the data began with asking questions. I started simple. How do these songs appear over time? How do the timelines of the bands overlap with each other? I begin all of my visual brainstorming on paper like in Figure 2. It allows me to quickly see ideas. Plus, there is no hesitation to nix bad ideas because I’ve only invested a few minutes in the concept.

 

- See more at: http://visualoop.com/21745/the-making-of-history-of-rock-in-100-songs#sthash.9JUvGyoV.dpuf

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Lincoln #Logarithms: Finding Meaning in Sermons | #dataviz #sna #DH

Lincoln #Logarithms: Finding Meaning in Sermons | #dataviz #sna #DH | e-Xploration | Scoop.it
luiy's insight:

The content and the tools


We explored the power and possibility of four digital tools—MALLET, Voyant, Paper Machines, andViewshare.  MALLET, Paper Machines, and Voyant all examine text.  They show how words are arranged in texts, their frequency, and their proximity. Voyant and Paper Machines also allow users to make visualizations of word patterns. Viewshare allows users to create timelines, maps, and charts of bodies of material. In this project, we wanted to experiment with understanding what these tools, which are in part created to reveal, could and could not show us in a small, but rich corpus.  What we have produced is an exploration of the possibilities and the constraints of these tools as applied to this collection.

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Data Visualization #Design by Etan Lightstone at FutureStack13 | #dataviz #workflow

luiy's insight:

Data visualizations have become a first class citizen of information dissemination on the web, and a powerful tool when used effectively in product user interfaces . With technologies like D3, we are able to provide a great deal of interactivity to these visualizations, and with almost unlimited possibilities. This talk will be focusing on how to design effective data visualizations:

* Design process for data viz
* Visual design patterns to follow
* Using the right charts
* Data mining, and cutting through the noise of very large data sets

 

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Which social media network type is your topic? Which did you want it to be? | #SNA #datascience

Which social media network type is your topic?  Which did you want it to be? | #SNA #datascience | e-Xploration | Scoop.it
There are at least six different types of social media network structures present in systems like Twitter and other services in which people are able to reply to one another. Each of the six patter...
luiy's insight:

This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.

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The Anatomy of a Forgotten Social Network | #SNA #Tumblr

The Anatomy of a Forgotten Social Network | #SNA #Tumblr | e-Xploration | Scoop.it
While network scientists have been poring over data from Twitter and Facebook, they’ve forgotten about Tumblr. Now they’ve begun to ask how this network differs from the rest.
luiy's insight:

The study of social networks has gripped computer scientists in recent years. In particular, researchers have focused on a few of the biggest networks that have made their data available, such as some mobile phone networks, Wikipedia and Twitter.


But in the rush, one network has been more or less ignored by researchers: Tumblr, a microblogging platform similar to Twitter. So an interesting question is how the network associated with Tumblr is different from the Twitter network.


Today we get an answer thanks to the work of Yi Chang and pals at Yahoo Labs in Sunnyvale. These guys point out that relatively little is known about Tumblr compared to other networks like Twitter and set out to change this.


The basic statistics are straightforward. Tumblr is a microblogging service with about 160 million users who together have published over 70 billion posts.


The most significant difference between Tumblr and its bigger cousin, Twitter, is that there is no limit to the size of the posts that users can create. By contrast, Twitter imposes the famous 140-character limit on all of its posts. Tumblr also supports multimedia posts, such as images, audio, and video.

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#Truthy : Information diffusion research | #political #memes #patterns #SNA

#Truthy : Information diffusion research | #political #memes #patterns #SNA | e-Xploration | Scoop.it
luiy's insight:

Information diffusion research at Indiana University

 

Truthy is a research project that helps you understand how communication spreads on Twitter. 

 

We currently focus on tweets about politics, social movements and news.

 

 

Political Topics

Interactive visualizations of U.S. political conversation on Twitter :

 

- How does sentiment change over time in response to political events?

- What is most popular over time?

- Who are the most influential users?

- How does information spread in the social network?

 

 

Sentiment Timeline

- How does sentiment change over time in response to political events?

 

 

Gallery Descriptions of interesting memes:  http://truthy.indiana.edu/gallery

 

 

Meme Patterns:

What other memes are related to this one?  http://truthy.indiana.edu/memedetail?id=783&resmin=45&theme_id=4

 

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Intro materials: network analysis software (UCINET, NodeXL, Gephi, Statnet, ERGM, RSiena) | #datascience #SNA

Intro materials: network analysis software (UCINET, NodeXL, Gephi, Statnet, ERGM, RSiena) | #datascience #SNA | e-Xploration | Scoop.it
Introductory materials, handouts and R scripts for network analysis and visualization.

 

In the fall of 2012, I got to design & lead the weekly labs for a network seminar at USC. I also worked on the methods portion of the syllabus for the class. COMM 645: Communication Networks is a PhD-level course taught by Peter Monge. The labs cover a range of network tools – from the classic UCINETprogram through NodeXL and Gephi, to R introduction, Statnet, exponential random graph and actor-based modeling. Since the handouts & script examples may be useful for people outside the course, I’m sharing them here.


Via Pierre Levy
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Social networks for managers | #SNA #KM #ONA

Revision of Previous Show on SNA and Introduction to Tools The Language of Networks Introduction to Social Network Analysis/ Cases Tools for Analyzing social...

Via june holley, ukituki
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june holley's curator insight, March 5, 8:20 AM

Lots in here about social network mapping and analysis.

Liz Rykert's curator insight, March 6, 9:58 AM

Thanks for this one June!

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Thoughts on #SNA and online #learning | #intelligencecollective

Thoughts on #SNA and online #learning | #intelligencecollective | e-Xploration | Scoop.it
Following the previous post... The structural paradigm of  Social Network Analysis (SNA) with its constitutive theory and methods, began to emerge around the 1930s, applied and influenced by a broa...

Via Susan Bainbridge, Marinella De Simone
luiy's insight:

The connections within nodes in a network facilitate exchange of “resources”  which can be influenced by the quantity and quality of the linkages and interactions. Looking at online educational networks through a SNA lens is a way to establish wether the ways in which individuals connect with a particular environment may influence their access to information and knowledge. As Rita Kop states “the Web is portrayed as a democratic network on which peer to peer interaction might lead to a creative explosion and participative culture of activity” (Kop, 2012 p3) but how is this potential being exploited in education? What are the processes beyond this interaction and how can they be used to facilitate students access to information, knowledge and ideas?

 

The potential of social media in forming networks, extending students knowledge and translating this into academic achievement is impacted by a multitude of elements such as individuals’ attitudes (Morrison, 2002), University environment and socialisation processes (Yu et al., 2010). Other mechanisms influencing this process may be the particular educational practices and experiences, the success of connections, the dynamics in which participants negotiate the structure of the network and exchange practices and many others which can not be controlled.

 

This analysis can be enriched by Bordieau’s concept of “social capital”, which introduces a set of dynamics between the social dimension, the identity dimension (habitus) and the individual’s practice. In this system of reciprocal influences it is interesting to look at the transformation processes and effects of elements such as “weak ties”, “brokers”, “latent connections” and “structural holes” in the information flow within a network.

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Milena Bobeva's curator insight, March 1, 1:10 AM

Social Network Analysis should be a  paradigm for researching, designing, and evaluating not only online learning, but  the wider phenomenon of Education 3.0

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#SemanadelEmprendedor - México 2014 - Gráficos de conversaciones y Ecosistema | #SNA #gephi

#SemanadelEmprendedor - México 2014 - Gráficos de conversaciones y Ecosistema | #SNA #gephi | e-Xploration | Scoop.it
luiy's insight:

En el contexto del evento #SemanadelEmprendedor 2014 en México desarrollamos y presentamos los siguiente gráficos:

 

- los actores principales que participarón en Twitter en el contexto del evento #SemanadelEmprendedor 2014

 

- los actores principales del Ecosistema Emprendedor 2014 en México.

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How Yahoo Research Labs Studies Culture as a Formal Computational Concept | #SNA #DH

How Yahoo Research Labs Studies Culture as a Formal Computational Concept | #SNA #DH | e-Xploration | Scoop.it
The ultimate goal: a truly computational understanding of human society, say Yahoo’s computational anthropologists.
luiy's insight:

Today, Luca Maria Aiello at Yahoo Labs in Barcelona, Spain, and a couple of pals, change that. They tease apart the nature of the links that form on social networks and say these atoms fall into three different categories. They also show how to extract this information automatically and then characterize the relationships according to the combination of atoms that exist between individuals. Their ultimate goal: to turn anthropology into a full-blooded subdiscipline of computer science.

 

Aiello and co used two data sets from a pair of large social networks. The first consists of over 1 million messages sent between 500,000 pairs of users of the aNobii social network, which people use to talk about books they have read. The second is a set of 100,000 anonymized user pairs who commented on each other’s photos on Flickr, sending around 2 million messages in total.

 

The team analyzes these messages based on the type of information they convey, which they divide into three groups. The first type of information is related to social status; messages displaying appreciation or announcing the creation of the social tie such as a follow or like. For example, a user might say a photograph is “an excellent shot” or say they’ve followed somebody or acknowledged attention they’ve got by thanking them for visiting a site.

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Francisco Restivo's curator insight, August 20, 3:51 AM

Alex Pentland would call this Social Physics.

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Israel, Gaza, #War & Data | #SNA #socialmedia

Israel, Gaza, #War & Data | #SNA #socialmedia | e-Xploration | Scoop.it
social networks and the art of personalizing propaganda
luiy's insight:

It’s hard to shake away the utterly depressing feeling that comes with news coverage these days. IDF and Hamas are at it again, a vicious cycle of violence, but this time it feels much more intense. While war rages on the ground in Gaza and across Israeli skies, there’s an all-out information war unraveling in social networked spaces.

 

Not only is there much more media produced, but it is coming at us at a faster pace, from many more sources. As we construct our online profiles based on what we already know, what we’re interested in, and what we’re recommended, social networks are perfectly designed to reinforce our existing beliefs. Personalized spaces, optimized for engagement, prioritize content that is likely to generate more traffic; the more we click, share, like, the higher engagement tracked on the service. Content that makes us uncomfortable, is filtered out.

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Exploring Co-studied #MOOCs Subjects via Social Network Analysis | #Learning #SNA

Exploring Co-studied #MOOCs Subjects via Social Network Analysis | #Learning #SNA | e-Xploration | Scoop.it
Exploring Co-studied Massive Open Online Course Subjects via Social Network Analysis
luiy's insight:
AbstractMassive Open Online Courses (MOOCs) allow students to study online courses without requiring previous experience or qualifications. This offers students the freedom to study a wide variety of topics, freed from the curriculum of a degree programme for example; however, it also poses a challenge for students in terms of making connections between individual courses. This paper examines the subjects which students at one MOOC platform (Coursera) choose to study. It uses a social network analysis based approach to create a network graph of co-studied subjects. The resulting network demonstrates a good deal of overlap between different disciplinary areas. Communities are identified within the graph and characterised. The results suggests that MOOC students may not be seeking to replicate degree-style courses in one specialist area, which may have implications for the future moves toward ‘MOOCs for credit’. 
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The Surprising Science Behind How Super Connectors Scale Their Networks | #SNA #CommunityBuilding

The Surprising Science Behind How Super Connectors Scale Their Networks | #SNA #CommunityBuilding | e-Xploration | Scoop.it

How one of the world’s top super connectors uses scientific principles of social network analysis to dramatically scale the size and depth of his network without increasing the time spent. 

 


Via Kenneth Mikkelsen
luiy's insight:

The basic idea behind Metcalfe’s Law is that the ‘potential value’ of a network increases exponentially as you add new interconnected nodes. In the context of relationship building, this means that as you build relationships that are connected to each other, the value of the network increases exponentially.

 

In the parlance of social network analysis, density is the percentage of people in your network connected to each other that could be. By increasing density, new things spread more quickly through it.

What sorts of new things?

 

New research by professors Nicholas Christakis and James Fowler, authors of Connected: The Surprising Power of Social Networks And How They Shape Our Lives, shows that a surprisingly large number of things are spread through networks such as violence, money, happiness, germs, obesity, gossip, ideas, norms, and behaviors.

In other words, as we increase the density of our networks based on mutual support, we dramatically increase the rate at which its participants learn from each other and deepen their relationships.

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Four Ways of Looking at Twitter | #dataviz #SNA

Four Ways of Looking at Twitter | #dataviz #SNA | e-Xploration | Scoop.it
Data visualization is cool. It's also becoming ever more useful, as the vibrant online community of data visualizers (programmers, designers, artists, and statisticians — sometimes all in one...
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visone : analysis & visualization of social networks | #SNA #datascience

visone : analysis & visualization of social networks | #SNA #datascience | e-Xploration | Scoop.it
luiy's insight:

On the applications page in the visone wiki we list research projects in which visone has been applied as well as datasets on which the usage of visone can be illustrated. We are planning to release a demonstration video soon, too. In the meanwhile you might want to jump directly into basic and advanced tutorials, that focus on differnet aspects of the software.

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The French #Incubators Network | #SNA #dataviz

The French #Incubators Network | #SNA #dataviz | e-Xploration | Scoop.it
The French Incubators network: startups mapping, venture, social network connections
luiy's insight:

This approach give us the chance to catch a glimpse of the network density. It’s really interesting to notice we have a complete list of organizations, individuals, companies having different roles in the start-ups landscape: incubator, startups accelerator, venture, entrepreneur’s fund, mentoring, association of entrepreneurs, magazine, blogs. This unveils the momentum in France around startups development and the will to setup various structures to promote entrepreneurship.

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K-CORE DECOMPOSITION OF INTERNET GRAPHS: HIERARCHIES, SELF-SIMILARITY AND MEASUREMENT BIASES | #datascience #SNA

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Data Mining #Algorithms In R/Clustering/K-Cores | #datascience #SNA

Data Mining #Algorithms In R/Clustering/K-Cores | #datascience #SNA | e-Xploration | Scoop.it
luiy's insight:
Cores

The notion of core is presented in Butts (2010) as following:

 

Let G = (V, E) be a graph, and let f (v, S, G) for v ∈ V, S ⊆ V be a real-valued vertex property function (in the language of Batagelj and Zaversnik). Then some set H ⊆ V is a generalized k-core for f if H is a maximal set such that f (v, H, G) ≥ k for all v ∈ H. Typically, f is chosen to be a degree measure with respect to S (e.g., the number of ties to vertices in S). In this case, the resulting k-cores have the intuitive property of being maximal sets such that every set member is tied (in the appropriate manner) to at least k others within the set.

 

Degree-based k-cores are a simple tool for identifying well-connected structures within large graphs. Let the core number of vertex v be the value of the highest-value core containing v. Then, intuitively, vertices with high core numbers belong to relatively well-connected sets (in the sense of sets with high minimum internal degree). It is important to note that, while a given k-core need not be connected, it is composed of subsets which are themselves well-connected; thus, the k-cores can be thought of as unions of relatively cohesive subgroups.

 

As k-cores are nested, it is also natural to think of each k-core as representing a “slice” through a hypothetical “cohesion surface” on G. (Indeed, k-cores are often visualized in exactly this manner.)

The kcores function produces degree-based k-cores, for various degree measures (with or without edge values). The return value is the vector of core numbers for V , based on the selected degree measure. Missing (i.e., NA) edge are removed for purposes of the degree calculation.

 
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An Interactive Introduction to Network Analysis and Representation | #SNA #tools

An Interactive Introduction to Network Analysis and Representation | #SNA #tools | e-Xploration | Scoop.it
luiy's insight:

This interactive application is designed to provide an overview of various network analysis principles used for analysis and representation. It also provides a few examples of untraditional networks used in digital humanities scholarship. Finally, along with the various methods described interactively here are links to related scholarship.

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A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket | #identity #socialmedia #dumbar

A Brief Primer on Human Social Networks, or How to Keep $16 Billion In Your Pocket | #identity #socialmedia #dumbar | e-Xploration | Scoop.it
Over at The New York Times, Jenna Wortham wonders whether Facebook’s acquisition of Whatsapp points to a resurgence of small social…
luiy's insight:

Facebook’s key problem for many people has been what academics sometimes call “context-collapse,” which is the sense that Facebook sometimes feels like an extended Thanksgiving dinner where everyone you have ever known is at the table. This is an identity-constraining environment as it’s hard to know how to address such a large crowd at the same time. People have been grappling with this for a long time and have come up with a variety of solutions, including fleeing to Twitter & Instagram and, yes, Whatsapp.

 

Social scientists have long been trying to communicate this to technology companies: it is normal, natural and healthy to have different communication needs at different levels of one’s social network. One wonders if, early on, Mark Zuckerberg had listened to social scientists rather than declaring “having two identities for yourself is an example of a lack of integrity”, would he now have $16 billion more in his pocket?

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