Análisis de Redes Sociales
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Onalytica: #BigData Influencers | #SNA #pagerank

Onalytica: #BigData Influencers | #SNA #pagerank | Análisis de Redes Sociales | Scoop.it

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luiy's curator insight, November 16, 2013 11:46 AM

The latest Onalytica ranking of Big Data influencers on Twitter is now available. The top 200 influencers are listed below.

 

Methodology: We took the tweets for the last 6 months containing the hashtag #BigData and treated mentions of other tweeters as a link to these. This initial network was made up of 150,486 nodes. After removing smaller, isolated components of the network we calculated mathematical PageRank, which is used to rank the list.

 

The methodology is different from our previous Big Data ranking which was based on Betweenness Centrality. This methodology weights connectors higher, but we have found that PageRank more effectively highlight the real influencers in the network.

 

The new entries in the top 200 are more due to people moving from below the 200 water mark to above because they are drawing more attention in the context of Big Data, than due to changes in the methodology.

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Exploring Image #Virality in Google Plus | #dataviz #contagion


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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 | Análisis de Redes Sociales | 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...

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luiy's curator insight, November 15, 2013 11:13 AM

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.

 

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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Onalytica: #BigData Influencers | #SNA #pagerank

Onalytica: #BigData Influencers | #SNA #pagerank | Análisis de Redes Sociales | Scoop.it

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luiy's curator insight, November 16, 2013 11:46 AM

The latest Onalytica ranking of Big Data influencers on Twitter is now available. The top 200 influencers are listed below.

 

Methodology: We took the tweets for the last 6 months containing the hashtag #BigData and treated mentions of other tweeters as a link to these. This initial network was made up of 150,486 nodes. After removing smaller, isolated components of the network we calculated mathematical PageRank, which is used to rank the list.

 

The methodology is different from our previous Big Data ranking which was based on Betweenness Centrality. This methodology weights connectors higher, but we have found that PageRank more effectively highlight the real influencers in the network.

 

The new entries in the top 200 are more due to people moving from below the 200 water mark to above because they are drawing more attention in the context of Big Data, than due to changes in the methodology.

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Social Network Analysis & an Introduction to Tools

This presentation covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.

 
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Stephen Dale's curator insight, July 21, 2013 7:41 AM

A great introduction to the power and benefits of SNA. Some useful pointers to SNA tools as well.

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El límite de nuestra red social

El límite de nuestra red social | Análisis de Redes Sociales | Scoop.it

"El tamaño de la red social que las personas podemos manejar cognitivamente es una de las claves del análisis de redes sociales (ARS). La respuesta a esta pregunta no solo es importante porque se ha reactivado tras el fenómeno de los social networks sites (SNS) sino porque las diferencias en la capacidad de las personas para gestionar de forma eficaz el tamaño de sus redes sociales determina la posición de cada uno en la estructura social."


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Análisis de redes sociales para comunicación, marketing y relaciones públicas

Análisis de redes sociales para comunicación, marketing y relaciones públicas | Análisis de Redes Sociales | Scoop.it

"Los investigadores en Análisis de Redes Sociales (ARS) o Social Network Analysis (SNA) -desde las matemáticas, la física y las ciencias computacionales- han aportado conceptos y métricas para el estudio sistemático de procesos sociales impensables hace unos años para científicos sociales y, también en gran medida, es cuestión de que los responsables de departamentos de comunicación, marketing y relaciones públicas asuman estas líneas de trabajo como algo que hoy es posible y que sólo hace unos años podía ser considerado como ciencia ficción."


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Anatomía de un rumor en Twitter: cómo nace un rumor - Reputación Online, Netnografía & ARS

Anatomía de un rumor en Twitter: cómo nace un rumor - Reputación Online, Netnografía & ARS | Análisis de Redes Sociales | Scoop.it

Análisis de redes sociales y su representación en Twitter e identificar a social media influencers, rastrear cómo se viraliza un hashtag.


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Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad

Les bases neurologiques des “mèmes” : comment les idées deviennent-elles contagieuses ? | #contagio #viralidad | Análisis de Redes Sociales | Scoop.it
InternetActu.net est un site d'actualité consacré aux enjeux de l'internet, aux usages innovants qu'il permet et aux recherches qui en découlent.

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luiy's curator insight, September 12, 2013 5:03 PM

Comment les idées deviennent-elles contagieuses ? La thèse comparant certaines idées à des “virus du cerveau” ne date pas d’hier. Dans son livre Le Gène égoïste, paru en 1976, Richard Dawkins avait créé la notion de mèmes, analogues “mentaux” des gènes, qui étaient capables de s’autorépliquer d’un cerveau à l’autre, et qui, à l’instar des créatures vivantes, cherchaient avant tout à maximiser leur capacité de reproduction. Par la suite, certains avaient essayé de donner corps à une nouvelle science, la mémétique, se basant sur cette notion. L’idée n’a jamais vraiment pris, et peu de chercheurs (à l’exception peut être du philosophe Daniel Dennett et de l’anthropologue des religions Pascal Boyer) ont vraiment continué à travailler sur ces bases. En 2005, le Journal of Memetics fermait définitivement ses portes après huit années d’existence.

 

En revanche, les mèmes sont devenus un élément constitutif de la pop culture internet. Restait cependant à savoir si cette contagion des idées possède de véritables bases neurales ou si elle n’est rien d’autre… qu’un mème.

 

Des recherches effectuées à l’UCLA, sous direction du psychologue Matthew Lieberman, donnent aujourd’hui à penser qu’il y aurait une réalité biologique à l’oeuvre dans ce processus de “contamination” intellectuelle. Des chercheurs ont en effet étudié les mécanismes cérébraux impliqués dans le “buzz”.

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#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography

#ANAMIA EGOCENTER : Three #tools to visualize #personal networks | #dataviz #ethnography | Análisis de Redes Sociales | Scoop.it
Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization tech...

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luiy's curator insight, November 9, 2013 7:25 AM

Three tools to visualize personal networks

Data visualization techniques are enjoying ever greater popularity, notably thank to the recent boom of Big Data and our increased capacity to handle large datasets. Network data visualization techniques are no exception. in fact, appealing diagrams of social connections (sociograms) have been at the heart of the field of social network analysis since the 1930s, and have contributed a lot to its success. Today, all this is evolving at unprecedented pace.

In line with these tendencies, the research team of the project ANAMIA (a study of the networks and online sociability of persons with eating disorders, funded by the French ANR) of which I was one of the investigators, have developed new software tools for the visualization of personal network data, with different solutions for the three stages of data collection, analysis, and dissemination of results.

 

Specifically:

- ANAMIA EGOCENTER is a graphical version of a name generator, to be embedded in a computer-based survey to collect personal network data. It has turned out to be a user-friendly, highly effective interface for interacting and engaging with survey respondents;

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#BigData & Causal Inference. Measuring and Propagation #Influence in Networks | #Patterns

Sinan Aral is a leading expert on Social Networks, Social Media and Digital Strategy. He is an Assistant Professor and Microsoft Faculty Fellow at the NYU St...

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luiy's curator insight, November 15, 2013 11:21 AM

Among his several lines of research, Dr. Aral has done notable work studying how information diffusion in massive online social networks influences demand patterns, consumer e-commerce behaviors and word of mouth marketing. Sinan is a Phi Beta Kappa graduate of Northwestern University, holds masters degrees from the London School of Economics and Harvard University, and received his PhD from MIT. You can find him on Twitter @sinanaral.

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Social Network Analysis & an Introduction to Tools | #dataviz #SNA_indatcom #SNA

This presentation covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.

 


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Stephen Dale's curator insight, July 21, 2013 7:41 AM

A great introduction to the power and benefits of SNA. Some useful pointers to SNA tools as well.

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Researchers Draw Romantic Insights From Maps of Facebook Networks today

Researchers Draw Romantic Insights From Maps of Facebook Networks today | Análisis de Redes Sociales | Scoop.it
It’s not in the stars after all. Instead, it seems, the shape of a person’s social network is a powerful signal that can identify one’s spouse or romantic partner — and even if a relationship is likely to break up.

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Stephen Dale's curator insight, October 29, 2013 11:55 AM

Researchers Jon Kleinberg and Lars Backstrom postulate that the total number of mutual friends two people share — embeddedness, in social networking terms — is actually a fairly weak indicator of romantic relationships. Far better, they found, was a network measure that they call dispersion. #sna

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Identificando influyentes y líderes de opinión: más allá de amigos, followers… (1)

Identificando influyentes y líderes de opinión: más allá de amigos, followers… (1) | Análisis de Redes Sociales | Scoop.it

"... El Análisis de Redes Sociales (ARS) o Social Network Analysis (SNA) -técnicas restringidas hasta no hace mucho al ámbito matemático, físico o computacional- permite la representación de las relaciones (links o edges) entre nodos (personas, lugares, etc.) formando networks (redes). El estudio del ARS y su aplicación a la Comunicación, Marketing y las RRPP para comprender el comportamiento de las personas, aún prácticamente inexplorado desde las escuelas de negocio, consultoras, agencias de comunicación y relaciones públicas, etc. abre unas posibilidades extraordinarias para superar las aproximaciones simplistas (aunque de consenso) de representar, medir y comprender las prácticas comunicativas que se están produciendo cada día en el contexto social online entre cientos de miles de personas de forma dinámica e imparable."


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Redes Sociales en educación II

Redes Sociales en educación II | Análisis de Redes Sociales | Scoop.it
ANÁLISIS DE LAS REDES SOCIALES EN EDUCACIÓN
 
Juandon
 
 
 
Las redes sociales se están convirtiendo en un caballo de batalla en centros de educación de muchos países desarrollados.

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Henry Chero 's curator insight, September 24, 2013 3:50 AM

Redes Sociales en educación II