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Social influences kill the wisdom of the crowd

A crowd can sometimes provide wisdom that its individual members lack. But, if you let those individuals talk among themselves, their individual errors can spread throughout the crowd, dragging its wisdom down.

Via Viktor Markowski
luiy's insight:

How important are these effects? Although popular culture has adopted the phrase "wisdom of the crowd" to apply to anything that involves more than a handful of people, the original description of a crowd's wisdom made it clear that it only applied to a limited number of question types and circumstances. So, finding additional limits really shouldn't be much of a surprise. It does, however, serve as an added caution that, just because there's a crowd involved, we shouldn't assume that anything that comes out of the crowd is wise. As the authors note, we seem to have a tendency to make exactly that assumption. "Opinion polls and the mass media largely promote information feedback and therefore trigger convergence of how we judge the facts," they conclude. "The wisdom of crowd effect is valuable for society, but using it multiple times creates collective overconfidence in possibly false beliefs."

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Influence et contagion
L'influence et la contagion dans la cyberculture
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Handbook of Graph Drawing and #Visualization | #freebook #SNA

Handbook of Graph Drawing and #Visualization | #freebook #SNA | Influence et contagion | Scoop.it
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FeaturesPresents the topological and geometric foundations of graph drawingDescribes many graph drawing algorithms and software systems, including the GDToolkit, OGDF, and PIGALECovers various applications of graph drawing in biological networks, computer security, data analytics, education, computer networks, and social networks

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Computer-based #personality judgments are more accurate than those made by humans | #social-cognitive

Computer-based #personality judgments are more accurate than those made by humans | #social-cognitive | Influence et contagion | Scoop.it

This study compares the accuracy of personality judgment a ubiquitous and important social-cognitive activity between computer models and humans. Using several criteria, we show that computers judgments of people's personalities based on their digital footprints are more accurate and valid than judgments made by their close others or acquaintances (friends, family, spouse, colleagues, etc.). Our findings highlight that people’s personalities can be predicted automatically andwithout involving human social-cognitive skills.

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Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA

Structural #Patterns of the Occupy Movement on Facebook | #socialchange #SNA | Influence et contagion | Scoop.it

In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks.

 

Structural Patterns of the Occupy Movement on Facebook
Michela Del Vicario, Qian Zhang, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi

http://arxiv.org/abs/1501.07203


Via Complexity Digest
luiy's insight:
Data Description The dataset represents a complete screenshot of the Occupy Movement in the period immediately following the outbreak of the protest on September 17th, 2011 in the Zuccotti Park of New York. The dataset covers all the posts until the end of February 2013, at the time when all the major protests were no more active. After the Zuccotti occupation, in fact, an October full of similar occupational events followed, leading to an international protest movement that extended itself until the end of 2012, when the movement was principally an online collective protest.
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How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns

How #Network Science Is Changing Our Understanding of #Law | #SNA #patterns | Influence et contagion | Scoop.it
The first network analysis of the entire body of European Community legislation reveals the pattern of links between laws and their resilience to change.

Via Complexity Digest
luiy's insight:

One of the more fascinating areas of science that has emerged in recent years is the study of networks and their application to everyday life. It turns out that many important properties of our world are governed by networks with very specific properties.

 

These networks are not random by any means. Instead, they are often connected in the now famous small world pattern in which any part of the network can be reached in a relatively small number of steps. These kinds of networks lie behind many natural phenomena such as earthquakes, epidemics and forest fires and are equally ubiquitous in social phenomena such as the spread of fashions, languages, and even wars.

 

So it should come as no surprise that the same kind of network should exist in the legal world. Today, Marios Koniaris and pals at the National Technical University of Athens in Greece show that the network of links between laws follows exactly the same pattern. They say their network approach provides a unique insight into the nature of the law, the way it has emerged and how changes may influence it in the future.

 

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Dynamical Contact #Patterns in a Primary School | #SNA #learning

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This movie represents the dynamical evolution of the contacts during the first day of a deployment of the SocioPatterns sensing platform, see sociopatterns.org. Each dot represents an individual, and an edge is drawn when a contact between two individuals occurs. Only contacts lasting at least 40 s are retained. Each frame corresponds to an aggregation of the contact network over a time window of 20 mn, and successive frames correspond to aggregation time windows shifted by 10 s; the movie is then built using 20 frames per second. Nodes are disposed in circles corresponding to the various classes, with the teacher at the center, and color-coded according to the grade (teachers are shown in black).

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The Divination Network of Tarot | #SNA

The Divination Network of Tarot | #SNA | Influence et contagion | Scoop.it
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Tarot is a deck of cards used since the 15th century to play various games as well as for divination purposes. We at Nodus Labs studied the structure of various Tarot decks, treating the cards as the nodes and relations between them as edges, building a graph of relations between the cards that are invariant across various Tarot decks. We discovered that the structure of the resulting graph has a very specific community structure, which makes Tarot a very efficient tool for telling narratives. We are currently working on practical implementations of this study.

 

This research was performed by Dmitry Paranyushkin and Colin Johnco.

 

http://noduslabs.com/cases/divination-network-tarot/

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#SNA : Drug Marketers Use Social Network Diagrams to Help Locate Influential Doctors #health #bigdata

#SNA : Drug Marketers Use Social Network Diagrams to Help Locate Influential Doctors #health #bigdata | Influence et contagion | Scoop.it
Consulting companies like Activate Networks use social network diagrams to help pharmaceutical marketers identify prescribing histories and relationships among doctors.
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luiy's curator insight, May 20, 2013 10:08 AM

The information allows drug makers to know which drugs a doctor is prescribing and how that compares to a colleague across town. They know whether patients are filling their prescriptions — and refilling them on time. They know details of patients’ medical conditions and lab tests, and sometimes even their age, income and ethnic backgrounds.

The result, said one marketing consultant, is what would happen if Arthur Miller’s Willy Loman met up with the data whizzes of Michael Lewis’s “Moneyball.” “There’s a group of geeks, if you will, who are running the numbers and helping the sales guys be much more efficient,” said Chris Wright, managing director of ZS Associates, which conducts such analyses for pharmaceutical companies.

 

Drug makers say they are putting the information to good use, by helping a doctor improve the chances that their patients take their medications as prescribed, or making sure they are prescribing the right drug to the right patients.

 

Some doctors, however, expressed discomfort with the idea of sensitive data being used to sell drugs, even though federal law requires that any personally identifiable information be removed. “I think the doctors tend not to be aware of the depths to which they are being analyzed and studied by people trying to sell them drugs and other medical products,” said Dr. Jerry Avorn, a professor of medicine at Harvard Medical School and a pioneer of programs for doctors aimed at counteracting the marketing efforts of drug makers. “Almost by definition, a lot of this stuff happens under the radar — there may be a sales pitch, but the doctor may not know that sales pitch is being informed by their own prescribing patterns.”

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Classic Authors Who Suck, According To Other Classic Authors | #controverses #dataviz #SNA

Classic Authors Who Suck, According To Other Classic Authors |  #controverses #dataviz #SNA | Influence et contagion | Scoop.it

Do you hate negative book reviews? Try telling that to Mark Twain, D.H. Lawrence, or any of these famous authors who have trashed their fellow writers' publications!

Photos: Associated Press, Getty
Jan Diehm for The Huffington Post

http://big.assets.huffingtonpost.com/20140514A_AuthorInsults.html

luiy's insight:

For every great author, there’s another great author eager to knock him or her down a few pegs. Although the writers on this map are typically deemed canonical by literary tastemakers, there wasn’t much mutual admiration amongst them.We’ve mapped out the rivalries and one-sided vendettas of many celebrated writers; just hover over an arrow between two authors to see a cutting insult directed by one to the other.

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Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence

Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence | Influence et contagion | Scoop.it
luiy's insight:

Socilab is a free tool that allows users to visualize, analyze, and download data on their LinkedIn network. It works with the LinkedIn API to a) calculate structural hole metrics such as network density, hierarchy and constraint - and displays your percentile compared to other users of the tool, b) display a dynamic/interactive visualization of your ego network with node coloring by industry and an option to enable/disable connections to self using D3.js, and c) produce a CSV adjacency matrix or Pajek edgelist for download and import into your favorite SNA package. Users might find it useful for class tutorials and/or quickly and cheaply fielding crude network surveys. Former users of the now deprecated LinkedIn inMaps may find this to be a useful alternative.

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

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

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

 

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

 

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


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


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

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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.

 

---------

 

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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How Videos Go #Viral part | / #metrics #SNA #contagion

How Videos Go #Viral part | / #metrics #SNA #contagion | Influence et contagion | Scoop.it
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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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#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz

#Culturegraphy: the Cultural Influences and References between Movies | #DH #influence #dataviz | Influence et contagion | Scoop.it
luiy's insight:

Culturegraphy [culturegraphy.com], developed by "Information Model Maker" Kim Albrecht reveals represent complex relationships of over 100 years of movie references.

 

Movies are shown as unique nodes, while their influences are depicted as directed edges. The color gradients from blue to red that originate in the1980s denote the era of postmodern cinema, the era in which movies tend to adapt and combine references from other movies.

 

Although the visualizations look rather minimalistic at first sight, their interactive features are quite sophisticated and the resulting insights are naturally interesting. Therefore, do not miss out the explanatory movie below.

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5 Questions for Duncan Watts | #SNA #influence

luiy's insight:

Duncan J. Watts, principal researcher at Microsoft Research, is the 2014 winner of the Everett M. Rogers Award. The USC Annenberg Norman Lear Center got to sit down with him and ask him 5 questions about his talk "Social Influence in Markets & Networks: What's So Viral About Going "Viral"? 

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How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA

How Steve Jobs Connected It All: An Interactive Look at #Apple’s Technology #History | #influence #SNA | Influence et contagion | Scoop.it
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Data sources & methodology

 

The networks in this post are based on all Apple patents published between January 1978 and October 2014. To avoid duplicate entries based on applications for the same invention in multiple countries, only representatives of so-called patent families (groups of patents disclosing the same invention in multiple countries) are included. This leads to a total dataset of 9,663 patents listing 5,272 unique inventors over the 36 years analysed. Inventor names were deduplicated using a pattern-matching algorithm – any false negatives and positives were taken care of manually. This was done to avoid e.g. ‘Steve Jobs’ and ‘Stephen P Jobs’ appearing as two separate inventors. Inventors (circles) are connected in the networks whenever they appear as co-inventors on a patent; patents (squares) are connected to inventors whenever an inventor appears on that patent. Colours were assigned to patents and inventors based on the network cluster they reside in – clusters were identified using an algorithm which groups nodes when they are densely connected internally, but sparsely connected to other groups.

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Determinants of #Meme Popularity | #influence #twitter

Determinants of #Meme Popularity | #influence  #twitter | Influence et contagion | Scoop.it

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

 

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

http://arxiv.org/abs/1501.05956


Via Complexity Digest
luiy's insight:

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

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Links that speak: The global #language network and its association with global fame | #SNA #DH

Links that speak: The global #language network and its association with global fame | #SNA #DH | Influence et contagion | Scoop.it
luiy's insight:
Languages vary enormously in global importance because ofhistorical, demographic, political, and technological forces. How-ever, beyond simple measures of population and economic power,there has been no rigorous quantitative way to define the globalinfluence of languages. Here we use the structure of the networksconnecting multilingual speakers and translated texts, as expressedin book translations, multiple language editions of Wikipedia, andTwitter, to provide a concept of language importance that goesbeyond simple economic or demographic measures. We find thatthe structure of these three global language networks (GLNs)is centered on English as a global hub and around a handfulof intermediate hub languages, which include Spanish, German,French, Russian, Portuguese, and Chinese. We validate the mea-sure of a language’s centrality in the three GLNs by showing that itexhibits a strong correlation with two independent measures ofthe number of famous people born in the countries associatedwith that language. These results suggest that the position ofa language in the GLN contributes to the visibility of its speakersand the global popularity of the cultural content they produce.
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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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The Evolution of Beliefs over Signed Social Networks | #SNA #influence

The Evolution of Beliefs over Signed Social Networks | #SNA #influence | Influence et contagion | Scoop.it
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We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes DeGroot model to account for negative links: when two enemies interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense, and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a {\it no-survivor} theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries, and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon.

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Social Networks Visualizer | #SNA #Tools

Social Networks Visualizer | #SNA #Tools | Influence et contagion | Scoop.it
Social Networks Visualizer KDE-Apps.org Community Portal for KDE Applications Software Office Multimedia Graphic Network Games Utilities Screensaver QT Linux
luiy's insight:

It lets you construct networks (mathematical graphs) with a few clicks on a virtual canvas or load networks of various formats (GraphML, GraphViz, Adjacency, Pajek, UCINET, etc). Also, SocNetV enables you to modify the social networks, analyse their social and mathematical properties and apply visualization layouts for relevant presentation.

Furthermore, random networks (Erdos-Renyi, Watts-Strogatz, ring lattice, etc) and known social network datasets (i.e. Padgett's Florentine families) can be easily recreated. SocNetV also offers a built-in web crawler, allowing you to automatically create networks from links found in a given initial URL.

The application computes basic graph properties, such as density, diameter, geodesics and distances (geodesic lengths), connectedness, eccentricity, etc. It also calculates advanced structural measures for social network analysis such as centrality and prestige indices (i.e. closeness centrality, betweeness centrality, information centrality, power centrality, proximity and rank prestige), triad census, cliques, clustering coefficient, etc.

 

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World Cup 2014 - Germany vs. Argentina | #SNA #dataviz

World Cup 2014 - Germany vs. Argentina | #SNA #dataviz | Influence et contagion | Scoop.it
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The 2014 World Cup in Brazil has begun. Check HuffPost's World Cup dashboard throughout the tournament for standings, schedules, and detailed summaries of each match.

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The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Influence et contagion | Scoop.it
Before I wrote this article, I went through two stages. In the first stage, I cruised the academic journals for interesting papers. Once I found a ...
luiy's insight:

Can the pattern of neurons firing in my brain predict how much this article will be retweeted on twitter?

 

A recent study conducted by Emily Falk, Matthew Lieberman, and colleagues gets us closer to answering these important questions. The researchers recruited undergraduate participants and randomly assigned them to two groups: the “interns” and the “producers.” The 20 interns were asked to view ideas for television pilots and provide recommendations to the 79 producers about which shows should be considered for further development and production. All of the interns had their brains scanned by fMRI while they viewed the videos, and they were then videotaped while they discussed the merits of each pilot show idea. The producers rated which ideas they would like to further recommend. How was neural activity related to the spread of ideas?

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Ebola Twitter Network | #SNA #influence #gephi

Ebola Twitter Network | #SNA #influence #gephi | Influence et contagion | Scoop.it
Introduction During the week beginning 15 September I collected 240k Tweets containing the word ‘ebola’. The following information was extracted from the Twitter Search API: Tweet: Text, Created At, Favorites User: Name, Followers, Following, Location ReTweets Mentions Tags Reply To...
luiy's insight:

TOOLS

 

More to follow in next post but here is a brief list of the tools used to create this post. 

 

- Data Collection: Python 

 

- Graph Selection: Neo4j graph database to query nodes and create the network. 

 

- Data Analysis: R 

 

- Visualisation: Gephi

 

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Nicholas Christakis: The #Sociological Science Behind Social #Networks and Social #Influence | #SNA

If You're So Free, Why Do You Follow Others? The Sociological Science Behind Social Networks and Social Influence. Nicholas Christakis, Professor of Medical ...

luiy's insight:

If you think you're in complete control of your destiny or even your own actions, you're wrong. Every choice you make, every behavior you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you're absorbing altruism performed by someone you'll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way

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Bill Aukett's curator insight, September 29, 2014 8:34 PM

Human networks as complex systems?

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Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics

Who is central to a social network? It depends on your centrality #measure | #sna #influence #basics | Influence et contagion | Scoop.it
luiy's insight:

One important feature of networks is the relative centrality of individuals in them.  Centrality is a structural characteristic of individuals in the network, meaning a centrality score tells you something about how that individual fits within the network overall.  Individuals with high centrality scores are often more likely to be leaders, key conduits of information, and be more likely to be early adopters of anything that spreads in a network. 

 

- Individuals who are highly connected to others within their own cluster will have a high closeness centrality.

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