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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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Connecting the Dots Behind the 2016 Candidates | #ddj #politics

Connecting the Dots Behind the 2016 Candidates | #ddj #politics | e-Xploration | Scoop.it
How the teams behind some likely and announced 2016 candidates are connected to previous campaigns, administrations and organizations.

Via Dominique Cardon
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Abridged List of #MachineLearning Topics. #Resources #tools #datascience

Abridged List of #MachineLearning Topics. #Resources #tools #datascience | e-Xploration | Scoop.it
luiy's insight:

- Deep learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.

 

- Online machine learning is a model of induction that learns one instance at a time thus reducing the amount of memory required.

 

- Natural Language Toolkit (NLTK) - a leading tool for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

 

-Computer Vision. OpenCV – popular computer vision library designed to by computational efficiency with a strong focus on real-time applications.

 

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Davos on Twitter: who do the attendees follow? | #dataviz #SNA

Davos on Twitter: who do the attendees follow? | #dataviz #SNA | e-Xploration | Scoop.it

Via ukituki
luiy's insight:

Every year, the World Economic Forum brings together the most recognisable figures of business and politics. With all eyes on Davos, we decided to turn the optics upside down and see who the twitterati gathered in Switzerland follow on social media.


The inner ring of circles represent the 20 most-followed accounts by Davos attendees, while the outer circles are individual attendees.

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ukituki's curator insight, January 22, 2015 10:39 AM

Network Visualization by Finanacial Times

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Linkedin inMaps discontinued : how to visualize your professional network now? | #SNA via @Linkurious

Linkedin inMaps discontinued : how to visualize your professional network now? | #SNA via @Linkurious | e-Xploration | Scoop.it
How to replace Linkedin inMaps and visualize your professional network with Gephi.
luiy's insight:

One of the cool not-so-known feature of Linkedin was the ability to visualize your professional network. This was provided via inMaps and allowed everyone to get a feel of the communities and influencers in his own network. On August 31 this service will be discontinued but you can still visualize your Linkedin network.

 

 

Collect your data

 

First step first. We need to collect the information about our connections and their relationships. The good news is that Linkedin has a nice API anyone can use. The bad news is that this API only allows us to see our connections and the connections between them. Just like with inMaps, we will not be able to see the individuals we are not connected to.

 

We are going to need to register a new application with Linkedin to be able to use its API. Simply go here and sign in with your account (you do have an account, right?).

 

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Text Visualization Browser | #dataviz #sna #datascience

Text Visualization Browser | #dataviz #sna #datascience | e-Xploration | Scoop.it
Text Visualization Browser
luiy's insight:
Text Visualization BrowserDeveloped by Kostiantyn Kucher and Andreas KerrenISOVIS group, Linnaeus University, Växjö, SwedenCheck out our IEEE VIS 2014 poster abstract
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DareDo's curator insight, October 30, 2014 6:07 AM

De multiples manières de visualiser des textes...

Sans doute devrions-nous réfléchir à des manières simples d'organiser nos propres textes et nos ressources.

A creuser certainement...

Stephen Dale's curator insight, November 7, 2014 11:23 AM

A Visual Survey of Text Visualization Techniques. Excellent resource.

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#Complexity in Social Networks | #algorithms #SNA

#Complexity in Social Networks | #algorithms #SNA | e-Xploration | Scoop.it
How network structure impacts consumer experience.
luiy's insight:

In the same way software has “eaten” many industries and continues to devour more, the structure of complex systems is relevant in an increasing number of subjects, from neurobiology to industrial engineering. In the consumer internet, many of the most interesting technology platforms are, at their core, networks. As with most complex systems, small changes can have large consequences, and the structure of a network can materially impact consumer experience, many times changing the core way that people interact with the service.

 

One way to think about these technology platforms is to think of any complex network as having four fundamental components:

 

- Nodes (the objects in the graph, e.g., people, things)

 

- Data/content (the thing being shared between the nodes, e.g., tweet

 

- Edges with rules (e.g., bidirectional “friend”, single-directional “follow”)

 

- Jumping functions, specifically ways to transmit the data/content from one subgroup of people to another on the same platform, usually based on rules surrounding how the edges are structured (e.g., retweeting / liking / favoriting).

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#Mining the Social Web, 2nd-Edition | #datascience #SNA #tools

#Mining the Social Web, 2nd-Edition | #datascience #SNA #tools | e-Xploration | Scoop.it
Mining-the-Social-Web-2nd-Edition - The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)
luiy's insight:

Chapter 0 - Preface

 

Chapter 1 - Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More

 

Chapter 2 - Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More

 

Chapter 3 - Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More

 

Chapter 4 - Mining Google+: Computing Document Similarity, Extracting Collocations, and More

 

Chapter 5 - Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts and More

 

Chapter 6 - Mining Mailboxes: Analyzing Who's Talking To Whom About What, How Often, and More

 

Chapter 7 - Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More

 

Chapter 8 - Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing Over RDF, and More

 

Chapter 9 - Twitter Cookbook

 

Appendix A - Virtual Machine Experience

Appendix B - OAuth Primer

Appendix C - Python & IPython Notebook Tips

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How To Detect #Communities Using Social Network Analysis | #SNA

How To Detect #Communities Using Social Network Analysis | #SNA | e-Xploration | Scoop.it
luiy's insight:

Think of communities as very similar to the segments identified in a brand’s customer segmentation model. (With demographics analysis layered on, you might even find that they’re the same.)

While direct marketing communications is often customized by segment, historically this hasn’t been something brands have done in social. But, using social network analysis and also Twitter & Facebook ad targeting, it’s possible to send specific messages to specific groups of people.

 

Powered by Pulsar TRAC these could be people engaging in a specific conversation, individuals sharing a piece of content online, or the followers of an account on Twitter. Any group of people, in essence, as long as we can define that audience through some property of its behaviour in social media – such as keyword, user bio, or location.

 

Community analysis allows brands to really understand the behavior of their audiences in a way they can’t replicate with offline, non-social data.

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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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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, 2014 6:51 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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How MIT Visualizes Supply Chain Risk | #SNA #predictive

How MIT Visualizes Supply Chain Risk | #SNA #predictive | e-Xploration | Scoop.it
MIT Supply Chain Management Director Bruce Arntzen discusses risk visualization and Sourcemap [Video]

 

How does a company keep tabs on thousands of suppliers? That’s the question Bruce Arntzen tried to answer when he started the Hi-Viz Research Project. As Executive Director of MIT’s Supply Chain Management Program, Arntzen works with corporations to find innovative solutions to supply chain problems. The idea for the Hi-Viz project came during a 2011 meeting of the Supply Chain Risk Leadership Council. A survey of attendees listed Supply Chain Visibility as the top concern. Why? With thousands of suppliers and sub-suppliers, it can be very time-consuming to find the weakest link in a supply chain. Arntzen’s solution: an automatic visualization of the end-to-end supply chain where the weakest links could be seen in real time. Watch his interview to learn how MIT and Sourcemap developed the first automated risk visualization [more details below the fold].

 

In 2015, the Hi-Viz project is partnering with actuarial data providers to provide predictive risk analytics. Sourcemap is making available inventory risk mapping as part of its enterprise software-as-a-service. Want to get involved? Learn more about the Hi-Viz project, or contact Sourcemap for a demo.

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TULIP : Data Visualization Software | #SNA #dataviz

TULIP : Data Visualization Software | #SNA #dataviz | e-Xploration | Scoop.it
Tulip is a software system dedicated to the visualization of huge graphs. It enables, 3D visualizations, 3D modifications, plugin support, support for clusters and navigation, and automatic graph drawing.
luiy's insight:

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing. 

 

Written in C++ the framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. One of the goal of Tulip is to facilitates the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.

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OpenGraphiti : Data Visualization Framework | #SNA #open #dataviz

OpenGraphiti : Data Visualization Framework | #SNA #open #dataviz | e-Xploration | Scoop.it
luiy's insight:
Description

OpenGraphiti is a free and open source 3D data visualization engine for data scientists to visualize semantic networks and to work with them. It offers an easy-to-use API with several associated libraries to create custom-made datasets. It leverages the power of GPUs to process and explore the data and sits on a homemade 3D engine.

 

YOUTUBE: https://www.youtube.com/watch?v=TE9qsYBu8MM

 

 

 

 

 

 

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Visualizing The Explosive Growth Of The #Robotics Industry | #SNA #dataviz

Visualizing The Explosive Growth Of The #Robotics Industry | #SNA #dataviz | e-Xploration | Scoop.it
Meet the overlords of your new overlords.
luiy's insight:

Over the past several years, a new wave of robot technology has offered us a glimpse at our heavily automated future, featuring drones, telepresence bots, therapeutic robots, and surgery bots that assist (or maybe take over for) doctors. The robotics industry has actually been around for a long time, with numerous companies working on warehouse automation, industrial robotics, and the like since the middle of the last century.

 

But as this interactive visualization from the Boston Consulting Group (BCG) and Quid reveals, the industry is expanding at near-warp speed.

 

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#Violence and guns in best-selling #videogames | #dataviz #opendata

#Violence and guns in best-selling #videogames | #dataviz #opendata | e-Xploration | Scoop.it
Research into connections between gun violence and video games to violence in real life has been inconclusive. As the hype for the latest in the Grand Theft Auto series builds up, a Guardian analysis of the top 50 video games sold in 2012 found more than half contain violent content labels, as assigned by an independent video game rating board (ESRB). Overall, one-third have weapons that depict real-life firearms
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Le design de la visibilité | by @Karmacoma #socialmedia #identity

Le design de la visibilité | by @Karmacoma #socialmedia #identity | e-Xploration | Scoop.it
renvoie en effet à un ensemble de pratiques hétérogènes : la participation aux débats publics, l’exposition de soi dans des récits intimes, le développement de pratiques amateurs, les bricolages (remix, sample, collage) de produits culturels standardisés, la recherche de signes de distinction, l’affirmation de choix de vie autonome, la quête de réputation, etc.
luiy's insight:

Le processus de subjectivation : l’extériorisation de soi

La première dynamique est celle de l’extériorisation de l’identité des personnes dans des signes, formulation qui recoupe, au moins partiellement, ce qui est souvent qualifié de processus de subjectivation[8]  La notion de subjectivation fait l’objet d’acceptions... [8] . Les interfaces du web 2.0 présentent toutes une entrée individuelle, une fiche de signalement, qui constitue le point de départ de toute navigation. Celle-ci enregistre certaines caractéristiques stables et durables des personnes, mais aussi et surtout des signes d’identité beaucoup plus diffus, mouvants et multiples que les participants déposent dans leurs goûts, leurs amis, leurs activités ou leurs œuvres. Ce que l’on qualifie habituellement de dynamique « expressiviste »[9]  ALLARD et VANDENBERGHE, 2003. [9] renvoie en effet à un ensemble de pratiques hétérogènes : la participation aux débats publics, l’exposition de soi dans des récits intimes, le développement de pratiques amateurs, les bricolages (remix, sample, collage) de produits culturels standardisés, la recherche de signes de distinction, l’affirmation de choix de vie autonome, la quête de réputation, etc.

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Creating a #heatmap with Photoshop (for #NodeXL) | #SNA #gephi

Creating a #heatmap with Photoshop (for #NodeXL) | #SNA #gephi | e-Xploration | Scoop.it
luiy's insight:

The method presented here shows how to manually create a heatmap of any given NodeXL network with Photoshop. Since this is not an automated process, you can actually create a heatmap out of everything (if you have enough time and creativity). For example an “infrared-image” of a human person:

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Mapping Internet #Governance | #cyberdemocracy #SNA

Mapping Internet #Governance | #cyberdemocracy #SNA | e-Xploration | Scoop.it
A collaborative map of processes that shape the evolution of the Internet
luiy's insight:

Already a pressing global issue, internet governance debates are taking place across a range of different fora often referred to as an ecosystem. With the revelations of mass government surveillance, the frequency, intensity, and stakes of internet governance debates have heightened in the last year and this trend promises to continue. At the core of such debates is the future of fundamental rights, such as freedom of expression, privacy, access to knowledge, and access to affordable internet, but also how to decision-making process can become more open, transparent, participatory, accountable, and democratic.

 

Mapping Internet Governance, a collective data compiling and mapping project, traces the relations between the internet governance events, processes, and organizing institutions. The map covers the past 2 years with an effort to better focus civil society advocacy by understanding where main discussions about global internet governance are taking place.

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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 AymericBds
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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