Orange Data for Development is an open data challenge, encouraging research teams around the world to use four datasets of anonymous call patterns of Orange’s Ivory Coast subsidiary, to help address society development questions in novel ways. The data sets are based on anonymized Call Detail Records extracted from Orange’s customer base, covering the months of December 2011 to April 2012.
Our team used the geolocation data from call detail records extracted from Orange’s customer base in order to know in which areas the customers have been moving around, to help us discover the morning and evening rush hours: the time when users were commuting between their place of residence and place of work.
Visualization
We used Python for crunching the numbers and D3.js for creating the visualization.
Bar Chart
The bar chart shows the total population density at a fixed time slot. Rush hours can be identified by the two peaks that emerge every day, one in the morning and one in the afternoon.
Choropleth
The choropleth shows how the population density flows over time, as people move from one region to another. Notice how the density increases (areas get darker) as the time gets closer to the rush hours.
Take a look!
If you want to see it running, you can either visit this link for a demo with simulated data, or clone the repo and start a local web server.
With this Plugin for Gephi, ParadigmaLabs wants to provide the community with an useful tool to analyze Twitter information. We have encapsulated all the complexity behind a simple button. A retweet is one of the main actions for information propagation, and now you can make your own analysis in real time by means of Gephi and the Retweet Monitor plugin.
It´s internal mechanisms are fairly simple. The software will connect to the TwitterStream, then apply(if desired) a content filter. All the information gathered will be displayed by Gephi, and you can then apply the standard algorithms and layouts in order to create a representative visualization.
#15oct and #ows
15th October 2011 was a world-level milestone day: Millions of people aroud the globe occupied the streets to protest against global financial crisis, influenced in a great measure by the power of social networks, essentially Twitter. The protest movement, tagged as #15o and #15oct was heavily based upon #15m (Spain) and #ows (“Occupy Wall Street”), social movements around the notion that 99% of the people is NOT responsible of the ‘financial games’ played by a minor 1% that get rich in the process of sucking their wealth from the remaining 99% (#weare99)
The Process
We present evolution through time of related Twitter activity, around 15th October 2011. Taking a Dataset of 1.2 million tweets (ranging from 13th October to 18th October), we worked to offer some global (geolocated) visualizations, local visualizations (centered around New York, San Francisco, Barcelona and Madrid) and, lastly, a visualization about how did the associated hashtags evolved in that time frame.
Read More
Análisis de Climas Emocionales
Hace mucho tiempo que trabajamos en lo que se conoce como ‘sentiment analysis’ o el análisis de la actitud del ‘emisor’ de un texto/opinión (positiva o negativa), bien sea en general, o respecto a una entidad (compañía, persona, producto, etc..). La minería emocional o ‘mood analysis’ pretende ir un paso más allá en el análisis emocional de un usuario, tratando de encontrar las emociones que provocan en él determinadas situaciones.
En los vídeos que se incluyen, se dibuja la evolución en el tiempo del ‘clima emocional’ que ha rodeado a los candidatos Mariano Rajoy y Alfredo Pérez Rubalcaba en Twitter, es decir: qué emociones subyacen en los usuarios cuando ‘tweetean’ sobre cada uno de los dos candidatos. Los estados emocionales incluidos en el motor de análisis son los siguientes: Sorpresa,Indignacion, Decepcion, Enfado, Miedo, Alegria y Esperanza Read More
Áreas Temáticas
El objetivo de las visualizaciones de candidatos por áreas en Twitter, es detectar de qué áreas de su Programa Electoral (o bien de áreas que puedan formar parte de las preocupaciones del ciudadano) se habla en mayor o menor medida en Twitter cuando un tweet se refiere a un candidato. Cada Área (Terrorismo, Inmigración, Medio Ambiente, Sanidad, Economia, Educacion, Trabajo/Paro, Medio Ambiente, Vivienda) está compuesta a su vez por pequeñas subáreas, y la visualización de ambas nos permite hacernos una idea del ‘panorama ideológico’ percibido por los usuarios de Twitter respecto a cada uno de los candidatos. Read More
Generales 20N: Conceptos y Evoluciones
En esta serie gráfica presentamos los conceptos más frecuentemente “tweeteados” por los usuarios, asociados a las elecciones o bien a los candidatos Mariano Rajoy y Alfredo Pérez Rubalcaba. Las nubes de conceptos se han generando gracias a wordle a partir de datos adquiridos en twitter desde los primeros días de Octubre (precampaña) hasta el día anterior a la jornada de reflexión (campaña). La selección de términos se ha realizado a través de un motor semántico de extracción de conceptos y entidades desarrollando conjuntamente entre Paradigma Tecnológico y HAVAS Media.
También incluimos unos gráficos de tipo “StreamGraph”, que cuentan con la particularidad.
It’s well-known that Twitter’s most powerful use is as tool for real-time journalism. Trying to understand its social connections and outstanding capacity to propagate information, we have developed a mathematical model to identify the evolution of a single tweet.
The way a tweet is spread through the network is closely related with Twitter’s retweet functionality, but retweet information is fairly incomplete due to the fight for earning credit/users by means of being the original source/author. We have taken into consideration this behavior and our approach uses text similarity measures as complement of retweet information. In addition, #hashtags and urls are included in the process since they have an important role in Twitter’s information propagation. Read More
In this video, we present the network evolution around March iPad 2 launch conversation.
Data was collected using twitter real-time API, on March 2nd, 2011, totalling around 50k tweets+retweets
After that, we used Gephi Streaming feature in tandem with its Force Atlas Layout, et voilà, Gephi instant gratification!
Roberto Maestre Martínez on 
Camilo Ramirez on