background preloader

Identifying the Pathways for Meaning Circulation using Text Network Analysis

Identifying the Pathways for Meaning Circulation using Text Network Analysis
By Dmitry Paranyushkin, Nodus Labs. Published October 2011, Berlin. Abstract: In this work we propose a method and algorithm for identifying the pathways for meaning circulation within a text. This is done by visualizing normalized textual data as a graph and deriving the key metrics for the concepts and for the text as a whole using network analysis. Keywords: network text analysis, text, network, meaning, narrative, discourse, language understanding, semantics, structure, system, semantic networks, context, cognition, interpretation, graph, diagram, visualization, interface, reading, writing, image Any text can be represented as a network. The use of diagrammatic approach to better understand text and analyze its narrative structures is not new. Later work, most notably “Coding Choices for Textual Analysis” (Carley, 1993) focused on using map analysis to extract the main concepts from the texts and relations between them. The resulting text would look like this: Related:  Gaphi

80legs export to Gephi CSV | 80Graph The Tidy Street Project | 1 Nov Click to enlarge I went to see the new documentary Urbanized—the third film in Gary Hustwit’s trilogy starting with Helvetica and followed by Objectified—which looks at city planning issues and stresses the importance of intelligent urban design for the immediate future when 75% of the population is estimated to inhabit cities by 2050. It’s a great film and I highly recommend it. There’s a lot more that could be said about the film, but instead I wanted to share a project that was featured and relates more to art, design, and typography: The Tidy Street Project. During March and April 2011, participating households on Tidy Street, in Brighton, UK, recorded their electricity consumption. Definitely a fun way to get people involved and interested. The Tidy Street Project is part of CHANGE, an EPSRC funded research collaboration between The Open University, Goldsmiths, Sussex University and Nottingham University.

Using Metadata to find Paul Revere - Kieran Healy London, 1772. I have been asked by my superiors to give a brief demonstration of the surprising effectiveness of even the simplest techniques of the new-fangled Social Networke Analysis in the pursuit of those who would seek to undermine the liberty enjoyed by His Majesty’s subjects. This is in connection with the discussion of the role of “metadata” in certain recent events and the assurances of various respectable parties that the government was merely “sifting through this so-called metadata” and that the “information acquired does not include the content of any communications”. The analysis in this report is based on information gathered by our field agent Mr David Hackett Fischer and published in an Appendix to his lengthy report to the government. Rest assured that we only collected metadata on these people, and no actual conversations were recorded or meetings transcribed. Here is what the data look like. The organizations are listed in the columns, and the names in the rows.

List of power stations in Germany The following page lists all power stations in Germany. For traction current, see List of installations for 15kV AC railway electrification in Germany, Austria and Switzerland. Nuclear[edit] Thermal[edit] Fossil[edit] List is incomplete! Hydroelectric[edit] Pumped-storage hydroelectric[edit] Photovoltaic[edit] See also[edit] References[edit] Spreadsheet converts tweets for social network analysis in Gephi EDIT 05/15/13: I’ve posted two scripts, one in PHP and one in Python, that overcome the main limitation of this spreadsheet–they pull in all mentioned names rather than just the first one. Download one or both here. If you’ve ever wanted to visualize Twitter networks but weren’t sure how to get the tweets into the right format, this spreadsheet I’ve been using in my classes might be worth a try. It prepares Twitter data for importing into Gephi, an open-source network visualization platform. It requires a little cutting and pasting, but once you get the hang of it you’ll be visualizing social network data in no time. Download the file and open it locally in Excel or OpenOffice to add your own data (right now it uses some of my recent tweets as example data). Here is a network graph of the example data.

Dbpedia DBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia, and to link the different data sets on the Web to Wikipedia data. We hope that this work will make it easier for the huge amount of information in Wikipedia to be used in some new interesting ways. Furthermore, it might inspire new mechanisms for navigating, linking, and improving the encyclopedia itself. Upcoming Events News Call for Ideas and Mentors for GSoC 2014 DBpedia + Spotlight joint proposal (please contribute within the next days)We started to draft a document for submission at Google Summer of Code 2014: are still in need of ideas and mentors. The DBpedia Knowledge Base Knowledge bases are playing an increasingly important role in enhancing the intelligence of Web and enterprise search and in supporting information integration. Within the

Exploring Hollywood values through IMDB genres and tags | Virostatiq A typical Hollywood story always portraits life in a twisted way. Movies are infused with values. There are typical stories: justice always prevails in the end, even if it means the death of a good guy; the coming-of-age story, in which hero becomes a man, the revenge story, in which the hero is wronged in the beginning, and must regain his life and justice in the course of the film. In American movies, family values are all-important, and so on. These values are interrelated in the movie world, but what is their importance relative to other values? Is war a good or a bad thing, as portrayed in the movies? There happens to be a treasure trove of useful information on IMDB to visualize these relations. It looks like this (click image to launch interactive page): Roughy 15,000 movies, as presented on IMDB. If a circle is bigger, it means it has more connections (movies associated with it). On the other hand, California, as represented in movies, seems much more family-oriented.

SOCIETY network analysis Social network analysis (SNA) is the analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizations, sexual relationships, etc.)[1][2] These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines. Overview[edit] Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, and sociolinguistics and is now commonly available as a consumer tool.[3][4][5][6] Metrics[edit] Connections[edit] Distributions[edit] Segmentation[edit] Practical applications[edit]