Data portraits : aesthetics and algorithms

While interacting online, one generates a multitude of personal data trails, both textual and behavioral. The data portrait is a way to collect, condense and represent these information trails, which are often time consuming and tedious to find and grasp when read linearly across web pages or domains, into an easy, legible, and compelling visualization. This thesis presents various data portraiture techniques that generate both individual and collective portraits of users participating in online social media. The data used in generating the portraits are unstructured text and publishing timestamps of Twitter micro-blog posts, as well as aggregate RSS feeds from FriendFeed. The strategies for depicting people's online personas explored in this thesis focus on the compression, mapping and visual representation components of the visualization pipeline. The resulting portraits attempt to maintain a tight connection with the data, and be legible to viewers, but at the same time, venture to explore more expressive visual forms, and engage with the evolving aesthetics of cinematography, typography and animation.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Dragulescu, Alexandru C

Advisor dc:contributor.advisor

• Judith Donath.

• Architecture. Program in Media Arts and Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/55188

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/55188