2026-09-18T20:13:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/551882022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Judith Donath.
Dragulescu, Alexandru C
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2010-05-25T21:01:46Z
2010-05-25T21:01:46Z
2009
2009
http://hdl.handle.net/1721.1/55188
608305541
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 91-93).
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.
by Alexandru C. Dragulescu.
S.M.
93 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Data portraits : aesthetics and algorithms
Thesis
application/pdf
Publication
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unknown
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<Abstract>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.</Abstract>
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