Computational support for media ecosystems research

This thesis summarizes the design, implementation, and evaluation of two end-user web tools for automated content analysis of online news data. The first tool is a visualization that displays neural word embeddings data, allowing a user to explore words used in similar contexts within a text corpus. The second tool is an interface that guides users through a supervised machine learning pipeline, enabling novices to train their own binary classification models to detect the presence of a specific frame within the text of a news story. The visualization and interface were evaluated in a user study and think-aloud test respectively. These tools were developed for integration into Media Cloud, an open-source platform for media analysis, which is part of a larger effort to facilitate and advance media ecosystems research.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Bell, Rebekah L

Advisor dc:contributor.advisor

• Ethan Zuckerman.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written 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/1721.1/119921

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119921