| dc.contributor.advisor | Ethan Zuckerman. | en_US |
| dc.contributor.author | Bell, Rebekah L | en_US |
| dc.contributor.other | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. | en_US |
| dc.date.accessioned | 2019-01-11T15:06:29Z | |
| dc.date.available | 2019-01-11T15:06:29Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.1/119921 | |
| dc.description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. | en_US |
| dc.description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. | en_US |
| dc.description | Cataloged from student-submitted PDF version of thesis. | en_US |
| dc.description | Includes bibliographical references (pages 67-71). | en_US |
| dc.description.abstract | 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. | en_US |
| dc.description.statementofresponsibility | by Rebekah L. Bell. | en_US |
| dc.format.extent | 71 pages | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Massachusetts Institute of Technology | en_US |
| 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. | en_US |
| dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
| dc.subject | Electrical Engineering and Computer Science. | en_US |
| dc.title | Computational support for media ecosystems research | en_US |
| dc.type | Thesis | en_US |
| dc.description.degree | M. Eng. | en_US |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.identifier.oclc | 1080935310 | en_US |
