Computational support for media ecosystems research
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | http://hdl.handle.net/1721.1/119921 |
| _version_ | 1811094033249861632 |
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| author | Bell, Rebekah L |
| author2 | Ethan Zuckerman. |
| author_facet | Ethan Zuckerman. Bell, Rebekah L |
| author_sort | Bell, Rebekah L |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T15:54:26Z |
| format | Thesis |
| id | mit-1721.1/119921 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:54:26Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1199212019-04-12T21:24:14Z Computational support for media ecosystems research Bell, Rebekah L Ethan Zuckerman. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 67-71). 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. by Rebekah L. Bell. M. Eng. 2019-01-11T15:06:29Z 2019-01-11T15:06:29Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119921 1080935310 eng 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. http://dspace.mit.edu/handle/1721.1/7582 71 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Bell, Rebekah L Computational support for media ecosystems research |
| title | Computational support for media ecosystems research |
| title_full | Computational support for media ecosystems research |
| title_fullStr | Computational support for media ecosystems research |
| title_full_unstemmed | Computational support for media ecosystems research |
| title_short | Computational support for media ecosystems research |
| title_sort | computational support for media ecosystems research |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119921 |
| work_keys_str_mv | AT bellrebekahl computationalsupportformediaecosystemsresearch |