2026-09-19T08:06:50Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1307162026-06-06T00:48:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
James Glass.
Tangri, Kunal.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-05-24T19:52:47Z
2021-05-24T19:52:47Z
2021
2021
https://hdl.handle.net/1721.1/130716
1251801786
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 65-71).
Fake news is a widespread problem due to the ease of information spread online, and its ability to deceive large populations with intentionally false information. The damage it causes is exacerbated by its political links and loaded language, which make it polarizing in nature, and preys on peoples' psychological biases to make it more believable and viral. In order to dampen the influence of fake news, organizations have begun to manually tag, or develop systems to automatically tag, false and biased information. However, manual efforts struggle to keep up with the rate at which content is published, and automated methods provide very little explanation to convince people of their validity. In an effort to address these issues, we present a system to classify media sources' political bias and factuality levels by analyzing the language that gives fake news its contagious and damaging power. Additionally, we survey potential approaches for increasing the transparency of black-box fake news detection methods.
by Kunal Tangri.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
71 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Using natural language to predict bias and factuality in media with a study on rationalization
Thesis
application/pdf
2021-05-24T19:52:47Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Fake news is a widespread problem due to the ease of information spread online, and its ability to deceive large populations with intentionally false information. The damage it causes is exacerbated by its political links and loaded language, which make it polarizing in nature, and preys on peoples' psychological biases to make it more believable and viral. In order to dampen the influence of fake news, organizations have begun to manually tag, or develop systems to automatically tag, false and biased information. However, manual efforts struggle to keep up with the rate at which content is published, and automated methods provide very little explanation to convince people of their validity. In an effort to address these issues, we present a system to classify media sources' political bias and factuality levels by analyzing the language that gives fake news its contagious and damaging power. Additionally, we survey potential approaches for increasing the transparency of black-box fake news detection methods.</Abstract>
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