2026-09-18T19:09:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1216892026-06-06T00:56:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
James R. Glass and Mitra Mohtarami.
Xu, Brian(Brian W.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-15T20:34:26Z
2019-07-15T20:34:26Z
2019
2019
https://hdl.handle.net/1721.1/121689
1102057862
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-80).
Factually incorrect claims on the web and in social media can cause considerable damage to individuals and societies by misleading them. As we enter an era where it is easier than ever to disseminate "fake news" and other dubious claims, automatic fact checking becomes an essential tool to help people discern fact from fiction. In this thesis, we focus on two main tasks: fact checking which involves classifying an input claim with respect to its veracity, and stance detection which involves determining the perspective of a document with respect to a claim. For the fact checking task, we present Bidirectional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) based models and conduct our experiments on the LIAR dataset [Wang, 2017], a recently released fact checking task. Our model outperforms the state of the art baseline on this dataset. For the stance detection task, we present bag of words (BOW) and CNN based models in hierarchy schemes. These architectures are then supplemented with an adversarial domain adaptation technique, which helps the models overcome dataset size limitations. We test the performance of these models by using the Fake News Challenge (FNC) [Pomerleau and Rao, 2017], the Fact Extraction and VERification (FEVER) [Thorne et al., 2018], and the Stanford Natural Language Inference (SNLI) [Bowman et al., 2015] datasets. Our experiments yielded a model which has state of the art performance on FNC target data by using FEVER source data coupled with adversarial domain adaptation [Xu et al., 2018].
by Brian Xu.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
80 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Combating fake news with adversarial domain adaptation and neural models
Thesis
application/pdf
2019-07-15T20:34:22Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Factually incorrect claims on the web and in social media can cause considerable damage to individuals and societies by misleading them. As we enter an era where it is easier than ever to disseminate "fake news" and other dubious claims, automatic fact checking becomes an essential tool to help people discern fact from fiction. In this thesis, we focus on two main tasks: fact checking which involves classifying an input claim with respect to its veracity, and stance detection which involves determining the perspective of a document with respect to a claim. For the fact checking task, we present Bidirectional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) based models and conduct our experiments on the LIAR dataset [Wang, 2017], a recently released fact checking task. Our model outperforms the state of the art baseline on this dataset. For the stance detection task, we present bag of words (BOW) and CNN based models in hierarchy schemes. These architectures are then supplemented with an adversarial domain adaptation technique, which helps the models overcome dataset size limitations. We test the performance of these models by using the Fake News Challenge (FNC) [Pomerleau and Rao, 2017], the Fact Extraction and VERification (FEVER) [Thorne et al., 2018], and the Stanford Natural Language Inference (SNLI) [Bowman et al., 2015] datasets. Our experiments yielded a model which has state of the art performance on FNC target data by using FEVER source data coupled with adversarial domain adaptation [Xu et al., 2018].</Abstract>
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