Master Thesis Online
Fake news detection using machine learning
Lorent, Simon 
Promotor(s) :
Ittoo, Ashwin
Date of defense : 9-Sep-2019/10-Sep-2019 • Permalink : http://hdl.handle.net/2268.2/8416
Details
| Title : | Fake news detection using machine learning |
| Author : | Lorent, Simon
|
| Date of defense : | 9-Sep-2019/10-Sep-2019 |
| Advisor(s) : | Ittoo, Ashwin
|
| Committee's member(s) : | Gribomont, Pascal
Geurts, Pierre
|
| Language : | English |
| Number of pages : | 91 |
| Keywords : | [en] Fake news [en] machine learning [en] detection [en] Attention Mechanism [en] LSTM [en] NLP |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Target public : | Researchers Professionals of domain Student General public Other |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en science des données, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] For some years, mostly since the rise of social media, fake news have become a society
problem, in some occasion spreading more and faster than the true information. In this
paper I evaluate the performance of Attention Mechanism for fake news detection on
two datasets, one containing traditional online news articles and the second one news
from various sources. I compare results on both dataset and the results of Attention
Mechanism against LSTMs and traditional machine learning methods. It shows that
Attention Mechanism does not work as well as expected. In addition, I made changes
to original Attention Mechanism paper, by using word2vec embedding, that proves to
works better on this particular case.
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