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Title:Primerjava metod jezikovnih tehnologij za odkrivanje lažnih novic : magistrsko delo
Authors:ID Lovrenčič, Nejc (Author)
ID Bošković, Borko (Mentor) More about this mentor... New window
ID Brest, Janez (Co-mentor)
Files:.pdf MAG_Lovrencic_Nejc_2022.pdf (1,36 MB)
MD5: 142469BCA8F08AD96D4072F7A896F269
PID: 20.500.12556/dkum/1fea2b6e-c0b0-4627-ad60-e99029c93d6a
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Socialna omrežja in tradicionalni viri novic imajo velik vpliv na razmišljanje ter dejanja posameznikov v družbi. Napačna ali izmišljena dejstva in lažne novice lahko zato povzročijo veliko škodo. V sklopu magistrskega dela smo primerjali metode Naivni Bayes, logistično regresijo, nevronsko mrežo z dolgim kratkoročnim spominom in graf konvolucijsko nevronsko mrežo za odkrivanje lažnih novic. S preučitvijo sorodne literature in primerjavo metod smo ugotovili, da je težko prepoznati lažne novice zgolj s klasifikacijo besedila. Pri klasifikaciji novic na dva razreda se je najbolje izkazal logistična regresija, pri klasifikaciji na šest razredov pa nevronska mreža LSTM.
Keywords:jezikovne tehnologije, nevronske mreže, lažne novice, klasifikacija besedila
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Lovrenčič]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (IX, 40 f.))
PID:20.500.12556/DKUM-81518 New window
UDC:004.8:519.76(043.2)
COBISS.SI-ID:110205187 New window
Publication date in DKUM:11.05.2022
Views:696
Downloads:73
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:07.04.2022

Secondary language

Language:English
Title:Comparison of natural language processing methods for detecting fake news
Abstract:Social networks and traditional news sources have a great influence on individuals' thinking and actions. False and made-up facts, as well as fake news, can therefore cause a lot of damage. As a part of the master's thesis, we compared Naive Bayes, logistic regression, long short-term memory neural network, and graph convolutional network for news classification into two and six classes. By studying related literature and executing method comparisons, we have figured out that it is difficult to identify fake news simply by using text classification methods. When classifying the news into two classes, we achieved the best results using logistic regression, while LSTM neural network proved to be the best when classifying news into the six classes.
Keywords:natural language processing, neural networks, fake news, text classification


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