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Title:
Analiza učinkovitosti algoritmov pri prepoznavanju in odstranjevanju sovražnega govora in lažnih informacij
Authors:
ID
Feltrin, Alen
(
Author
)
ID
Polančič, Gregor
(
Mentor
)
More about this mentor...
Files:
UN_Feltrin_Alen_2025.pdf
(2,25 MB)
MD5: 9A4879603BECC974900F4649661D6AAD
Language:
Slovenian
Work type:
Bachelor thesis/paper
Typology:
2.11 - Undergraduate Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
Diplomsko delo preučuje učinkovitost algoritmov umetne inteligence pri zaznavanju sovražnega govora in dezinformacij v angleškem in slovenskem jeziku. Ocenjeni so bili modeli BERT, IMSyPP in SVC na javnih podatkovnih nizih. Rezultati potrjujejo prednost globokih modelov v angleščini, medtem ko v slovenščini učinkovitost omejuje pomanjkanje lokalnega prilagajanja. Analiza razkriva vpliv jezikovne pristranskosti ter potrjuje pomen metod razložljive umetne inteligence (LIME) za večjo transparentnost in zaupanje pri moderaciji vsebin.
Keywords:
umetna inteligenca
,
sovražni govor
,
dezinformacije
,
BERT
,
družbena omrežja
Place of publishing:
Maribor
Year of publishing:
2025
PID:
20.500.12556/DKUM-95315
Publication date in DKUM:
03.11.2025
Views:
0
Downloads:
7
Metadata:
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:
12.09.2025
Secondary language
Language:
English
Title:
Analysis of the effectiveness of algorithms in detecting and removing hate speech and misinformation
Abstract:
The thesis examines the effectiveness of artificial intelligence algorithms in detecting hate speech and disinformation in English and Slovenian. Models BERT, IMSyPP, and SVC were evaluated using public datasets. Results confirm the superiority of deep models in English, while performance in Slovenian is limited by the lack of local adaptation. The analysis highlights the impact of linguistic bias and confirms the value of explainable AI methods (LIME) in improving transparency and trust in content moderation.
Keywords:
artificial intelligence
,
hate speech
,
misinformation
,
BERT
,
social media
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