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Title: Brainwashing 2.0: Bias and double standard in western social media and news outlets.
Authors: ABDELLATIF, ABdelraouf
Keywords: Artificial Intelligence
Machine Learning
Deep Learning
Artificial Neural Networks
Natural Language Processing
Support Vector Machines
Mean Square Error
Least Absolute Shrinkage And Selection Operator
L1 norms
L2 norms
Continuous Bag Of Words
Global Vectors For Word Representation
Proportional Hazards Regression
K-Nearest Neighbors
Principal Component Analysis
t-Distributed Stochastic Neighbor Embedding
Long Short-Term Memory
Gated Recurrent Units
Radial Basis Function Networks
Generative Adversarial Networks
Self-Organizing Maps
Term Frequency
Inverse Document Frequency
Term Frequency-Inverse Document Frequency
Automatic Speech Recognition
Conditional Random Fields
Bidirectional Long Short-Term Memory
Stanford Question Answering Dataset
Question Answering
Named Entity Recognition
Document Understanding Conference
Issue Date: 2023
Abstract: ABSTRACT : In this study, we leverage techniques from machine learning, deep learning, natural language processing and sentiment analysis to investigate the presence and impact of biases in the coverage of the Western news platforms and social media sources. Our focus centers around detecting and analyzing biases, differentiating between intentional and accidental misinformation, and interpreting how these biases may influence public perception, followed by sentiment analysis to study the variation of sentiments over time. This research report delves into the intersection of Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), sentiment analysis, and media bias detection. The study elucidates how these innovative technologies can be leveraged to scrutinize and interpret media content, ensuring unbiased information dissemination..*** RÉSUMÉ : Dans cette étude, nous exploitons des techniques issues de l’apprentissage automatique, de l’apprentissage profond, du traitement du langage naturel et de l’analyse des sentiments pour examiner la présence et l’impact des biais dans la couverture des plateformes d’information occidentales et des sources de médias sociaux. Notre attention se concentre sur la détection et l’analyse des biais, en différenciant les informations erronées intentionnelles et accidentelles, et en interprétant comment ces biais peuvent influencer la perception du public, suivis par une analyse des sentiments pour étudier la variation des sentiments dans le temps. Ce mémoire se plonge dans l’intersection de l’Apprentissage Automatique (AA), de l’Apprentissage Profond (AP), du Traitement du Langage Naturel (TLN), de l’analyse des sentiments et de la détection des biais médiatiques. L’étude éclaire comment ces technologies innovantes peuvent être exploitées pour scruter et interpréter le contenu médiatique, assurant une diffusion de l’information sans biais.
Description: Encadrant : Dr Sid Ahmed Benabderrahmane / Co-Encadrant : Pr Sidi Mohammed Benslimane
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