In today’s world, where online news is so widespread, various methods have been developed in order to provide users with personalized news recommendations. Wonderful accomplish ments have been made when it comes to providing readers with everything that could attract their attention. While accuracy is critical in news recommendation, other factors, such as diversity, novelty, and reliability, are essential in satisfying the readers’ satisfaction. In fact, technological advancements bring additional challenges which might have a detrimental im pact on the news domain. Therefore, researchers need to consider the new threats in the development of news recommendations. Fake news, in particular, is a hot topic in the media today and a new threat to public safety. This work presents a modularized system capable of recommending news to the user and detecting fake news, all while helping users become more aware of this issue. First, we suggest FANAR, FAke News Aware Recommender system, a modification to news recommendation algorithms that removes untrustworthy persons from the candidate user’s neighbourhood. To do this, we created a probabilistic model, the Beta Trust model, to calculate user rep utation. For the recommendation process, we employed Graph Neural Networks. Then, we propose EXMULF, EXplainable MUltimodal Content-based Fake News Detection Sys tem. It is tasked with the veracity analysis of information based on its textual content and the associated image, together with an Explainable AI (XAI) assistant that is tasked with combating the spread of fake news. Finally, we try to raise awareness about fake news by providing personalized alerts based on user reliability. To fulfill the objective of this work, we build a new dataset named FNEWR. Our exper iments reveal that EXMULF outperforms 10 state-of-the-art fake news detection models in terms of accuracy. It is also worth mentioning that FANAR , which takes into account vi sual information in news, outperforms competing approaches based only on textual content. Furthermore, it reduces the amount of fake news found in the recommendations list / De nos jours, où les actualités en ligne sont si répandues, diverses méthodes ont été dé veloppées afin de fournir aux utilisateurs des recommandations d’actualités personnalisées. De merveilleuses réalisations ont été faites lorsqu’il s’agit de fournir aux lecteurs tout ce qui pourrait attirer leur attention. Bien que la précision soit essentielle dans la recommandation d’actualités, d’autres facteurs, tels que la diversité, la nouveauté et la fiabilité, sont essentiels pour satisfaire la satisfaction des lecteurs. En fait, les progrès technologiques apportent des défis supplémentaires qui pourraient avoir un impact négatif sur le domaine de l’information. Par conséquent, les chercheurs doivent tenir compte des nouvelles menaces lors de l’élabo ration de nouvelles recommandations. Les fausses nouvelles, en particulier, sont un sujet brûlant dans les médias aujourd’hui et une nouvelle menace pour la sécurité publique. Au vu des faits mentionnés ci-dessus, ce travail présente un système modulaire capable de détecter les fausses nouvelles, de recommander des nouvelles à l’utilisateur et de les aider à être plus conscients de ce problème. Tout d’abord, nous suggérons FANAR, FAke News Aware Recommender system, une modification d’algorithme de recommandation d’actuali tés qui élimine les personnes non fiables du voisinage de l’utilisateur candidat. A cette fin, nous avons créé un modèle probabiliste, Beta Trust Model, pour calculer la réputation des utilisateurs. Pour le processus de recommandation, nous avons utilisé Graph Neural Net works. Ensuite, nous proposons EXMULF, EXplainable MUltimodal Content-based Fake News Detection System. Il s’agit de l’analyse de la véracité de l’information basée sur son contenu textuel et l’image associée, ainsi qu’un assistant d’intelligence artificielle Explicable (XAI) pour lutter contre la diffusion de fake news. Enfin, nous essayons de sensibiliser aux fake news en fournissant des alertes personnalisées basées sur le profil des utilisateurs. Pour remplir l’objectif de ce travail, nous construisons un nouveau jeu de données nommé FNEWR. Nos résultats expérimentaux montrent qu’EXMULF surpasse 10 modèles de pointe de détection de fausses nouvelles en termes de précision. Aussi, FANAR qui prend en compte les informations visuelles dans les actualités, surpasse les approches concurrentes basées uniquement sur le contenu textuel. De plus, il permet de réduire le nombre de fausses nouvelles dans la liste des recommandations.

With the vast amount of information available on the Internet today, helping users find relevant content has become a prioritized task in many software products that recommend news articles. One such product is Opera for Android, which has a news feed containing articles the user may be interested in. In order to easily determine what articles to recommend, they can be categorized by the topics they contain. One approach of categorizing articles is using Machine Learning and Natural Language Processing (NLP). A commonly used model is Latent Dirichlet Allocation (LDA), which finds latent topics within large datasets of for example text articles. An extension of LDA is hierarchical Latent Dirichlet Allocation (hLDA) which is an hierarchical variant of LDA. In hLDA, the latent topics found among a set of articles are structured hierarchically in a tree. Each node represents a topic, and the levels represent different levels of abstraction in the topics. A further extension of hLDA is constrained hLDA, where a set of predefined, constrained topics are added to the tree. The constrained topics are extracted from the dataset by grouping highly correlated words. The idea of constrained hLDA is to improve the topic structure derived by a hLDA model by making the process semi-supervised. The aim of this thesis is to create a hLDA and a constrained hLDA model from a dataset of articles provided by Opera. The models should then be evaluated using the novel metric word frequency similarity, which is a measure of the similarity between the words representing the parent and child topics in a hierarchical topic model. The results show that word frequency similarity can be used to evaluate whether the topics in a parent-child topic pair are too similar, so that the child does not specify a subtopic of the parent. It can also be used to evaluate if the topics are too dissimilar, so that the topics seem unrelated and perhaps should not be connected in the hierarchy. The results also show that the two topic models created had comparable word frequency similarity scores. None of the models seemed to significantly outperform the other with regard to the metric.