Παραγοντοποίηση τανυστή με πληροφορίες δικτύου για τον εντοπισμό ψευδών ειδήσεων

Παπαναστασίου, Ευφροσύνη; Papanastasiou, Effrosyni

dc.contributor.author	Παπαναστασίου, Ευφροσύνη	el
dc.contributor.author	Papanastasiou, Effrosyni	en
dc.date.accessioned	2020-05-18T13:47:50Z
dc.date.available	2020-05-18T13:47:50Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/50623
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.18321
dc.rights	Αναφορά Δημιουργού-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nd/3.0/gr/	*
dc.subject	Machine learning	en
dc.subject	CP decomposition	en
dc.subject	Fake news	en
dc.subject	Tensor factorization	en
dc.subject	Social networks	en
dc.title	Παραγοντοποίηση τανυστή με πληροφορίες δικτύου για τον εντοπισμό ψευδών ειδήσεων	el
dc.title	Network-based tensor factorization with label information for fake news detection	en
heal.type	bachelorThesis
heal.classification	Machine learning	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-11-25
heal.abstract	Social media usage for news consumption has grown excessively during the last years. Along with it came the spread of large amounts of "fake news" that are deliberately written to deceive readers. Due to the serious consequences of mass misinformation, fake news detection is considered an urgent technological issue in need of automated solutions. In related literature, we can find several attempts to detect fake news only from its content, but their performance is generally considered non-satisfactory. Hence, we need to incorporate auxiliary information, coming from the network, in order to be able to identify misinformation more accurately. Our intuition is that the network connections of users that share fake news can be discriminatory enough to facilitate the detection of fake news. Consequently, the aim of this thesis is to detect fake news using network-based information and some labeled data. Firstly, we present a novel tensor-based way of representing the news pieces using only the friendships between users who have shared the news. Then, we wish to exploit this representation and a set of labels we have in hand, by developing a tensor factorization method that associates the class information of some posts with their latent representations. Finally, we end up with a unified optimization process that integrates a classification error term inside the factorization itself. Results on real-world datasets demonstrate the effectiveness of our proposed method and suggest that it can become competitive against existing state-of-the-art methods, by employing an arguably simpler approach.	en
heal.advisorName	Στάμου, Γιώργος	el
heal.committeeMemberName	Στάμου, Γιώργος	el
heal.committeeMemberName	Παπασπύρου, Νικόλαος	el
heal.committeeMemberName	Σταφυλοπάτης, Ανδρέας-Γεώργιος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	68 σ.
heal.fullTextAvailability	false