Identification of troll vulnerable tergets in online social networks (Master thesis)
“Trolling” describes a range of antisocial online behaviors that aim at disrupting the normal operation of online social networks and media. Combating trolling is an important problem in the online world. Existing approaches rely on human-based or automatic mechanisms for identifying trolls and troll posts. In this work, we take a novel approach to the trolling problem: our goal is to identify the targets of the trolls, so as to prevent trolling before it happens. We thus define the troll vulnerability prediction problem, where given a post we aim at predicting whether it is vulnerable to trolling. Towards this end, we define a novel troll vulnerability metric of how likely a post is to be attacked by trolls, and we construct models that use features from the content and the history of the post for the prediction. Our experiments with real data from Reddit demonstrate that our approach is successful in recalling a large fraction of the troll-vulnerable posts.
|Institution and School/Department of submitter:||Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Η/Υ & Πληροφορικής|
|Subject classification:||On- line trolls|
|Keywords:||Διαδικτυακά τρολς,Διαδικτυακά κοινωνικά δίκτυα,Ευπαθοί στόχοι,Βαθμός ευπάθειας σε τρολς,Online trolls,Online social networks,Troll vulnerable targets,Troll vulnerability rank|
|Appears in Collections:||Διατριβές Μεταπτυχιακής Έρευνας (Masters)|
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|Μ.Ε. ΤΣΑΝΤΑΡΛΙΩΤΗΣ ΠΑΡΑΣΚΕΥΑΣ 2016.pdf||1.08 MB||Adobe PDF||View/Open|
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