Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.Comment: In 28th ACM Conference on Hypertext and Social Media (ACM HyperText 2017

Large scale crowdsourcing and characterization of Twitter abusive behavior

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels.By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.Accepted manuscrip

Ανάλυση συμπεριφοράς χρηστών στον κοινωνικό ιστό με τεχνικές εξόρυξης γνώσης

The emergence of social media platforms changed drastically the way that people communicate. Due to the always-on always-connected devices, social interactions have migrated online, and social media has become an integral part of people's everyday life. The preoccupation with social media has resulted to an overwhelming sentiment expression. Users express opinions on various topics, while also emotional responses are transferred with diverse intensity. At the same time, however, new instantiations of negative interactions have arisen, including abusive behavior among online users. As new generations engage more with social media, abusive incidents are alas increasing, reaching epidemic levels. Since today's hyper-connected society allows human expression to occur anytime, anywhere, and instantly, the study of online human behavior will result to valuable knowledge discovery. Such discovered knowledge can empower new web services and applications with easily interpretable and comprehensible conclusions for the end users. Meanwhile, efficient methods should be developed so that the early detection and prevention of abusive behaviors become possible. This thesis addresses three problems: (i) sentiment detection from online text resources, (ii) discovery of abusive behaviors in social media, and (iii) empowering services with social, spatial, and temporal knowledge. Methods for data understanding, modeling, and knowledge extraction are proposed and go beyond the state of the art. As a source of information social media are mainly considered, spanning from microblogs and online social networks to content sharing platforms and location-based social networks. Both lexicon-based and machine learning methods are presented, either separately or under a hybrid scheme, to detect human opinions and emotions. To cover a wide human emotional spectrum, basic emotions, social ones, and those that characterize general affective states are analyzed. Moreover, in order to better conceptualize the structure of human emotion and the prevailing differences between the online and offline social interactions, an empirical examination of the emotion experience in computer mediated (online) and face-to-face (offline) social interactions takes place. A thorough study on diverse communities is presented which analyzes the properties of abusive users, the content they post, and how they differ from legitimate social media users. A robust methodology for extracting text, user, and network based attributes is proposed, studying the properties of bullies and aggressors, and what features distinguish them from regular users. In addition to measuring and modeling human sentiment expression and abusive behavior on the social web, the relationships that exist between social media users' activity and the real-world events are considered. At the same time, new services and applications are designed and developed that permit both the validation of the aforementioned methods and the imprinting of the discovered knowledge in an attractive and comprehensive way for the end-users. Overall, this thesis improves understanding of human sentiment expression in social media and reveals online abusive users and behaviors. New services and applications are built to connect the users' activity in social media with real-world events by considering social, spatial, and time-related characteristics.Τα κοινωνικά μέσα αποτελούν αναπόσπαστο κομμάτι της καθημερινότητας στη σύγχρονη κοινωνία καθώς επιτρέπουν την κοινωνική ψηφιακή αλληλεπίδραση ανάμεσα στους χρήστες τους. Ο ρυθμός παραγωγής πληθώρας πληροφοριών καθίσταται ραγδαίος, για παράδειγμα λόγω της έκφρασης απόψεων, άσκησης κριτικής, ή της παροχής πληροφορίας για κάθε είδους θέματα. Συνεπώς, η μελέτη της συμπεριφοράς των χρηστών κατά τη δραστηριοποίησή τους στο λεγόμενο διαδικτυακό κόσμο αποτελεί αναμφισβήτητα ιδιαίτερα ενδιαφέρον και προκλητικό πεδίο έρευνας. Δεδομένης της καθημερινής ενασχόλησης και αλληλεπίδρασης μέσω του διαδικτύου, παραβατικές και καταχρηστικές συμπεριφορές παρατηρούνται ολοένα και περισσότερο. Η έκφραση απόψεων με επιθετικό/προσβλητικό τρόπο, η από πρόθεση προσβολή και υποβίβαση ατόμων, και η εσκεμμένη περιθωριοποίηση αποτελούν ένα δείγμα τέτοιων συμπεριφορών. Η ανάλυση, η κατανόηση, και ο χαρακτηρισμός της συμπεριφοράς των χρηστών αυτών μπορούν να συμβάλλουν σημαντικά τόσο στην επίλυση κοινωνικών προβλημάτων, όσο και στον έγκαιρο εντοπισμό φαινομένων μεγάλης επιρροής. Πιο συγκεκριμένα η διδακτορική διατριβή επικεντρώνεται σε τρεις επιμέρους θεματικούς άξονες: (i) στον εντοπισμό απόψεων και συναισθημάτων λαμβάνοντας υπόψη τη δραστηριότητα των χρηστών σε διάφορα κοινωνικά μέσα, (ii) στον εντοπισμό παρεμβατικών/καταχρηστικών συμπεριφορών ως αναδυόμενα φαινόμενα που οφείλονται στην απεριόριστη πρόσβαση και τη συνεχή συμμετοχή των χρηστών στα κοινωνικά μέσα, και (iii) στη σύνοψη και ενοποίηση προτύπων κοινωνικής δραστηριότητας και αλληλεπίδρασης. Στο σύνολό της η διατριβή αυτή στοχεύει στην ανάλυση της συμπεριφοράς των χρηστών του διαδικτύου εστιάζοντας κυρίως σε ένα σύνολο ιδιαιτέρως δημοφιλών κοινωνικών μέσων, όπως Twitter, Facebook, YouTube, και Foursquare. Η διαφορετική στόχευση των επιμέρους κοινωνικών μέσων, καθώς και των δεδομένων που παράγονται σε αυτά, συμβάλλουν στην εξαγωγή μιας πληθώρας γνώσεων και πληροφορίας χρήσιμης για την καλύτερη κατανόηση της συμπεριφοράς των χρηστών κατά τη ψηφιακή τους δραστηριότητα. Νέες μεθοδολογίες προτείνονται με στόχο την ανάλυση και τον εντοπισμό των εκφραζόμενων απόψεων και συναισθημάτων έτσι όπως αυτά αποτυπώνονται στα εκάστοτε κοινωνικά μέσα επιτυγχάνοντας να βελτιώσουν την απόδοση των υφιστάμενων μεθοδολογιών. Παράλληλα πραγματοποιείται μελέτη της δομής του συναισθήματος λαμβάνοντας υπόψη τις κατά πρόσωπο αλληλεπιδράσεις, καθώς και αυτές που λαμβάνουν χώρα κατά τη δραστηριοποίηση των χρηστών στα κοινωνικά μέσα, έχοντας ως πληροφορία και στις δύο περιπτώσεις τη ρητή δήλωση των συναισθηματικών εμπειριών από τους ίδιους τους χρήστες. Στόχος της μελέτης αυτής είναι η εμβάθυνση στην κατανόηση του τρόπου διαμόρφωσης των συναισθημάτων των ανθρώπων όταν συμμετέχουν σε κοινωνικές δραστηριότητες τόσο στον διαδικτυακό όσο και στον φυσικό κόσμο. Δεδομένης της αύξησης την περιπτώσεων εκδήλωσης επιθετικών και εκφοβιστικών συμπεριφορών στο διαδίκτυο, στα πλαίσια της διδακτορικής διατριβής μελετώνται διαδικασίες και μέθοδοι κατάλληλοι για την έγκαιρη ανίχνευση αυτών. Αρχικά, πραγματοποιείται μελέτη των επιθετικών και εκφοβιστικών συμπεριφορών τόσο για την καλύτερη κατανόηση των φαινομένων αυτών όσο και για τον εντοπισμό ενός συνόλου αντιπροσωπευτικών χαρακτηριστικών, όπως για παράδειγμα χαρακτηριστικά που βασίζονται στο ίδιο το κείμενο, στο προφίλ του χρήστη, ή ακόμη και στον τρόπο αλληλεπίδρασης με το κοινωνικό του δίκτυο, στοχεύοντας στη συνέχεια στην ανάπτυξη μεθοδολογίας κατάλληλης για τον έγκαιρο και αυτόματο εντοπισμό τέτοιων συμπεριφορών. Τέλος, εκτός από την αποτίμηση και τη μοντελοποίηση της συμπεριφοράς των ανθρώπων στο διαδίκτυο, νέα συστήματα και εφαρμογές σχεδιάζονται και δημιουργούνται με στόχο την περαιτέρω βελτίωση τόσο της διαδικτυακής όσο και της δραστηριότητας των ατόμων μίας κοινωνίας στον φυσικό κόσμο

Catching them red-handed: Real-time aggression detection on social media

Aggression on social media has evolved into a major point of concern. However, recently proposed machine learning (ML) approaches to detect various types of aggressive behavior fall short, due to the fast and increasing pace of content generation as well as evolution of such behavior over time. This work introduces the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming ML paradigm. This method adapts its ML binary classifiers in an incremental fashion, while receiving new annotated examples, and achieves similar performance as batch-based ML models, with 82-93% accuracy, precision, and recall. Experimental analysis on real Twitter data reveals how this framework, implemented in Spark Streaming, easily scales to process millions of tweets in minutes

A Streaming Machine Learning Framework for Online Aggression Detection on Twitter

The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. In this work, we introduce the first, practical, real-time framework for detecting aggression on Twitter via embracing the streaming machine learning paradigm. Our method adapts its ML classifiers in an incremental fashion as it receives new annotated examples and is able to achieve the same (or even higher) performance as batch-based ML models, with over 90% accuracy, precision, and recall. At the same time, our experimental analysis on real Twitter data reveals how our framework can easily scale to accommodate the entire Twitter Firehose (of 778 million tweets per day) with only 3 commodity machines. Finally, we show that our framework is general enough to detect other related behaviors such as sarcasm, racism, and sexism in real time

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration