<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter</style></title><tertiary-title><style face="normal" font="default" size="100%">HT '17</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Prague, Czech Republic</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 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.&lt;/p&gt;

&lt;p&gt;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.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Harvesting Opinions and Emotions from Social Media Textual Resources</style></title><secondary-title><style face="normal" font="default" size="100%">Internet Computing, IEEE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation models</style></keyword><keyword><style  face="normal" font="default" size="100%">Analytical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet/Web technologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Media</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Text processing</style></keyword><keyword><style  face="normal" font="default" size="100%">textual resources</style></keyword><keyword><style  face="normal" font="default" size="100%">Web 2.0</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">46-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Hydra: An Open Framework for Virtual-Fusion of Recommendation Filters</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Today’s web commercial applications demand more powerfulrecommendation systems due to the rapid increase in thenumber of both consumers and available products. Searchingfor the best algorithm with the highest accuracy and realisticcomplexity is, most of the time, a very costly processin terms of both time and resources. In this paper we suggestan alternative framework called Hydra which enablesthe virtual fusion of any and as many currently availablerecommendation algorithms in such a distributed mannerthat algorithms’ complexities are not summarized but parallelized.Therefore, we utilize the available algorithms andtechnologies aiming to achieve better accuracy in order tosurpass even the most state of the art algorithms. In addition,Hydra can be used to find how algorithms interactwith each other in order to estimate the resulting accuracytowards inventing a more precise algorithm diminishing therisk of a failed investment. Hydra can be adjusted and integratedin any recommendation application while it is alsoopen to new functionalities which can be embedded easilyand in a transparent manner.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Terzi, Evimaria</style></author><author><style face="normal" font="default" size="100%">Bertino, Elisa</style></author><author><style face="normal" font="default" size="100%">Elmagarmid, Ahmed K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical data placement for navigational multimedia applications</style></title><secondary-title><style face="normal" font="default" size="100%">Data Knowl. Eng.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">49-80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>