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dc.contributor.authorFounta, Antigoni-Mariaen_US
dc.contributor.authorDjouvas, Constantinosen_US
dc.contributor.authorChatzakou, Despoinaen_US
dc.contributor.authorLeontiadis, Iliasen_US
dc.contributor.authorBlackburn, Jeremyen_US
dc.contributor.authorStringhini, Gianlucaen_US
dc.contributor.authorVakali, Athenaen_US
dc.contributor.authorSirivianos, Michaelen_US
dc.contributor.authorKourtellis, Nicolasen_US
dc.date.accessioned2020-04-13T14:50:39Z
dc.date.available2020-04-13T14:50:39Z
dc.date.issued2018
dc.identifierhttp://www.aaai.org/Library/ICWSM/icwsm18contents.php
dc.identifier.citationAntigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, Nicolas Kourtellis. 2018. "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior.." ICWSM, pp. 491 - 500.
dc.identifier.urihttps://hdl.handle.net/2144/40119
dc.description.abstractIn 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.en_US
dc.format.extentpp. 491 - 500.en_US
dc.language.isoen_US
dc.publisherAAAI Pressen_US
dc.relation.ispartofICWSM
dc.titleLarge scale crowdsourcing and characterization of Twitter abusive behavioren_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
pubs.elements-sourcedblpen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
dc.identifier.mycv399755


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