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    Large scale crowdsourcing and characterization of Twitter abusive behavior

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    Date Issued
    2018
    Author(s)
    Founta, Antigoni-Maria
    Djouvas, Constantinos
    Chatzakou, Despoina
    Leontiadis, Ilias
    Blackburn, Jeremy
    Stringhini, Gianluca
    Vakali, Athena
    Sirivianos, Michael
    Kourtellis, Nicolas
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    Permanent Link
    https://hdl.handle.net/2144/40119
    Version
    Accepted manuscript
    Citation (published version)
    Antigoni-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.
    Abstract
    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.
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    • ENG: Electrical and Computer Engineering: Scholarly Papers [376]
    • BU Open Access Articles [4754]


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