Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.
AnzovinoM., FersiniE. and RossoP., Automatic Identification and Classification of Misogynistic Language on Twitter, Proc 23rd International Conference on Applications of Natural Language to Information Systems, NLDB-2018, Springer-Verlag, LNCS 10859, 2018, pp. 57–64.
BurnapP. and WilliamsM.L., Hate speech, machine classification and statistical modelling of information flows on Twitter: Interpretation and communication for policy decision making, Internet, Policy and Politics, Oxford, UK, 2014.
4.
BurnapP., RanaO.F., AvisN., WilliamsM., HousleyW., EdwardsA., MorganJ. and SloanL., Detecting tension in online communities with computational Twitter analysis, Technological Forecasting and Social Change95 (2015), 96–108.
5.
ClarkeI. and GrieveJ., Dimensions of abusive language on twitter, Proceedings of the First Workshop on Abusive Language Online, 2017, pp. 1–10.
6.
ChenY., ZhouY., ZhuS. and XuH., Detecting offensive language in social media to protect adolescent online safety, Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom), Amsterdam, Netherlands, IEEE, 2012, pp. 71–80.
7.
EscalanteH.J., Villatoro-TelloE., Garza VillarealS.E.,
López-MonroyA.P.,
Montes-y-GómezM. and Villaseñor-PinedaL., Early detection of deception and aggressiveness using profilebased representations, Expert Systems with Applications89 (2017), 99–111.
8.
FersiniE., AnzovinoM. and RossoP., Overview of the Task on Automatic Misogyny Identification at IBEREVAL, CEUR Workshop Proceedings 2150, Seville, Spain, 2018.
9.
FersiniE., NozzaD. and RossoP., Overview of the Evalita 2018 Task on Automatic Misogyny Identification (AMI), Proceedings of the 6th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA’18), Turin, Italy, 2018.
10.
FordT.E. and BoxerC.F., Sexist humor in the workplace: A case of subtle harassment, Insidious Workplace Behavior, Routledge, 2011, pp. 203–234.
11.
FoxJ. and TangW.Y., Sexism in online video games: The role of conformity to masculine norms and social dominance orientation, Computers in Human Behavior33 (2014), 314–320.
12.
FoxJ., CruzC. and LeeJ.Y., Perpetuating online sexism offline: Anonymity, interactivity, and the effects of sexist hashtags on social media, Computers in Human Behavior52 (2015), 436–442.
13.
FulperR., CiampagliaG.L., FerraraE., AhnY., FlamminiA., MenczerF., LewisB. and RoweK., Misogynistic language on Twitter and sexual violence, Proceedings of the ACM Web Science Workshop on Computational Approaches to Social Modeling (ChASM), 2014.
14.
GambäckB. and SikdarU.K., Using convolutional neural networks to classify hate-speech, Proceedings of the First Workshop on Abusive Language Online2017.
15.
GitariN.D., ZupingZ., DamienH. and LongJ., A lexiconbased approach for hate speech detection, International Journal of Multimedia and Ubiquitous Engineering10(4) (2015), 215–230.
16.
HewittS., TiropanisT. and BokhoveC., The problem of identifying misogynist language on Twitter (and other online social spaces), Proceedings of the 8th ACM Conference on Web Science, 2016, pp. 333–335.
JustoR., CorcoranT., LukinS.M., WalkerM. and TorresM.I., Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web, Knowledge-Based Systems, 2014.
19.
Lapidot-LeflerN. and BarakA., Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition, Computers in Human Behavior28(2) (2012), 434–443.
20.
ManneK., Down Girl: The Logic of Misogyny, Oxford Scholarship Online, 2017.
21.
NobataC., TetreaultJ., ThomasA., MehdadY. and ChangY., Abusive language detection in online user content, Proceedings of the 25th International Conference on World Wide Web, Geneva, Switzerland, 2016, pp. 145–153.
22.
PolandB., Haters: Harassment, Abuse, and Violence Online,
University of Nebraska Press, Lincoln, 2016.
23.
SamghabadiN.S., MaharjanS., SpragueA., Diaz-SpragueR. and SolorioT., Detecting nastiness in social media, Proceedings of the First Workshop on Abusive Language Online, Vancouver, Canada, 2017, pp. 63–72. Association for Computational Linguistics.
24.
SoodS., AntinJ. and ChurchillE., Profanity use in online communities, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2012, pp. 1481–1490.
25.
WaseemZ. and HovyD., Hateful symbols or hateful people, Predictive Features for Hate Speech Detection on Twitter.[email protected] HLT-NAACL, 2016.
