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H. Rosa, R. Ribeiro, J. P. Carvalho, L. Coheur, I. Trancoso, N. Pereira...A. M. Veiga Simão (2018). Automatic Cyberbullying Detection in Social Networks from Scratch. ticEduca 2018 — V Congresso Internacional TIC e Educação.

H. Rosa et al.,  "Automatic Cyberbullying Detection in Social Networks from Scratch", in ticEduca 2018 — V Congr.o Internacional TIC e Educação, Lisboa, 2018

@misc{rosa2018_1768880039730,
	author = "H. Rosa and R. Ribeiro and J. P. Carvalho and L. Coheur and I. Trancoso and N. Pereira and P. Ferreira and S. Oliveira and P. Paulino and A. M. Veiga Simão",
	title = "Automatic Cyberbullying Detection in Social Networks from Scratch",
	year = "2018",
	howpublished = "Digital",
	url = "http://ticeduca2018.ie.ulisboa.pt/"
}

TY  - CPAPER
TI  - Automatic Cyberbullying Detection in Social Networks from Scratch
T2  - ticEduca 2018 — V Congresso Internacional TIC e Educação
AU  - H. Rosa
AU  - R. Ribeiro
AU  - J. P. Carvalho
AU  - L. Coheur
AU  - I. Trancoso
AU  - N. Pereira
AU  - P. Ferreira
AU  - S. Oliveira
AU  - P. Paulino
AU  - A. M. Veiga Simão
PY  - 2018
CY  - Lisboa
UR  - http://ticeduca2018.ie.ulisboa.pt/
AB  - Machine  Learning  (ML)  based  Cyberbullying  detection  is  a  subject  of  rising  interest amongst both computer science and  psychology researchers. In this article, we present the results from a collaborative effort between the Faculty of Psychology (University of Lisbon) and Institute of System and Computer Engineering, Research and Development (INESC-ID),  with  the  goal  to  develop  a  mobile  application  that  can  not  only  classify cyberbullying  instances,  but  also  teach  and  inform  users  to  develop  an  assertive communication  style  that  benefits  their  online  experience.  From  data  modelling  and  labelling, to building ML classifiers and implementing the “ComViver Online” app, we detail  on  our  novel  approach  to  have the  data  correctly  represent  the  cyberbullying phenomenon,  as  well  as  study  the  effectiveness  of  the  studied  classifiers  and  the challenges that this task presents from a Natural Language Processing (NLP) point of view.  By  labelling the  presence  of  cyberbullying  through  sets  of  interactions  between Twitter users, as opposed to isolated tweets, we believe that is possible to capture the nature of the relation between mentioned users (friendly or not), as well as the notion of repetitiveness   through   a   longer   period   of   time,   which   is   a   defining   feature   of cyberbullying.  Finally,  we  present the “ComViver  Online” prototype  app,  which  has been recently used by forty 9th grade students from schools in the Metropolitan Area of Lisbon,  as  a  part  of  an  intervention  made  in  the  scope  of  the  following  Portuguese Foundation for Science and Technology (FCT) funded project: PTDC/MHCPED/3297/2014.
ER  -