Technology Solutions to Combat Online Harassment

By George Kennedy, A. McCollough, Edward Dixon, A. Bastidas, John Ryan, Chris Loo, Saurav Sahay. 2017

48 citations recorded by the source corpus.

This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes under-reported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross-platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

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Citations in the corpus

Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, methodology): “Toxic comment identification is a supervised classification task and approached by either methods including manual feature engineering (Burnap and Williams, 2015; Mehdad and Tetreault, 2016; Waseem, 2016; Davidson et al., 2017; Nobata et al., 2016; Kennedy et al., 2017; Samghabadi et al., 2017; Robi”
Aggression Identification Using Deep Learning and Data Augmentation (2018, background): “One of the main limitation for research progress in the field of toxic comment classification is the low amount of available accurately labeled data (Kennedy et al., 2017).”
I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language (2020, background): “…increasing effort from the Natural Language Processing (NLP) community to develop methods for the automatic detection of abusive language and related phenomena, as the volume of data is such that it has become impossible to manually track and monitor it (Nobata et al., 2016; Kennedy et al., 2017).”