Using Machine Learning to Detect Cyberbullying

By Kelly Reynolds, April Kontostathis, Lynne Edwards. 2011 10th International Conference on Machine Learning and Applications and Workshops, 2011

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/90972e7394b5fc884470cf78a657aae3932a8d8a

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Citations in the corpus

HABERTOR: An Efficient and Effective Deep Hatespeech Detector (2020, methodology): “…Awekar, 2018; Dadvar and Eckert, 2018; Gröndahl et al., 2018), where those methods are tested in other datasets and domains such as Twitter data (Waseem and Hovy, 2016), Wikipedia data (Wulczyn et al., 2017), Formspring data (Reynolds et al., 2011), and YouTube comment data (Dadvar et al., 2014).”
Detection of Cyberbullying Incidents on the Instagram Social Network (2015, background): “…2010),(Dadvar et al. 2012),(K. Reynolds and Edwards 2011),(Dinakar et al. 2012),(H. Sanchez 2012),(Kontostathis et al. 2013), (Xu et al. 2012),(Nahar et al. 2014),(Na-har, Li, and Pang 2013),(Dinakar, Reichart, and Lieberman 2011),(Nahar et al. 2012) are more accurately described as research on…”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, methodology): “To investigate such significance, we created two baseline features from our data set, namely, a bag of words and a combination of possible features proposed in previous studies (Chavan& Shylaja, 2015; Dadvar et al., 2012; Dadvar, Trieschnigg, Ordelman, et al., 2013; Kontostathis et al., 2013; Reynol”
Automatic detection of cyberbullying in social media text (2018, background): “To tackle this problem, several studies have adopted resampling techniques (Nahar et al., 2014; Al-garadi et al., 2016; Reynolds et al., 2011) that create synthetic minority class examples or reduce the number of negative class examples (i.”
Technology Solutions to Combat Online Harassment (2017, methodology): “Previous work has applied anti-spam techniques like machine learning based text classiﬁcation (Reynolds et al., 2011) to detecting harassing messages.”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “whether the content is hateful or not (Reynolds et al., 2011; Rafiq et al., 2015; Hosseinmardi et al., 2015; de Gibert et al., 2018; ElSherief et al., 2018).”
Toward Multimodal Cyberbullying Detection (2017, methodology): “In our work, we consider textual features indicative of emotion, gender specific terminology, sexual connotations, as well as the relative distribution of different parts of speech [40, 36, 11, 41, 13, 15].”
Detection and Fine-Grained Classification of Cyberbullying Events (2015, methodology): “Reynolds et al. (2011) worked with data that is similar to ours (i.e. question-answer pairs) and made use of lexical features including the number of ‘bad’ words in a post.”
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms (2018, methodology): “Their method has accuracy score of 78.5% [12].”