Cross-Domain Detection of Abusive Language Online

By Mladen Karan, Jan Snajder. 2018

84 citations recorded by the source corpus.

We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types. To this end, we compare the cross-domain performance of simple classification models on nine different datasets, finding that the models fail to generalize to out-domain datasets and that having at least some in-domain data is important. We also show that using the frustratingly simple domain adaptation (Daume III, 2007) in most cases improves the results over in-domain training, especially when used to augment a smaller dataset with a larger one.

Subfields: Artificial Intelligence, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, background): “Classifiers often have more false negatives if they have only been trained on data from one source or on hate against just one target (Karan and Šnajder, 2018; Pamungkas et al., 2020; Swamy et al., 2019; Basile et al., 2019).”
Challenges and frontiers in abusive content detection (2019, methodology): “Karan and Šnajder use a simple methodology to show the huge differences in performance when applying classifiers on different datasets without domain-specific tuning (Karan & Šnajder, 2018).”
Detection of Abusive Language: the Problem of Biased Datasets (2019, methodology): “We need to ﬁnd an explanation for this deviation since Waseem is by far the most frequently used dataset for detecting abusive language (Badjatiya et al., 2017; Bourgonje et al., 2017; Pitsilis et al., 2018; Agrawal and Awekar, 2018; Karan and Snajder, 2018; Kshirsagar et”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, background): “Recently, Waseem et al. (2018) and Karan and ˇSnajder (2018) exploited multi-task learning frameworks to train models that are robust across data from different distributions and data annotated under different guidelines.”
Studying Generalisability across Abusive Language Detection Datasets (2019, methodology): “This is in line with a similar conclusion by Karan and ˇSnajder (2018).”
Developing an online hate classifier for multiple social media platforms (2020, background): “Even though hate has been observed as a problem in multiple online social media platforms, including Reddit, YouTube, Wikipedia, Twitter, and so on [5, 7, 16–18], apart from a few exploratory studies [15, 19], there is a lack of development and testing of models using data from multiple social media”
Towards multidomain and multilingual abusive language detection: a survey (2021, methodology): “Another effort by Karan and Snajder [64] adopted a domain adaptation approach called FEDA (Frustratingly Easy Domain Adaptation), which works by duplicating features across domains to allow the model to learn domain-dependent weights for each feature.”
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (2020, unknown): “In a similar vein, Karan and Šnajder (2018) employ frustratingly easy domain adaptation (Daumé III, 2007) to experiment with domain transfer on a wide range of abusive language data sets.”
HateBERT: Retraining BERT for Abusive Language Detection in English (2020, background): “Previous work (Karan and Šnajder, 2018; Benk, 2019; Pamungkas and Patti, 2019; Rizoiu et al., 2019) has addressed this task by conflating portability with generalizability, forcing datasets with different phenomena into homogenous annotations by collapsing labels into (binary) macro-categories.”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “…detection Davidson, Bhattacharya, and Weber (2019); Park, Shin, and Fung (2018); Wiegand, Ruppenhofer, and Kleinbauer (2019), and cross-domain abusive language detection Karan and Snajder (2018); Pamungkas and Patti (2019); Swamy, Jamatia, and Gambäck (2019); Waseem, Thorne, and Bingel (2018).”