Thushari Atapattu


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Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability
Thushari Atapattu | Mahen Herath | Georgia Zhang | Katrina Falkner
Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association

Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5 (HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistiBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.

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AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media
Mahen Herath | Thushari Atapattu | Hoang Anh Dung | Christoph Treude | Katrina Falkner
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.

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Enhancing the Identification of Cyberbullying through Participant Roles
Gathika Rathnayake | Thushari Atapattu | Mahen Herath | Georgia Zhang | Katrina Falkner
Proceedings of the Fourth Workshop on Online Abuse and Harms

Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.


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Automatic Detection of Cross-Disciplinary Knowledge Associations
Menasha Thilakaratne | Katrina Falkner | Thushari Atapattu
Proceedings of ACL 2018, Student Research Workshop

Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically sensible novel research hypotheses. This will also introduce new areas of research that can be successfully linked with their research discipline. Currently, this process is mostly performed manually by exploring the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for a scientist to keep track of all research advances. As a result, scientists tend to deal with fragments of the literature according to their specialisation. Consequently, important and hidden associations among these fragmented knowledge that can be linked to produce significant scientific discoveries remain unnoticed. This doctoral work aims to develop a novel knowledge discovery approach that suggests most promising research pathways by analysing the existing scientific literature.