Accepted Papers

We are pleased to present the following accepted papers:


  1. Social contagion in ethnic abusive swearing during periods of increased terrorist activity. Christoph Spörlein.
  2. Neural Character-based Composition Models for Abuse Detection. Pushkar Mishra, Helen Yannakoudakis and Ekaterina Shutova. (pdf)
  3. Hate Speech Dataset from a White Supremacy Forum. Ona de Gibert, Naiara Perez, Aitor García Pablos and Montse Cuadros. (pdf)
  4. A review of standard text classification practices for multi-label toxicity identification of online content. Isuru Gunasekara and Isar Nejadgholi. (pdf)
  5. Predictive Embeddings for Hate Speech Detection on Twitter. Rohan Kshirsagar, Tyrus Cukuvac, Kathy McKeown and Susan McGregor. (pdf)
  6. Challenges for Toxic Comment Classification: An In-Depth Error Analysis. Betty van Aken, Julian Risch, Ralf Krestel and Alexander Löser. (pdf)
  7. Aggression Detection on Social Media Text Using Deep Neural Networks. Vinay Singh, Aman Varshney, Syed Sarfaraz Akhtar, Deepanshu Vijay and Manish Shrivastava. (pdf)
  8. Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying. Rachele Sprugnoli, Stefano Menini, Sara Tonelli, Filippo Oncini and Enrico Piras. (pdf)
  9. Improving Moderation of Online Discussions via Interpretable Neural Models. Andrej Švec, Matúš Pikuliak, Marian Simko and Maria Bielikova. (pdf)
  10. Aggressive language in an online hacking forum. Andrew Caines, Sergio Pastrana, Alice Hutchings and Paula Buttery. (pdf)
  11. The Effects of User Features on Twitter Hate Speech Detection. Elise Fehn Unsvåg and Björn Gambäck. (pdf)
  12. Interpreting Neural Network Hate Speech Classifiers. Cindy Wang. (pdf)
  13. Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks. Rijul Magu and Jiebo Luo. (pdf)
  14. Comparative Studies of Detecting Abusive Language on Twitter. Younghun Lee, Seunghyun Yoon and Kyomin Jung. (pdf)
  15. Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs. Sima Sharifirad, Borna Jafarpour and Stan Matwin. (pdf)
  16. Learning Representations for Detecting Abusive Language. Magnus Sahlgren, Tim Isbister and Fredrik Olsson. (pdf)
  17. Datasets of Slovene and Croatian Moderated News Comments. Nikola Ljubešić, Tomaž Erjavec and Darja Fišer. (pdf)
  18. Cross-Domain Detection of Abusive Language Online. Mladen Karan and Jan Šnajder. (pdf)
  19. Did you offend me? Classification of Offensive Tweets in Hinglish Language. Puneet Mathur, Ramit Sawhney, Meghna Ayyar, Rajiv Ratn Shah. (pdf)
  20. Decipherment for Adversarial Offensive Language Detection. Zhelun Wu, Nishant Kambhatla and Anoop Sarkar. (pdf)
  21. The Linguistic Ideologies of Deep Abusive Language Classification. Michael Castelle. (pdf)