Fetching the paper…
Reading the bibliography…
Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms.
CRC press (1993)
Geisser, S.: Predictive inference, vol. 55 · 1993
Earlier work this paper cites.
Communications of the ACM 38
Miller, G.A.: Wordnet: a lexical database for english · 1995
Earlier work this paper cites.
In: AAAI-98 workshop on learning for text categorization, vol. 752, pp. 41–48. Citeseer (1998)
McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification · 1998
Earlier work this paper cites.
Tech. rep., Stanford University, Technical Report, San Francisco, CA (1999)
Friedman, J.: Stochastic gradient boosting. department of statistics · 1999
Earlier work this paper cites.
Statistics for Engineering and Information Science. Springer (2000)
Vapnik, V.N.: The Nature of Statistical Learning Theory, Second Edition · 2000
Earlier work this paper cites.
Pitenis, Z., Zampieri, M., Ranasinghe, T.: Offensive language identification in greek · 2003
Earlier work this paper cites.
BMC bioinformatics 7
Varma, S., Simon, R.: Bias in error estimation when using cross-validation for model selection · 2006
Earlier work this paper cites.
International Journal of Data Warehousing and Mining (IJDWM) 3
Tsoumakas, G., Katakis, I.: Multi-label classification: An overview · 2007
Earlier work this paper cites.
Pattern recognition 40
Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning · 2007
Earlier work this paper cites.
Published online (2001)
Porter, M.F.: Snowball: A language for stemming algorithms · 2008
Earlier work this paper cites.
In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 254–269. Springer, Springer, Bled, Slovenia (2009)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification · 2009
Earlier work this paper cites.
International Journal of Information Security Science 2
Almeida, T., Hidalgo, J.M.G., Silva, T.P.: Towards sms spam filtering: Results under a new dataset · 2013
Earlier work this paper cites.
In: Proceedings of the NAACL Student Research Workshop, pp. 88–93. Association for Computational Linguistics, San Diego, California (2016)
Waseem, Z., Hovy, D.: Hateful symbols or hateful people? predictive features for hate speech detection on twitter · 2013
Earlier work this paper cites.
In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Doha, Qatar (2014)
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation · 2014
Earlier work this paper cites.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate · 2015
Earlier work this paper cites.
In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 847–854. IEEE Computer Society, USA (2015)
Benites, F., Sapozhnikova, E.: Haram: A hierarchical aram neural network for large-scale text classification · 2015
Earlier work this paper cites.
In: Q. Yang, M.J. Wooldridge (eds.) Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pp. 4168–4172. AAAI Press (2015)
Dinakar, K., Picard, R.W., Lieberman, H.: Common sense reasoning for detection, prevention, and mitigation of cyberbullying (extended abstract) · 2015
Earlier work this paper cites.
Mach. Learn. 100
Krempl, G., Kottke, D., Lemaire, V.: Optimised probabilistic active learning (OPAL) - for fast, non-myopic, cost-sensitive active classification · 2015
Earlier work this paper cites.
In: R. Mihalcea, J.Y. Chai, A. Sarkar (eds.) NAACL HLT 2015, Denver, Colorado, USA, May 31 - June 5, 2015, pp. 441–451. The Association for Computational Linguistics (2015)
Sharma, M., Zhuang, D., Bilgic, M.: Active learning with rationales for text classification · 2015
Earlier work this paper cites.
J. Biomedical Semantics 7
Dramé, K., Mougin, F., Diallo, G.: Large scale biomedical texts classification: a knn and an esa-based approaches · 2016
Earlier work this paper cites.
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext.zip: Compressing text classification models (2016)
2016
Earlier work this paper cites.
In: Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM ’17, pp. 512–515. AAAI Press, Montreal, Canada (2017)
Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language · 2017
Cited alongside, same era.
In: Z. Waseem, W.H.K. Chung, D. Hovy, J.R. Tetreault (eds.) Proceedings of the First Workshop on Abusive Language Online, ALW@ACL 2017, Vancouver, BC, Canada, August 4, 2017, pp. 85–90. Association for Computational Linguistics (2017)
Gambäck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech · 2017
Cited alongside, same era.
In: RANLP (2017)
Gao, L., Huang, R.: Detecting online hate speech using context aware models · 2017
Cited alongside, same era.
Pers. Ubiquitous Comput. 21
Malik, H., Shakshuki, E.M.: Detecting performance anomalies in large-scale software systems using entropy · 2017
Cited alongside, same era.
Information Fusion 40
Tommasel, A., Godoy, D.: A social-aware online short-text feature selection technique for social media · 2017
Cited alongside, same era.
In: Working Notes of FIRE 2019, December 12-15, 2019, CEUR Workshop Proceedings , vol. 2517, pp. 199–207. CEUR-WS.org, Kolkata, India (2019)
Ranasinghe, T., Zampieri, M., Hettiarachchi, H.: BRUMS at HASOC 2019: Deep learning models for multilingual hate speech and offensive language identification · 2019
Later among the works it cites.
arXiv preprint arXiv:1910.01108 (2019)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter · 2019
Later among the works it cites.
In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019, pp. 2893–2896. ACM, Beijing, China (2019)
Sun, C., Asudeh, A., Jagadish, H.V., Howe, B., Stoyanovich, J.: Mithralabel: Flexible dataset nutritional labels for responsible data science · 2019
Later among the works it cites.
Knowl. Eng. Rev. 34
Tommasel, A., Godoy, D.: Short-text learning in social media: a review · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Inf. Fusion 40
Tommasel, A., Godoy, D.: A social-aware online short-text feature selection technique for social media · 2017
Cited alongside, same era.
In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 5796–5798. International Joint Conferences on Artificial Intelligence Organization, Stockholm, Sweden (2018)
Anagnostou, A., Mollas, I., Tsoumakas, G.: Hatebusters: A web application for actively reporting youtube hate speech · 2018
Cited alongside, same era.
arXiv preprint arXiv:1810.04805 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding · 2018
Cited alongside, same era.
In: IberEval@ SEPLN, pp. 214–228 (2018)
Fersini, E., Rosso, P., Anzovino, M.: Overview of the task on automatic misogyny identification at ibereval 2018 · 2018
Cited alongside, same era.
Pers. Ubiquitous Comput. 22
Furini, M., Montangero, M.: Sentiment analysis and twitter: a game proposal · 2018
Cited alongside, same era.
Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) (2018)
de Gibert, O., Perez, N., García-Pablos, A., Cuadros, M.: Hate speech dataset from a white supremacy forum · 2018
Cited alongside, same era.
In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pp. 1847–1850. ACM (2018)
Hoang, T., Vo, K.D., Nejdl, W.: W2E: A worldwide-event benchmark dataset for topic detection and tracking · 2018
Cited alongside, same era.
In: Proceedings of the Third Workshop on Abusive Language Online, pp. 11–18. Association for Computational Linguistics, Florence, Italy (2019)
Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., Predovic, G.: Exploring deep multimodal fusion of text and photo for hate speech classification · 2019
Later among the works it cites.
Int. J. Mach. Learn. & Cyber. 10
Yu, D., Fu, B., Xu, G., Qin, A.: Constrained nonnegative matrix factorization-based semi-supervised multilabel learning · 2019
Later among the works it cites.
In: NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 1415–1420 (2019)
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., Kumar, R.: Predicting the type and target of offensive posts in social media · 2019
Later among the works it cites.
In: K. Arai, S. Kapoor, R. Bhatia (eds.) Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 1, SAI 2020, London, UK, 16-17 July 2020, Advances in Intelligent Systems and Computing , vol. 1228, pp. 521–541. Springer (2020)
Alharthi, D.N., Regan, A.C.: Social engineering defense mechanisms: A taxonomy and a survey of employees’ awareness level · 2020
Closest in time.
In: N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis (eds.) Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11-16, 2020, pp. 279–287. European Language Resources Association (2020)
Haagsma, H., Bos, J., Nissim, M.: MAGPIE: A large corpus of potentially idiomatic expressions · 2020
Closest in time.
Journal of Responsible Technology 1
Jirotka, M., Stahl, B.C.: The need for responsible technology · 2020
Closest in time.
In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pp. 3235–3244. IEEE (2020)
Kim, S., Kim, D., Cho, M., Kwak, S.: Proxy anchor loss for deep metric learning · 2020
Closest in time.
J. Comput. Sci. Technol. 35
Kumar, P., Gupta, A.: Active learning query strategies for classification, regression, and clustering: A survey · 2020
Closest in time.
In: J. Monti, V. Basile, M.P. di Buono, R. Manna, A. Pascucci, S. Tonelli (eds.) Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language, ResTUP@LREC 2020, Marseille, France, May 2020, pp. 14–19. European Language Resources Association (ELRA) (2020)
van Rosendaal, J., Caselli, T., Nissim, M.: Lower bias, higher density abusive language datasets: A recipe · 2020
Closest in time.
arXiv preprint arXiv:2004.14454 (2020)
Rosenthal, S., Atanasova, P., Karadzhov, G., Zampieri, M., Nakov, P.: A large-scale semi-supervised dataset for offensive language identification · 2020
Closest in time.
In: C. Hung, T. Cerný, D. Shin, A. Bechini (eds.) SAC ’20: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020, pp. 1119–1126. ACM (2020)
Shim, H., Luca, S., Lowet, D., Vanrumste, B.: Data augmentation and semi-supervised learning for deep neural networks-based text classifier · 2020
Closest in time.
Comput. Speech Lang. 65
Skrlj, B., Martinc, M., Kralj, J., Lavrac, N., Pollak, S.: tax2vec: Constructing interpretable features from taxonomies for short text classification · 2020
Closest in time.
In: K. Arai, S. Kapoor, R. Bhatia (eds.) Intelligent Computing - Proceedings of the 2020 Computing Conference, Volume 1, SAI 2020, London, UK, 16-17 July 2020, Advances in Intelligent Systems and Computing , vol. 1228, pp. 491–501. Springer (2020)
Tang, M.J., Chan, E.T.: Social media: Influences and impacts on culture · 2020
Closest in time.
Ethics Inf. Technol. 22
Ullmann, S., Tomalin, M.: Quarantining online hate speech: technical and ethical perspectives · 2020
Closest in time.
Available at https://support.minitab.com/en-us/minitab/18/help-and-how-to/quality-and-process-improvement/measurement-system-analysis/how-to/attribute-agreement-analysis/attribute-agreement-analysis/interpret-the-results/all-statistics-and-graphs/kappa-statistics/ (2021/04/17)
Inc., M.: Kappa statistics for attribute agreement analysis · 2021
Closest in time.
In: D. Gkatzia, D. Seddah (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, EACL 2021, Online, April 19-23, 2021, pp. 238–243. Association for Computational Linguistics (2021)
Nghiem, M., Baylis, P., Ananiadou, S.: Paladin: an annotation tool based on active and proactive learning · 2021
Closest in time.