skip to main content
research-article

Cyber-aggression, Cyberbullying, and Cyber-grooming: A Survey and Research Challenges

Published:02 January 2021Publication History
Skip Abstract Section

Abstract

Cyber-aggression, cyberbullying, and cyber-grooming are distinctive and similar phenomena that represent the objectionable content appearing on online social media. Timely detection of the objectionable content is very important for its prevention and reduction. This article explores and spotlights diversity of definitions of cyber-aggression, cyberbulling, and cyber-grooming; analyzes current categorization systems and taxonomies; identifies the targets, target categories, and subcategories of the subjects of the objectionable content research; analyzes the ambiguity of the linguistic terms in the domain; reviews present databases gathered for researching the field; explores types of features used for modeling systems for automatic detection; and examines methods for automatic detection and/or prediction of the objectionable content. The results point to directions of system development for tracing transformations of objectionable content over time on different online social platforms.

References

  1. Niyati Aggrawal. 2018. Detection of offensive Tweets: A comparative study. Comput. Rev. J. 1, 1 (2018), 75--89.Google ScholarGoogle Scholar
  2. Sweta Agrawal and Amit Awekar. 2018. Deep learning for detecting cyberbullying across multiple social media platforms. In Proceedings of the European Conference on Information Retrieval. Springer, 141--153.Google ScholarGoogle ScholarCross RefCross Ref
  3. Mohammed Ali Al-garadi, Kasturi Dewi Varathan, and Sri Devi Ravana. 2016. Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Comput. Hum. Behav. 63 (2016), 433--443.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ika Alfina, Rio Mulia, Mohamad Ivan Fanany, and Yudo Ekanata. 2017. Hate speech detection in the Indonesian language: A dataset and preliminary study. In Proceedings of the 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS’17) 2017.Google ScholarGoogle ScholarCross RefCross Ref
  5. Thais G. Almeida, Bruno À. Souza, Fabíola G. Nakamura, and Eduardo F. Nakamura. 2017. Detecting hate, offensive, and regular speech in short comments. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. ACM, 225--228.Google ScholarGoogle Scholar
  6. Hamza H. M. Altarturi, Muntadher Saadoon, and Nor Badrul Anuar. 2020. Cyber parental control: A bibliometric study. Childr. Youth Serv. Rev. 116 (2020).Google ScholarGoogle Scholar
  7. Segun Taofeek Aroyehun and Alexander Gelbukh. 2018. Aggression detection in social media: Using deep neural networks, data augmentation, and pseudo labeling. In Proceedings of the 1st Workshop on Trolling, Aggression and Cyberbullying (TRAC’18). 90--97.Google ScholarGoogle Scholar
  8. Varsha S. Babar and Roshani Ade. 2015. A review on imbalanced learning methods. In Proceedings of the International Journal of Computer Applications (0975–8887) National Conference on Advances in Computing (NCAC’15).Google ScholarGoogle Scholar
  9. Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017. Deep learning for hate speech detection in tweets. In Proceedings of the 26th International Conference on World Wide Web Companion. 759--760.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Bartlomiej Balcerzak and Wojciech Jaworski. 2015. Application of linguistic cues in the analysis of language of hate groups. Comput. Sci. 16, 2 (2015).Google ScholarGoogle Scholar
  11. Koray Balci and Albert Ali Salah. 2015. Automatic analysis and identification of verbal aggression and abusive behaviors for online social games. Comput. Hum. Behav. 53 (2015), 517--526.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Connie S. Barber and Silvia Cristina Bettez. 2014. Deconstructing the online grooming of youth: Toward improved information systems for detection of online sexual predators. In Proceedings of the International Conference on Information Systems (ICIS’14).Google ScholarGoogle Scholar
  13. Alexei Bastidas, Edward Dixon, Chris Loo, and John Ryan. 2016. Harassment detection: A benchmark on the #HackHarassment dataset. ArXiv, abs/1609.02809.Google ScholarGoogle Scholar
  14. Sofia Berne, Ann Frisén, Anja Schultze-Krumbholz, Herbert Scheithauer, Karin Naruskov, Piret Luik, Catarina Katzer, Rasa Erentaite, and Rita Zukauskiene. 2013. Cyberbullying assessment instruments: A systematic review. Aggress. Violent Behav. 18, 2 (2013), 320--334.Google ScholarGoogle ScholarCross RefCross Ref
  15. Catherine Blaya. 2019. Cyberhate: A review and content analysis of intervention strategies. Aggress. Violent Behav. 45 (2019), 163--172.Google ScholarGoogle ScholarCross RefCross Ref
  16. Dasha Bogdanova, Paolo Rosso, and Thamar Solorio. 2014. Exploring high-level features for detecting cyberpedophilia. Comput. Speech Lang. 28, 1 (2014), 108--120.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Peter Bourgonje, Julian Moreno-Schneider, Ankit Srivastava, and Georg Rehm. 2017. Automatic classification of abusive language and personal attacks in various forms of online communication. In Proceedings of the German Society for Computational Linguistics and Language Technology (GSCL’17). Springer, 180--191.Google ScholarGoogle Scholar
  18. Patrick Bours and Halvor Kulsrud. 2019. Detection of cyber grooming in online conversation. In Proceedings of the IEEE International Workshop on Information Forensics and Security (WIFS’19). 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  19. Pete Burnap and Matthew L. Williams. 2015. Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making. Policy Internet 7, 2 (2015), 223--242.Google ScholarGoogle ScholarCross RefCross Ref
  20. Pete Burnap and Matthew L. Williams. 2016. Us and them: Identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Sci. 5, 1 (2016), 11.Google ScholarGoogle ScholarCross RefCross Ref
  21. Erik Cambria, Praphul Chandra, Avinash Sharma, and Amir Hussain. 2010. Do not feel the trolls. In Proceedings of the SDoW2010: The 9th International Semantic Web Conference (ISWC’10) Workshop, Vol. 664.Google ScholarGoogle Scholar
  22. Amparo Elizabeth Cano, Miriam Fernandez, and Harith Alani. 2014. Detecting child grooming behaviour patterns on social media. In Proceedings of the International Conference on Social Informatics. Springer, 412--427.Google ScholarGoogle ScholarCross RefCross Ref
  23. Claudia Cardei and Traian Rebedea. 2017. Detecting sexual predators in chats using behavioral features and imbalanced learning. Natur. Lang. Eng. 23, 4 (2017), 589--616.Google ScholarGoogle ScholarCross RefCross Ref
  24. Michael Castelle. 2018. The linguistic ideologies of deep abusive language classification. In Proceedings of the Workshop on Abusive Language Online (ALW2’18). 160--170.Google ScholarGoogle ScholarCross RefCross Ref
  25. Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Athena Vakali. 2017. Mean birds: Detecting aggression and bullying on twitter. In Proceedings of the 2017 ACM on Web Science Conference. ACM, 13--22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Ying Chen, Yilu Zhou, Sencun Zhu, and Heng Xu. 2012. Detecting offensive language in social media to protect adolescent online safety. In Proceedings of the 2012 International Conference on Social Computing, Privacy, Security, Risk and Trust (PASSAT’12). IEEE, 71--80.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Yun-Gyung Cheong, Alaina K. Jensen, Elín Rut Gudnadóttir, Byung-Chull Bae, and Julian Togelius. 2015. Detecting predatory behavior in game chats. IEEE Trans. Comput. Intell. AI Games 7, 3 (2015), 220--232.Google ScholarGoogle ScholarCross RefCross Ref
  28. Isobelle Clarke and Jack Grieve. 2017. Dimensions of abusive language on Twitter. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 1--10.Google ScholarGoogle ScholarCross RefCross Ref
  29. Lucie Corcoran, Conor Mc Guckin, and Garry Prentice. 2015. Cyberbullying or cyber aggression?: A review of existing definitions of cyber-based peer-to-peer aggression. Societies 5, 2 (2015), 245--255.Google ScholarGoogle ScholarCross RefCross Ref
  30. Maral Dadvar, de F. M. G. Jong, Roeland Ordelman, and Dolf Trieschnigg. 2012. Improved cyberbullying detection using gender information. In Proceedings of the 12th Dutch-Belgian Information Retrieval Workshop (DIR’12). University of Ghent.Google ScholarGoogle Scholar
  31. Maral Dadvar, Rudolf Berend Trieschnigg, and Franciska M. G. de Jong. 2013. Expert knowledge for automatic detection of bullies in social networks. In Proceedings of the 25th Benelux Conference on Artificial Intelligence (BNAIC’13). TU Delft.Google ScholarGoogle Scholar
  32. Thomas Davidson, Dana Warmsley, Michael W. Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’17).Google ScholarGoogle Scholar
  33. Ona de Gibert, Naiara Perez, Aitor García Pablos, and Montse Cuadros. 2018. Hate speech dataset from a white supremacy forum. In Proceedings of the Workshop on Abusive Language Online (ALW2’18). ACL, 11--20.Google ScholarGoogle ScholarCross RefCross Ref
  34. Rogers Prates de Pelle and Viviane P. Moreira. 2017. Offensive comments in the Brazilian Web: A dataset and baseline results. In Proceedings of the WS on BraSNAM in Conjunction with CSBC. 510--519.Google ScholarGoogle Scholar
  35. Tom De Smedt, Guy De Pauw, and Pieter Van Ostaeyen. 2018. Automatic detection of online Jihadist hate speech. arXiv:1803.04596. Retrieved from https://arxiv.org/abs/1803.04596.Google ScholarGoogle Scholar
  36. Tom De Smedt, Sylvia Jaki, Eduan Kotzé, Leïla Saoud, Maja Gwóźdź, Guy De Pauw, and Walter Daelemans. 2018. Multilingual cross-domain perspectives on online hate speech. arXiv:1809.03944. Retrieved from https://arxiv.org/abs/1809.03944.Google ScholarGoogle Scholar
  37. Karthik Dinakar, Birago Jones, Catherine Havasi, Henry Lieberman, and Rosalind Picard. 2012. Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Trans. Interact. Intell. Syst. 2, 3 (2012), 18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Karthik Dinakar, Roi Reichart, and Henry Lieberman. 2011. Modeling the detection of textual cyberbullying. Soc. Mobile Web 11, 02 (2011), 11--17.Google ScholarGoogle Scholar
  39. Nemanja Djuric, Jing Zhou, Robin Morris, Mihajlo Grbovic, Vladan Radosavljevic, and Narayan Bhamidipati. 2015. Hate speech detection with comment embeddings. In Proceedings of the 24th International Conference on World Wide Web. ACM, 29--30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Jiangjiao Duan and Jianping Zeng. 2013. Web objectionable text content detection using topic modeling technique. Expert Syst. Appl. 40, 15 (2013), 6094--6104.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Mohammadreza Ebrahimi, Ching Y. Suen, Olga Ormandjieva, and Adam Krzyzak. 2016. Recognizing predatory chat documents using semi-supervised anomaly detection. In Proceedings of IS8T International Symposium on Electronic Imaging Science and Technology 2016: Document Recognition and Retrieval XXIII. Ingenta, 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  42. Matthew Edwards, Awais Rashid, and Paul Rayson. 2015. A systematic survey of online data mining technology intended for law enforcement. ACM Comput. Surv. 48, 1 (2015).Google ScholarGoogle Scholar
  43. Mai ElSherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, and Elizabeth Belding. 2018. Hate lingo: A target-based linguistic analysis of hate speech in social media. arXiv:1804.04257. Retrieved from https://arxiv.org/abs/1804.04257.Google ScholarGoogle Scholar
  44. Escalante, Hugo Jair and Villatoro-Tello, Esaú and Garza, Sara E. and López-Monroy, A. Pastor and Montes-y-Gómez, Manuel and Villaseñor-Pineda, Luis. 2017. Early detection of deception and aggressiveness using profile-based representations. Expert Syst. Appl. 89, C (2017), 99--111.Google ScholarGoogle Scholar
  45. Ali Fauzi and Anny Yuniarti. 2018. Ensemble method for Indonesian Twitter hate speech detection. J. Electrochem. Energy Convers. Stor. 11, 1 (2018), 294--299.Google ScholarGoogle Scholar
  46. Darja Fišer, Tomaž Erjavec, and Nikola Ljubešić. 2017. Legal framework, dataset and annotation schema for socially unacceptable online discourse practices in slovene. In Proceedings of the 1st Workshop on Abusive Language Online. 46--51.Google ScholarGoogle ScholarCross RefCross Ref
  47. International Centre for Missing and Exploited Children. 2017. Annual Report. Retrieved Feb. 25, 2019 from https://www.icmec.org/wp-content/uploads/2018/08/2017-Annual-Report-Final-Digital.pdf.Google ScholarGoogle Scholar
  48. Paula Fortuna and Sérgio Nunes. 2018. A survey on automatic detection of hate speech in text. ACM Comput. Surv. 51, 4 (2018), 85:1–85:30.Google ScholarGoogle Scholar
  49. Paula Cristina Teixeira Fortuna. 2017. Automatic Detection of Hate Speech in Text: An Overview of the Topic and Dataset Annotation with Hierarchical Classes. Master’s thesis. Faculdade de Engenharia da Universidade do Porto.Google ScholarGoogle Scholar
  50. Antigoni-Maria Founta, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, and Ilias Leontiadis. 2018. A unified deep learning architecture for abuse detection. arXiv, 1802.00385v2.Google ScholarGoogle Scholar
  51. Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018. Large scale crowdsourcing and characterization of Twitter abusive behavior. arXiv:1802.00393. Retrieved from https://arxiv.org/abs/1802.00393.Google ScholarGoogle Scholar
  52. Simona Frenda, Ghanem Bilal, et al. 2018. Exploration of misogyny in Spanish and English tweets. In Proceedings of the 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval’18), Vol. 2150. 260--267.Google ScholarGoogle Scholar
  53. Patxi Galán-García, José Gaviria de la Puerta, Carlos Laorden Gómez, Igor Santos, and Pablo García Bringas. 2014. Supervised machine learning for the detection of troll profiles in Twitter social network: Application to a real case of cyberbullying. In Proceedings of the International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Springer International Publishing, 419--428.Google ScholarGoogle Scholar
  54. Björn Gambäck and Utpal Kumar Sikdar. 2017. Using convolutional neural networks to classify hate-speech. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). 85--90.Google ScholarGoogle ScholarCross RefCross Ref
  55. Lei Gao and Ruihong Huang. 2017. Detecting online hate speech using context aware models. arXiv:1710.07395. Retrieved from https://arxov.org/abs/1710.07395.Google ScholarGoogle Scholar
  56. Lei Gao, Alexis Kuppersmith, and Ruihong Huang. 2017. Recognizing explicit and implicit hate speech using a weakly supervised two-path bootstrapping approach. arXiv:1710.07394. Retrieved from https://arxiv.org/abs/1710.07394.Google ScholarGoogle Scholar
  57. Njagi Dennis Gitari, Zhang Zuping, Hanyurwimfura Damien, and Jun Long. 2015. A lexicon-based approach for hate speech detection. Int. J. Multimedia Ubiq. Eng. 10, 4 (2015), 215--230.Google ScholarGoogle ScholarCross RefCross Ref
  58. Jennifer Golbeck, Zahra Ashktorab, Rashad O. Banjo, Alexandra Berlinger, Siddharth Bhagwan, Cody Buntain, Paul Cheakalos, Alicia A. Geller, Quint Gergory, Rajesh Kumar Gnanasekaran, et al. 2017. A large labeled corpus for online harassment research. In Proceedings of the 2017 ACM on Web Science Conference. ACM, 229--233.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Denis Gordeev. 2016. Automatic detection of verbal aggression for Russian and American imageboards. Proc. Soc. Behav. Sci. 236 (2016), 71--75.Google ScholarGoogle ScholarCross RefCross Ref
  60. Mario Graff, Sabino Miranda-Jiménez, Eric S. Tellez, Daniela Moctezuma, Vladimir Salgado, José Ortiz-Bejar, and Claudia N. Sánchez. 2018. Ingeotec at mex-a3t: Author profiling and aggressiveness analysis in twitter using tc and evomsa. In Proceedings of the 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (WS on IberEval’18).Google ScholarGoogle Scholar
  61. Edel Greevy and Alan F. Smeaton. 2004. Classifying racist texts using a support vector machine. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 468--469.Google ScholarGoogle Scholar
  62. Dorothy Wunmi Grigg. 2010. Cyber-aggression: Definition and concept of cyberbullying. Austr. J. Guid. Counsel. 20, 2 (2010), 143--156.Google ScholarGoogle ScholarCross RefCross Ref
  63. Fergyanto E. Gunawan, Livia Ashianti, Sevenpri Candra, and Benfano Soewito. 2016. Detecting online child grooming conversation. In Proceedings of the 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS’16). IEEE, 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  64. Aditi Gupta, Ponnurangam Kumaraguru, and Ashish Sureka. 2012. Characterizing pedophile conversations on the Internet using online grooming. CoRR abs/1208.4324 (2012).Google ScholarGoogle Scholar
  65. Batoul Haidar, Maroun Chamoun, and Ahmed Serhrouchni. 2017. A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst. J. 2, 6 (2017), 275--284.Google ScholarGoogle ScholarCross RefCross Ref
  66. Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq, Richard Han, Qin Lv, and Shivakant Mishr. 2015. Prediction of cyberbullying incidents on the Instagram social network. arXiv:1508.06257. Retrieved from https://arxiv.org/abs/1508.06257.Google ScholarGoogle Scholar
  67. Qianjia Huang, Diana Inkpen, Jianhong Zhang, and David Van Bruwaene. 2018. Cyberbullying Intervention interface based on convolutional neural networks. Proceedings of the International Conference on Computational Linguistics (COLING’18) (2018), 42.Google ScholarGoogle Scholar
  68. Muhammad Okky Ibrohim and Indra Budi. 2018. A dataset and preliminaries study for abusive language detection in Indonesian social media. Proc. Comput. Sci. 135 (2018), 222--229.Google ScholarGoogle ScholarCross RefCross Ref
  69. Borna Jafarpour, Stan Matwin, et al. 2018. Boosting text classification performance on sexist tweets by text augmentation and text generation using a combination of knowledge graphs. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2’18). 107--114.Google ScholarGoogle Scholar
  70. Harri Jalonen, Jarkko Paavola, Tuomo Helo, Mika Sartonen, and Aki-Mauri Huhtinen. 2016. Understanding the trolling phenomenon: The automated detection of bots and cyborgs in the social media. J. Inf. Warfare 15, 4 (2016), 100--111.Google ScholarGoogle Scholar
  71. Akshita Jha and Radhika Mamidi. 2017. When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data. In Proceedings of the 2nd Workshop on NLP and Computational Social Science. 7--16.Google ScholarGoogle ScholarCross RefCross Ref
  72. P. Jinju Joby and Jyothi Korra. 2015. Article: Message filtering on social media content. Proceedings of the International Conference on Current Trends in Advanced Computing (ICCTAC’15).1--4.Google ScholarGoogle Scholar
  73. Mladen Karan and Jan Šnajder. 2018. Cross-domain detection of abusive language online. In Proceedings of the Workshop on Abusive Language Online (ALW2’18). 132--137.Google ScholarGoogle ScholarCross RefCross Ref
  74. Imrul Kayes, Nicolas Kourtellis, Daniele Quercia, Adriana Iamnitchi, and Francesco Bonchi. 2015. The social world of content abusers in community question answering. In Proceedings of the 24th International Conference on World Wide Web. 570--580.Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. George J. Kennedy, Andrew W. McCollough, Edward Dixon, A. M. Parra Bastidas, J. Mark Ryan, and Chris Loo. 2017. Hack harassment: Technology solutions to combat online harassment. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 73--77.Google ScholarGoogle ScholarCross RefCross Ref
  76. April Kontostathis. 2009. ChatCoder: Toward the tracking and categorization of internet predators. In Proceedings of the Text Mining Workshop 2009 Held in Conjunction with the Ninth Siam International Conference on Data Mining.Google ScholarGoogle Scholar
  77. April Kontostathis, Lynne Edwards, and Amanda Leatherman. 2010. Text mining and cybercrime. Text Mining: Applications and Theory. John Wiley 8 Sons, Ltd, Chichester, UK.Google ScholarGoogle Scholar
  78. Ritesh Kumar, Aishwarya N. Reganti, Akshit Bhatia, and Tushar Maheshwari. 2018. Aggression-annotated corpus of Hindi-English code-mixed data. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC’18). ELRA.Google ScholarGoogle Scholar
  79. Irene Kwok and Yuzhou Wang. 2013. Locate the hate: Detecting tweets against blacks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI’13).Google ScholarGoogle Scholar
  80. Amanda Kay Leatherman. 2009. Luring language and virtual victims: Analyzing cyber-predators' online communicative behavior. Ursinus College, PA.Google ScholarGoogle Scholar
  81. Younghun Lee, Seunghyun Yoon, and Kyomin Jung. 2018. Comparative studies of detecting abusive language on Twitter. arXiv:1808.10245. Retrieved from https://arxiv.org/abs/1808.10245.Google ScholarGoogle Scholar
  82. Dan Liu, Ching Yee Suen, and Olga Ormandjieva. 2017. A novel way of identifying cyber predators. CoRR abs/1712.03903.Google ScholarGoogle Scholar
  83. Ping Liu, Joshua Guberman, Libby Hemphill, and Aron Culotta. 2018. Forecasting the presence and intensity of hostility on Instagram using linguistic and social features. arXiv:1804.06759. Retrieved from https://arxiv.org/abs/1804.06759.Google ScholarGoogle Scholar
  84. Rijul Magu, Kshitij Joshi, and Jiebo Luo. 2017. Detecting the hate code on social media. arXiv:1703.05443. Retrieved from https://arxiv.org/abs/1703.05443.Google ScholarGoogle Scholar
  85. Alessandro Maisto, Serena Pelosi, Simonetta Vietri, Pierluigi Vitale, and Via Giovanni Paolo II. 2017. Mining offensive language on social media. In Proceedings of the Italian Conference on Computational Linguistics (CLiC-it’17) 252.Google ScholarGoogle ScholarCross RefCross Ref
  86. Shervin Malmasi and Marcos Zampieri. 2017. Detecting hate speech in social media. arXiv:1712.06427. Retrieved from https://arxiv.org/abs/1712.06427.Google ScholarGoogle Scholar
  87. Shervin Malmasi and Marcos Zampieri. 2018. Challenges in discriminating profanity from hate speech. J. Exp. Theor. Artif. Intell. 30, 2 (2018), 187--202.Google ScholarGoogle ScholarCross RefCross Ref
  88. India McGhee, Jennifer Bayzick, April Kontostathis, Lynne Edwards, Alexandra McBride, and Emma Jakubowski. 2011. Learning to identify internet sexual predation. Int. J. Electr. Commerce 15, 3 (2011), 103--122.Google ScholarGoogle ScholarDigital LibraryDigital Library
  89. Yashar Mehdad and Joel Tetreault. 2016. Do characters abuse more than words? In Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL’16). 299--303.Google ScholarGoogle ScholarCross RefCross Ref
  90. Maxime Meyer. 2015. Machine learning to detect online grooming. Retrieved February 25, 2019 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260390.Google ScholarGoogle Scholar
  91. Md Waliur Rahman Miah, John Yearwood, and Sid Kulkarni. 2011. Detection of child exploiting chats from a mixed chat dataset as a text classification task. In Proceedings of the Australasian Language Technology Association Workshop 2011. 157--165.Google ScholarGoogle Scholar
  92. Mainack Mondal, Leandro Araújo Silva, and Fabrício Benevenuto. 2017. A measurement study of hate speech in social media. In Proceedings of the 28th ACM Conference on Hypertext and Social Media. ACM, 85--94.Google ScholarGoogle ScholarDigital LibraryDigital Library
  93. Joaquın Padilla Montani. 2018. TUWienKBS at GermEval 2018: German abusive tweet detection. In Proceedings of the 14th Conference on Natural Language Processing. Austrian Academy of Sciences, 45--50.Google ScholarGoogle Scholar
  94. Colin Morris. 2013. Identifying Online Sexual Predators by svm Classification with Lexical and Behavioral Features. Master of Science Thesis, University of Toronto, Canada (2013).Google ScholarGoogle Scholar
  95. Zewdie Mossie and Jenq-Haur Wang. 2018. Social network hate speech detection for Amharic language. In Proceedings of The 4th International Conference on Natural Language Computing. AIRCC Publishing Corporation, 41--55.Google ScholarGoogle ScholarCross RefCross Ref
  96. Hamdy Mubarak, Kareem Darwish, and Walid Magdy. 2017. Abusive language detection on Arabic social media. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 52--56.Google ScholarGoogle ScholarCross RefCross Ref
  97. Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohamad Sharef, Aida Mustapha, and Somayeh Shojaee. 2013. A review of cyberbullying detection: An overview. In Proceedings of the 2013 13th International Conference on Intellient Systems Design and Applications. 325--330.Google ScholarGoogle ScholarCross RefCross Ref
  98. Vinita Nahar, Xue Li, and Chaoyi Pang. 2013. An effective approach for cyberbullying detection. Commun. Inf. Sci. Manage. Eng. 3, 5 (2013), 238.Google ScholarGoogle Scholar
  99. Parma Nand, Rivindu Perera, and Abhijeet Kasture. 2016. “How bullying is this message?”: A psychometric thermometer for bullying. In Proceedings of the International Conference on Computational Linguistics (COLING’16): Technical Papers. 695--706.Google ScholarGoogle Scholar
  100. Cnythia Hombakazi Ngejane, Gugulethu Mabuza-Hocquet, Jan H. P. Eloff, and Samuel Lefophane. 2018. Mitigating online sexual grooming cybercrime on social media using machine learning: A desktop survey. In Proceedings of the International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD’18), 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  101. Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016. Abusive language detection in online user content. In Proceedings of the 25th International Conference on the World Wide Web. 145--153.Google ScholarGoogle ScholarDigital LibraryDigital Library
  102. Cicero Nogueira dos Santos, Igor Melnyk, and Inkit Padhi. 2018. Fighting offensive language on social media with unsupervised text style transfer. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). ACLs, 189--194.Google ScholarGoogle ScholarCross RefCross Ref
  103. A. I. of Criminology (AIC). 2008. Online Child Grooming Laws. Technical Report.Google ScholarGoogle Scholar
  104. Loreen N. Olson, Joy L. Daggs, Barbara L. Ellevold, and Teddy K. K. Rogers. 2007. Entrapping the innocent: Toward a theory of child sexual predators’ luring communication. Commun. Theory 17, 3 (2007), 231--251.Google ScholarGoogle ScholarCross RefCross Ref
  105. Rachel O’Connell. 2003. A Typology of Child Cybersexploitation and Online Grooming Practices. Technical Report. University of Central Lancashire.Google ScholarGoogle Scholar
  106. Jure Pajić, Mateo Šimonović, and Rafael Slekovac. 2016. Offensive text detection with Naive bayes classifier. In Proceedings of the TAR 2016. University of Zagreb, 55--57.Google ScholarGoogle Scholar
  107. Endang Wahyu Pamungkas, Alessandra Teresa Cignarella, Valerio Basile, and Viviana Patti. 2018. 14-ExLab@ UniTo for AMI at IberEval2018: Exploiting lexical knowledge for detecting misogyny in English and Spanish tweets. Proceedings of the Evaluation of Human Language Technologies for Iberian Languages (IberEval’18). 234--241.Google ScholarGoogle Scholar
  108. Alexander Panchenko, Richard Beaufort, and Cedrick Fairon. 2012. Detection of child sexual abuse media on p2p networks: Normalization and classification of associated filenames. In Proceedings of the LREC Workshop on Language Resources for Public Security Applications. 27--31.Google ScholarGoogle Scholar
  109. Javier Parapar, David E. Losada, and Alvaro Barreiro. 2012. A learning-based approach for the identification of sexual predators in chat logs. In Proceedings of the Conference and Labs of the Evaluation Forum (CLEF’12) (Online Working Notes/Labs/Workshop).Google ScholarGoogle Scholar
  110. Javier Parapar, David E. Losada, and Alvaro Barreiro. 2014. Combining psycho-linguistic, content-based and chat-based features to detect predation in chatrooms.J. Univers. Comput. Sci. 20, 2 (2014), 213--239.Google ScholarGoogle Scholar
  111. Ji Ho Park and Pascale Fung. 2017. One-step and two-step classification for abusive language detection on Twitter. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 41--45.Google ScholarGoogle ScholarCross RefCross Ref
  112. John Pavlopoulos, Prodromos Malakasiotis, and Ion Androutsopoulos. 2017. Deep learning for user comment moderation. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 25--35.Google ScholarGoogle ScholarCross RefCross Ref
  113. Nick Pendar. 2007. Toward spotting the pedophile telling victim from predator in text chats. In Proceedings of the International Conference on Semantic Computing (ICSC’07). IEEE, 235--241.Google ScholarGoogle ScholarDigital LibraryDigital Library
  114. Georgios K. Pitsilis, Heri Ramampiaro, and Helge Langseth. 2018. Effective hate-speech detection in Twitter data using recurrent neural networks. Appl. Intell. 48, 12 (2018), 4730--4742.Google ScholarGoogle ScholarDigital LibraryDigital Library
  115. Fabio Poletto, Marco Stranisci, Manuela Sanguinetti, Viviana Patti, and Cristina Bosco. 2017. Hate speech annotation: Analysis of an Italian Twitter corpus. In Proceedings of the CEUR Workshop(CEUR-WS’17), Vol. 2006. 1--6.Google ScholarGoogle ScholarCross RefCross Ref
  116. Nektaria Potha and Manolis Maragoudakis. 2014. Cyberbullying detection using time series modeling. In Proceedings of the IEEE International Conference on Data Mining Workshop (ICDMW’14). IEEE, 373--382.Google ScholarGoogle ScholarCross RefCross Ref
  117. Michal Ptaszynski, Juuso Kalevi Kristian Eronen, and Fumito Masui. 2017. Learning deep on cyberbullying is always better than brute force. In Proceedings of the IJCAI 2017 3rd Workshop on Linguistic and Cognitive Approaches to Dialogue Agents (LaCATODA’17). 19--25.Google ScholarGoogle Scholar
  118. Michal Ptaszynski, Fumito Masui, Taisei Nitta, Suzuha Hatakeyama, Yasutomo Kimura, Rafal Rzepka, and Kenji Araki. 2016. Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization. Int. J. Child-Comput. Interact. 8 (2016), 15--30.Google ScholarGoogle ScholarCross RefCross Ref
  119. Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang. 2018. Hierarchical CVAE for fine-grained hate speech classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. ACL, 3550--3559.Google ScholarGoogle ScholarCross RefCross Ref
  120. Kashyap Raiyani, Teresa Gonçalves, Paulo Quaresma, and Vitor Beires Nogueira. 2018. Fully connected neural network with advance preprocessor to identify aggression over facebook and twitter. In Proceedings of the 1st Workshop on Trolling, Aggression and Cyberbullying (TRAC’18). 28--41.Google ScholarGoogle Scholar
  121. Amir H. Razavi, Diana Inkpen, Sasha Uritsky, and Stan Matwin. 2010. Offensive language detection using multi-level classification. In Proceedings of the Canadian Conference on Artificial Intelligence. Springer, 16--27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  122. Kelly Reynolds, April Kontostathis, and Lynne Edwards. 2011. Using machine learning to detect cyberbullying. In Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops (ICMLA’11), Vol. 2. IEEE, 241--244.Google ScholarGoogle ScholarDigital LibraryDigital Library
  123. Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie L. Shalin, and Amit Sheth. 2018. A quality type-aware annotated corpus and lexicon for harassment research. In Proceedings of the International ACM Conference on Web Science (WebSci’18). ACM, 33--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  124. Manoel Ribeiro, Pedro Calais, Yuri Santos, Virgílio Almeida, and Wagner Meira Jr. 2018. Characterizing and detecting hateful users on Twitter. CoRR, abs/1803.08977.Google ScholarGoogle Scholar
  125. Manoel Horta Ribeiro, Pedro H. Calais, Yuri A. Santos, Virgílio A. F. Almeida, and Wagner Meira Jr.2018. “Like sheep among wolves”: Characterizing hateful users on Twitter. CoRR abs/1801.00317.Google ScholarGoogle Scholar
  126. Julian Risch and Ralf Krestel. 2018. Delete or not delete? Semi-automatic comment moderation for the newsroom. In Proceedings of the 1st Workshop on Trolling, Aggression and Cyberbullying (TRAC’18). 166--176.Google ScholarGoogle Scholar
  127. David Robinson, Ziqi Zhang, and Jonathan Tepper. 2018. Hate speech detection on Twitter: Feature engineering vs feature selection. In Proceedings of the European Semantic Web Conference. Springer, 46--49.Google ScholarGoogle ScholarCross RefCross Ref
  128. Björn Ross, Michael Rist, Guillermo Carbonell, Benjamin Cabrera, Nils Kurowsky, and Michael Wojatzki. 2016. Measuring the reliability of hate speech annotations: The case of the European refugee crisis. CoRR abs/1701.08118.Google ScholarGoogle Scholar
  129. Semiu Salawu, Yulan He, and Joanna Lumsden. 2018. Approaches to automated detection of cyberbullying: A survey. IEEE Trans. Affect. Comput. 11, 1 (2018), 3--24.Google ScholarGoogle ScholarCross RefCross Ref
  130. Haji Mohammad Saleem, Kelly P. Dillon, Susan Benesch, and Derek Ruths. 2017. A web of hate: Tackling hateful speech in online social spaces. arxiv:1709.10159. Retrieved from http://arxiv.org/abs/1709.10159.Google ScholarGoogle Scholar
  131. Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-gyo Jung, Jisun An, Haewoon Kwak, and Bernard J. Jansen. 2018. Anatomy of online hate: Developing a taxonomy and machine learning models for identifying and classifying hate in online news media. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’18). 330--339.Google ScholarGoogle Scholar
  132. Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, and Thamar Solorio. 2017. Detecting nastiness in social media. In Proceedings of the 1st Workshop on Abusive Language Online. 63--72.Google ScholarGoogle ScholarCross RefCross Ref
  133. Manuela Sanguinetti, Fabio Poletto, Cristina Bosco, Viviana Patti, and Marco Stranisci. 2018. An Italian Twitter corpus of hate speech against immigrants. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC’18).Google ScholarGoogle Scholar
  134. Sasha Sax. 2016. Flame Wars: Automatic Insult Detection.Google ScholarGoogle Scholar
  135. Anna Schmidt and Michael Wiegand. 2017. A survey on hate speech detection using natural language processing. In Proceedings of the 5th International Workshop on Natural Language Processing for Social Media. 1--10.Google ScholarGoogle ScholarCross RefCross Ref
  136. Leandro Araújo Silva, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. 2016. Analyzing the targets of hate in online social media. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM’16). 687--690.Google ScholarGoogle Scholar
  137. Sugan Sirihattasak, Mamoru Komachi, and Hiroshi Ishikawa. 2018. Annotation and classification of toxicity for Thai Twitter. In Proceedings of LREC 2018 Workshop and the 2nd Workshop on Text Analytics for Cybersecurity and Online Safety (TA-COS’18).Google ScholarGoogle Scholar
  138. Peter Smith, Jess Mahdavi, Manuel Carvalho, Sonja Fisher, Shanette Russell, and Neil Tippett. 2008. Cyberbullying: Its nature and impact in secondary school pupils. J. Child Psychol. Psychiatr. Allied Discipl. 49, 4 (2008), 376--85.Google ScholarGoogle ScholarCross RefCross Ref
  139. Sara Owsley Sood, Judd Antin, and Elizabeth F. Churchill. 2012. Using crowdsourcing to improve profanity detection. In Proceedings of the AAAI Spring Symposium: Wisdom of the Crowd, Vol. 12. 06.Google ScholarGoogle Scholar
  140. Frederik Stjernfelt and Anne Mette Lauritzen. 2020. Your Post has been Removed: Tech Giants and Freedom of Speech. Springer.Google ScholarGoogle Scholar
  141. Hui-Po Su, Zhen-Jie Huang, Hao-Tsung Chang, and Chuan-Jie Lin. 2017. Rephrasing profanity in Chinese text. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 18--24.Google ScholarGoogle ScholarCross RefCross Ref
  142. Andrej Švec, Matúš Pikuliak, Marian Simko, and Maria Bielikova. 2018. Improving moderation of online discussions via interpretable neural models. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2’18). Association for Computational Linguistics, 60--65.Google ScholarGoogle ScholarCross RefCross Ref
  143. Michele Tomaiuolo, Gianfranco Lombardo, Monica Mordonini, Stefano Cagnoni, and Agostino Poggi. 2020. A survey on troll detection. Fut. Internet 12, 2 (2020).Google ScholarGoogle Scholar
  144. Jan Tomljanovic, Luka Zuanovic, and Tomislav Šebrek. 2016. Sexual predator identification using word2vec features. In Proceedings of the Text Analysis and Retrieval 2016 (TAR'16). University of Zagreb, 70--72.Google ScholarGoogle Scholar
  145. Stéphan Tulkens, Lisa Hilte, Elise Lodewyckx, Ben Verhoeven, and Walter Daelemans. 2016. The automated detection of racist discourse in dutch social media. Comput. Ling. Netherlands J. 6, 1 (2016), 3--20.Google ScholarGoogle Scholar
  146. Elise Fehn Unsvåg and Björn Gambäck. 2018. The effects of user features on Twitter hate speech detection. In Proceedings of the Workshop on Abusive Language Online (ALW2’18). ACL, 75--85.Google ScholarGoogle ScholarCross RefCross Ref
  147. Betty van Aken, Julian Risch, Ralf Krestel, and Alexander Löser. 2018. Challenges for toxic comment classification: An in-depth error analysis. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2’18). Association for Computational Linguistics, 33--42.Google ScholarGoogle ScholarCross RefCross Ref
  148. Cynthia Van Hee, Els Lefever, Ben Verhoeven, Julie Mennes, Bart Desmet, Guy De Pauw, Walter Daelemans, and Véronique Hoste. 2015. Automatic detection and prevention of cyberbullying. In Proceedings of the International Conference on Human and Social Analytics (HUSO’15). IARIA, 13--18.Google ScholarGoogle Scholar
  149. Fabio Del Vigna, Andrea Cimino, Felice Dell’Orletta, Marinella Petrocchi, and Maurizio Tesconi. 2017. Hate me, hate me not: Hate speech detection on Facebook. In Proceedings of the Italian Conference on CyberSecurity (ITASEC’17).Google ScholarGoogle Scholar
  150. Esaú Villatoro-Tello, Antonio Juárez-González, Hugo Jair Escalante, Manuel Montes-y Gómez, and Luis Villasenor Pineda. 2012. A two-step approach for effective detection of misbehaving users in chats. In Proceedings of the Conference and Labs of the Evaluation Forum (CLEF’12) (Online Working Notes/Labs/Workshop).Google ScholarGoogle Scholar
  151. William Warner and Julia Hirschberg. 2012. Detecting hate speech on the world wide web. In Proceedings of the 2nd Workshop on Language Analysis in Social Media (LASM’12). ACL, 19--26.Google ScholarGoogle Scholar
  152. Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017. Understanding abuse: A typology of abusive language detection subtasks. In Proceedings of the Workshop on Abusive Language Online (ALW1’17). ACL, 78--84.Google ScholarGoogle ScholarCross RefCross Ref
  153. Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In Proceedings of the NAACL Student Research Workshop. 88--93.Google ScholarGoogle ScholarCross RefCross Ref
  154. Hajime Watanabe, Mondher Bouazizi, and Tomoaki Ohtsuki. 2018. Hate speech on Twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6 (2018), 13825--13835.Google ScholarGoogle ScholarCross RefCross Ref
  155. Ellery Wulczyn, Nithum Thain, and Lucas Dixon. 2017. Ex machina: Personal attacks seen at scale. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1391--1399.Google ScholarGoogle ScholarDigital LibraryDigital Library
  156. Guang Xiang, Bin Fan, Ling Wang, Jason Hong, and Carolyn Rose. 2012. Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM, 1980--1984.Google ScholarGoogle ScholarDigital LibraryDigital Library
  157. Zhi Xu and Sencun Zhu. 2010. Filtering offensive language in online communities using grammatical relations. In Proceedings of the 7th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference. 1--10.Google ScholarGoogle Scholar
  158. Fan Yang, Xiaochang Peng, Gargi Ghosh, Reshef Shilon, Hao Ma, Eider Moore, and Goran Predovic. 2019. Exploring deep multimodal fusion of text and photo for hate speech classification. In Proceedings of the 3th Workshop on Abusive Language Online. ACL, 11--18.Google ScholarGoogle ScholarCross RefCross Ref
  159. Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. Davison, April Kontostathis, and Lynne Edwards. 2009. Detection of harassment on Web 2.0. Proc. Content Anal. Web 2.0 2, 1 (2009), 1--7.Google ScholarGoogle Scholar
  160. Patricio Zambrano, Jenny Torres, Luis Tello-Oquendo, Rubeén Jácome, Marco E. Benalcázar, Roberto Andrade, and Walter Fuertes. 2019. Technical mapping of the grooming anatomy using machine learning paradigms: An information security approach. IEEE Access 7 (2019), 142129--142146.Google ScholarGoogle ScholarCross RefCross Ref
  161. Xhemal Zenuni, Jaumin Ajdari, Florije Ismaili, and Bujar Raufi. 2017. Automatic hate speech detection in online contents using latent semantic analysis. PressAcad. Proc. 5, 1 (2017), 368--371.Google ScholarGoogle ScholarCross RefCross Ref
  162. Ziqi Zhang, David Robinson, and Jonathan Tepper. 2018. Detecting hate speech on Twitter using a convolution-GRU based deep neural network. In Proceedings of the European Semantic Web Conference. Springer, 745--760.Google ScholarGoogle ScholarCross RefCross Ref
  163. Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Griffin, David J. Miller, and Cornelia Caragea. 2016. Content-driven detection of cyberbullying on the Instagram social network. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’16). 3952--3958.Google ScholarGoogle Scholar
  164. Steven Zimmerman, Udo Kruschwitz, and Chris Fox. 2018. Improving hate speech detection with deep learning ensembles. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC’18).Google ScholarGoogle Scholar
  165. Zheming Zuo, Jie Li, Philip Anderson, Longzhi Yang, and Nitin Naik. 2018. Grooming detection using fuzzy-rough feature selection and text classification. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’18). IEEE, 1--8.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Cyber-aggression, Cyberbullying, and Cyber-grooming: A Survey and Research Challenges

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 54, Issue 1
      January 2022
      844 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/3446641
      Issue’s Table of Contents

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 January 2021
      • Accepted: 1 August 2020
      • Revised: 1 July 2020
      • Received: 1 April 2019
      Published in csur Volume 54, Issue 1

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format