A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 64, TOP 7
Turn off MathJax
Article Contents
S. W. Wang, X. Q. Zhu, W. P. Ding, and  A. A. Yengejeh,  “Cyberbullying and cyberviolence detection: A triangular user-activity-content view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1384–1405, Aug. 2022. doi: 10.1109/JAS.2022.105740
Citation: S. W. Wang, X. Q. Zhu, W. P. Ding, and  A. A. Yengejeh,  “Cyberbullying and cyberviolence detection: A triangular user-activity-content view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1384–1405, Aug. 2022. doi: 10.1109/JAS.2022.105740

Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View

doi: 10.1109/JAS.2022.105740
Funds:  This work was partially supported by the U.S. National Science Foundation (CNS-1828181, IIS-1763452), and by a seed grant of the College of Engineering and Computer Science, Florida Atlantic University
More Information
  • Recent years have witnessed the increasing popularity of mobile and networking devices, as well as social networking sites, where users engage in a variety of activities in the cyberspace on a daily and real-time basis. While such systems provide tremendous convenience and enjoyment for users, malicious usages, such as bullying, cruelty, extremism, and toxicity behaviors, also grow noticeably, and impose significant threats to individuals and communities. In this paper, we review computational approaches for cyberbullying and cyberviolence detection, in order to understand two major factors: 1) What are the defining features of online bullying users, and 2) How to detect cyberbullying and cyberviolence. To achieve the goal, we propose a user-activities-content (UAC) triangular view, which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate content. Accordingly, we categorize cyberbully features into three main categories: 1) User centered features, 2) Content centered features, and 3) Activity centered features. After that, we review methods for cyberbully detection, by taking supervised, unsupervised, transfer learning, and deep learning, etc., into consideration. The UAC centered view provides a coherent and complete summary about features and characteristics of online users (their activities), approaches to detect bullying users (and malicious content), and helps defend cyberspace from bullying and toxicity.

     

  • loading
  • [1]
    D. Olweus, “Victimization by peers: Antecedents and long-term outcomes,” in Social Withdrawal, Inhibition, and Shyness in Childhood, K. H. Rubin and J. Asendorpf, Eds. Hillsdale, Canada: Psychology Press, 1993, pp. 315–341.
    [2]
    K. Smith, K. C. Madsen, and J. C. Moody, “What causes the age decline in reports of being bullied at school? Towards a developmental analysis of risks of being bullied” Educ. Res., vol. 41, no. 3, pp. 267–285, 1999. doi: 10.1080/0013188990410303
    [3]
    A. Lenhart, M. Madden, A. Smith, K. Purcell, K. Zickuhr, and L. Rainie, “Teens, kindness and cruelty on social network sites: How American teens navigate the new world of “digital citizenship”.” [Online]. Available: https://eric.ed.gov/?id=ED537516.
    [4]
    National Crime Prevention Council, An explanation of the growing phenomenon of cyberbullying. [Online]. Available: https://www.ncpc.org/resources/cyberbullying//what-is-cyberbullying/.
    [5]
    E. Englander, E. Donnerstein, R. Kowalski, C. A. Lin, and K. Parti, “Defining cyberbullying,” Pediatrics, vol. 140, no. S2, pp. S148–S151, Nov. 2017.
    [6]
    T. Beran and Q. Li, “Cyber-harassment: A study of a new method for an old behavior,” J. Educ. Comput. Res., vol. 32, no. 3, pp. 265–277, Apr. 2005. doi: 10.2190/8YQM-B04H-PG4D-BLLH
    [7]
    P. Bocij, Cyberstalking: Harassment in the Internet Age and How to Protect Your Family. Westport, USA: Greenwood Publishing Group, 2004.
    [8]
    B. W. Reyns and B. S. Fisher, “Being pursued online: Applying cyberlifestyle–routine activities theory to cyberstalking victimization,” Crim. Justice Behav., vol. 38, no. 11, pp. 1149–1169, Nov. 2011. doi: 10.1177/0093854811421448
    [9]
    J. M. Allen and G. H. Norris, “Is genocide different? Dealing with hate speech in a post-genocide society,” J. Int. Law Int. Relat., vol. 7, 2011.
    [10]
    B. Perry, B. Levin, P. Iganski, R. Blazak, and F. M. Lawrence, Hate Crimes. Westport, USA: Greenwood Publishing Group, 2009.
    [11]
    J. Hawdon, A. Oksanen, and Räsänen, “Online extremism and online hate: Exposure among adolescents and young adults in four nations,” Nord. Inf., vol. 37, no. 3–4, pp. 29–37, Dec. 2015.
    [12]
    National Center for Education Statistics, Bullying at school and electronic bullying. [Online]. Available: https://nces.ed.gov/programs/coe/indicator/a10/.
    [13]
    J. Wang, R. J. Iannotti, and T. R. Nansel, “School bullying among adolescents in the united states: Physical, verbal, relational, and cyber,” J. Adolesc. Health, vol. 45, no. 4, pp. 368–75, Oct. 2009. doi: 10.1016/j.jadohealth.2009.03.021
    [14]
    L. Cheng, Y. N. Silva, D. Hall, and H. Liu, “Session-based cyberbullying detection: Problems and challenges,” IEEE Internet Comput., vol. 25, no. 2, pp. 66–72, Mar.–Apr. 2021. doi: 10.1109/MIC.2020.3032930
    [15]
    V. Balakrishnan, S. Khan, T. Fernandez, and H. R. Arabnia, “Cyberbullying detection on twitter using big five and dark triad features,” Pers. Individ. Differ., vol. 141, pp. 252–257, Apr. 2019. doi: 10.1016/j.paid.2019.01.024
    [16]
    C. Iwendi, G. Srivastava, S. Khan, and P. K. R. Maddikunta, “Cyberbullying detection solutions based on deep learning architectures,” Multimedia Syst., pp. 1–14, 2020.
    [17]
    B. Haidar, M. Chamoun, and F. Yamout, “Cyberbullying detection: A survey on multilingual techniques,” in Proc. European Modelling Symp., Pisa, Italy, 2016, pp. 165–171.
    [18]
    A. Alakrot and N. S. Nikolov, “A survey of text mining approaches to cyberbullying detection in online communication flows,” in Proc. NUI Galway-UL Alliance 5th Postgraduate Research Day 2015, Galway, Ireland.
    [19]
    S. Salawu, Y. L. He, and J. Lumsden, “Approaches to automated detection of cyberbullying: A survey,” IEEE Trans. Affect. Comput., vol. 11, no. 1, pp. 3–24, Jan.–Mar. 2020. doi: 10.1109/TAFFC.2017.2761757
    [20]
    F. Elsafoury, S. Katsigiannis, Z. Pervez, and N. Ramzan, “When the timeline meets the pipeline: A survey on automated cyberbullying detection,” IEEE Access, vol. 9, pp. 103541–103563, Jul. 2021. doi: 10.1109/ACCESS.2021.3098979
    [21]
    K. M. Douglas, C. McGarty, A. M. Bliuc, and G. Lala, “Understanding cyberhate: Social competition and social creativity in online white supremacist groups,” Soc. Sci. Comput. Rev., vol. 23, no. 1, pp. 68–76, Feb. 2005. doi: 10.1177/0894439304271538
    [22]
    K. M. Douglas, “Psychology, discrimination and hate groups online,” in Oxford Handbook of Internet Psychology, A. N. Joinson, K. Y. A. McKenna, T. Postmes, and U. D. Reips, Eds. Oxford, UK: Oxford University Press, 2009, pp. 155–164.
    [23]
    A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, “You are who you know: Inferring user profiles in online social networks,” in Proc. 3rd ACM Int. Conf. Web Search and Data Mining, New York, USA, 2010, pp. 251–260.
    [24]
    H. Hosseinmardi, S. A. Mattson, R. I. Rafiq, R. Han, Q. Lv, and S. Mishra, “Detection of cyberbullying incidents on the instagram social network,” arXiv preprint arXiv: 1503.03909, 2015.
    [25]
    Q. J. Huang, V. K. Singh, and P. K. Atrey, “Cyber bullying detection using social and textual analysis,” in Proc. 3rd Int. Workshop on Socially-Aware Multimedia, Orlando, USA, 2014, pp. 3–6.
    [26]
    A. Squicciarini, S. Rajtmajer, Y. Liu, and C. Griffin, “Identification and characterization of cyberbullying dynamics in an online social network,” in Proc. IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Paris, France, 2015, pp. 280–285.
    [27]
    M. A. Al-Garadi, M. R. Hussain, N. Khan, G. Murtaza, H. F. Nweke, I. Ali, G. Mujtaba, H. Chiroma, H. A. Khattak, and A. Gani, “Predicting cyberbullying on social media in the big data era using machine learning algorithms: Review of literature and open challenges,” IEEE Access, vol. 7, pp. 70701–70718, May 2019. doi: 10.1109/ACCESS.2019.2918354
    [28]
    J. D. Wright, Int. Encyclopedia of the Social & Behavioral Sciences. 2nd ed. Amsterdam, Netherlands: Elsevier, 2015.
    [29]
    C. Duarte, S. K. Pittman, M. M. Thorsen, R. M. Cunningham, and M. L. Ranney, “Correlation of minority status, cyberbullying, and mental health: A cross-sectional study of 1031 adolescents,” J. Child Adolesc. Trauma, vol. 11, no. 1, pp. 39–48, Mar. 2018. doi: 10.1007/s40653-018-0201-4
    [30]
    M. Dadvar, F. M. G. de Jong, R. J. F. Ordelman, and R. B. Trieschnigg, “Improved cyberbullying detection using gender information,” in Proc. 12th Dutch-Belgian Information Retrieval Workshop, Ghent, USA, 2012, pp. 23–25.
    [31]
    R. M. Kowalski, G. W. Giumetti, A. N. Schroeder, and M. R. Lattanner, “Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth,” Psychol. Bull., vol. 140, no. 4, pp. 1073–1137, Jul. 2014. doi: 10.1037/a0035618
    [32]
    V. Nahar, S. Al-Maskari, X. Li, and C. Y. Pang, “Semi-supervised learning for cyberbullying detection in social networks,” in Proc. 25th Australasian Database Conf. Databases Theory and Applications, Brisbane, Australia, 2014, pp. 160–171.
    [33]
    M. Dadvar, R. Ordelman, F. de Jong, and D. Trieschnigg, “Towards user modelling in the combat against cyberbullying,” in Proc. 17th Int. Conf. Applications of Natural Language to Information Systems, Groningen, Netherlands, 2012, pp. 277–283.
    [34]
    D. Chatzakou, I. Leontiadis, J. Blackburn, E. de Cristofaro, G. Stringhini, A. Vakali, and N. Kourtellis, “Detecting cyberbullying and cyberaggression in social media,” ACM Trans. Web, vol. 13, no. 3, pp. 1–51, Aug. 2019.
    [35]
    M. McCord and M. Chuah, “Spam detection on twitter using traditional classifiers,” in Proc. 8th Int. Conf. Autonomic and Trusted Computing, Banff, Canada, 2011, pp. 175–186.
    [36]
    T. Jr. Costa and R. R. McCrae, “Four ways five factors are basic,” Pers. Individ. Differ., vol. 13, no. 6, pp. 653–665, Jun. 1992. doi: 10.1016/0191-8869(92)90236-I
    [37]
    O. P. John and S. Srivastava, “The big five trait taxonomy: History, measurement, and theoretical perspectives,” in Handbook of Personality: Theory and Research, L. A. Pervin and O. P. John, Eds. New York, USA: Guilford Press, 1999, pp. 102–138.
    [38]
    L. R. Goldberg, J. A. Johnson, H. W. Eber, R. Hogan, M. C. Ashton, C. R. Cloninger, and H. G. Gough, “The international personality item pool and the future of public-domain personality measures,” J. Res. Pers., vol. 40, no. 1, pp. 84–96, Feb. 2006. doi: 10.1016/j.jrp.2005.08.007
    [39]
    L. R. Goldberg, “A broad-bandwidth, public domain, personality inventory measuring the lower-level facets of several five-factor models,” in Personality Psychology in Europe, I. Mervielde, I. Deary, F. de Fruyt, and F. Ostendorf, Eds, Tilburg, Netherlands: Tilburg University Press, 1999, pp. 7–28.
    [40]
    D. L. Paulhus and K. M. Williams, “The dark triad of personality: Narcissism, machiavellianism, and psychopathy,” J. Res. Pers., vol. 36, no. 6, pp. 556–563, Dec. 2002. doi: 10.1016/S0092-6566(02)00505-6
    [41]
    K. Jonason and G. D. Webster, “The dirty dozen: A concise measure of the dark triad,” Psychol. Assess., vol. 22, no. 2, pp. 420–432, Jun. 2010. doi: 10.1037/a0019265
    [42]
    S. Jakobwitz and V. Egan, “The dark triad and normal personality traits,” Pers. Individ. Differ., vol. 40, no. 2, pp. 331–339, Jan. 2006. doi: 10.1016/j.paid.2005.07.006
    [43]
    H. Douglas, M. Bore, and D. Munro, “Distinguishing the dark triad: Evidence from the five-factor model and the Hogan development survey,” Psychology, vol. 3, no. 3, pp. 237–242, Mar. 2012. doi: 10.4236/psych.2012.33033
    [44]
    M. van Geel, A. Goemans, F. Toprak, and Vedder, “Which personality traits are related to traditional bullying and cyberbullying? A study with the big five, dark triad and sadism” Pers. Individ. Differ., vol. 106, pp. 231–235, Feb. 2017. doi: 10.1016/j.paid.2016.10.063
    [45]
    R. Festl and T. Quandt, “Social relations and cyberbullying: The influence of individual and structural attributes on victimization and perpetration via the internet,” Hum. Commun. Res., vol. 39, no. 1, pp. 101–126, Jan. 2013. doi: 10.1111/j.1468-2958.2012.01442.x
    [46]
    A. K. Goodboy and M. M. Martin, “The personality profile of a cyberbully: Examining the dark triad,” Comput. Hum. Behav., vol. 49, pp. 1–4, Aug. 2015. doi: 10.1016/j.chb.2015.02.052
    [47]
    R. Ang, K. A. Tan, and A. T. Mansor, “Normative beliefs about aggression as a mediator of narcissistic exploitativeness and cyberbullying,” J. Interpers. Violence, vol. 26, no. 13, pp. 2619–2634, Dec. 2010.
    [48]
    S. Pabian, C. J. S. de Backer, and H. Vandebosch, “Dark triad personality traits and adolescent cyber-aggression,” Pers. Individ. Differ., vol. 75, pp. 41–46, Mar. 2015. doi: 10.1016/j.paid.2014.11.015
    [49]
    M. F. Yao, C. Chelmis, and D. S. Zois, “Cyberbullying ends here: Towards robust detection of cyberbullying in social media,” in Proc. World Wide Web Conf., San Francisco, USA, 2019, pp. 3427–3433.
    [50]
    A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proc. 7th ACM SIGCOMM Conf. Internet Measurement, San Diego, USA, 2007, pp. 29–42.
    [51]
    M. Pennacchiotti and A. M. Popescu, “Democrats, republicans and starbucks afficionados: User classification in twitter,” in Proc. 17th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Diego, USA, 2011, pp. 430–438.
    [52]
    M. de Choudhury, W. A. Mason, J. M. Hofman, and D. J. Watts, “Inferring relevant social networks from interpersonal communication,” in Proc. 19th Int. Conf. World Wide Web, Raleigh, USA, 2010, pp. 301–310.
    [53]
    J. F. Mancilla-Caceres, D. Espelage, and E. Amir, “A computer game-based method for studying bullying and cyberbullying,” J. Sch. Violence, vol. 14, no. 1, pp. 66–86, 2015.
    [54]
    V. Nahar, S. Unankard, X. Li, and C. Y. Pang, “Sentiment analysis for effective detection of cyber bullying,” in Proc. 14th Asia-Pacific Web Conf. Web Technologies and Applications, Kunming, China, 2012, pp. 767–774.
    [55]
    C. Ziems, Y. Vigfusson, and F. Morstatter, “Aggressive, repetitive, intentional, visible, and imbalanced: Refining representations for cyberbullying classification,” in Proc. 14th Int. AAAI Conf. Web and Social Media, Atlanta, USA, 2020, 808–819.
    [56]
    E. Papegnies, V. Labatut, R. Dufour, and G. Linarès, “Graph-based features for automatic online abuse detection,” in Proc. 5th Int. Conf. Statistical Language and Speech Processing, Le Mans, France, 2017, pp. 70–81.
    [57]
    C. Chelmis, D. S. Zois, and M. F. Yao, “Mining patterns of cyberbullying on twitter,” in Proc. IEEE Int. Conf. Data Mining Workshops, New Orleans, USA, 2017, pp. 126–133.
    [58]
    V. Bindu, S. Thilagam, and D. Ahuja, “Discovering suspicious behavior in multilayer social networks,” Comput. Hum. Behav., vol. 73, pp. 568–582, Aug. 2017. doi: 10.1016/j.chb.2017.04.001
    [59]
    R. M. Kowalski and S. Limber, “Psychological, physical, and academic correlates of cyberbullying and traditional bullying,” J. Adolesc. Health, vol. 53, no. 1, pp. S13–20, Jul. 2013. doi: 10.1016/j.jadohealth.2012.09.018
    [60]
    D. Soni and V. Singh, “Time reveals all wounds: Modeling temporal characteristics of cyberbullying,” in Proc. 12th Int. AAAI Conf. Web and Social Media, Stanford, USA, 2018, pp. 684–687.
    [61]
    A. Gupta, W. X. Yang, D. Sivakumar, Y. Silva, D. Hall, and M. N. Barioni, “Temporal properties of cyberbullying on instagram,” in Proc. Companion Web Conf., Taipei, China, 2020, 576–583.
    [62]
    J. Kleinberg, “Bursty and hierarchical structure in streams,” in Proc. Eighth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Alberta, Canada, 2002, 91–101.
    [63]
    S. Y. Ge, L. Cheng, and H. Liu, “Improving cyberbullying detection with user interaction,” in Proc. Web Conf., Ljubljana, Slovenia, 2021, pp. 496–506.
    [64]
    Noviantho, S. M. I. Isa, and L. Ashianti, “Cyberbullying classification using text mining,” in Proc. 1st Int. Conf. Informatics and Computational Sciences, Semarang, Indonesia, 2017, pp. 241–246.
    [65]
    S. Nadali, M. A. A. Murad, N. M. Sharef, A. Mustapha, and S. Shojaee, “A review of cyberbullying detection: An overview,” in Proc. 13th Int. Conf. Intellient Systems Design and Applications, Salangor, Malaysia, 2013, pp. 325–330.
    [66]
    N. Tahmasbi and E. Rastegari, “A socio-contextual approach in automated detection of public cyberbullying on twitter,” ACM Trans. Soc. Comput., vol. 1, no. 4, Dec. 2018.
    [67]
    R. Zhao, A. N. Zhou, and K. Z. Mao, “Automatic detection of cyberbullying on social networks based on bullying features,” in ICDCN, ACM 978-1-4503-4032-8, pp. 1–6, 2016.
    [68]
    K. Dinakar, B. Jones, C. Havasi, H. Lieberman, and R. Picard, “Common sense reasoning for detection, prevention, and mitigation of cyberbullying,” ACM Trans. Interact. Intell. Syst., vol. 2, no. 3, pp. 1–30, Sep. 2012.
    [69]
    N. Pendar, “Toward spotting the pedophile telling victim from predator in text chats,” in Proc. Int. Conf. Semantic Computing, Irvine, USA, 2017, pp. 235–241.
    [70]
    J. M. Xu, K. S. Jun, X. J. Zhu, and A. Bellmore, “Learning from bullying traces in social media,” in Proc. Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Montrèal, Canada, 2012, pp. 656–666.
    [71]
    E. Cambria, D. Das, S. Bandyopadhyay, and A. Feraco, A Practical Guide to Sentiment Analysis, Cham, Germany: Springer, 2017.
    [72]
    S. Murnion, W. J. Buchanan, A. Smales, and G. Russell, “Machine learning and semantic analysis of in-game chat for cyberbullying,” Comput. Secur., vol. 76, pp. 197–213, Jul. 2018. doi: 10.1016/j.cose.2018.02.016
    [73]
    M. A. Al-Ajlan and M. Ykhlef, “Optimized twitter cyberbullying detection based on deep learning,” in Proc. 21st Saudi Computer Society Nat. Computer Conf., Riyadh, Saudi Arabia, 2018, pp. 1–5.
    [74]
    J. L. Bigelow, A. Edwards, and L. Edwards, “Detecting cyberbullying using latent semantic indexing,” in Proc. 1st Int. Workshop on Computational Methods for CyberSafety, Indianapolis, USA, pp. 2016, 11–14.
    [75]
    K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the detection of textual cyberbullying,” in Proc. 14th Int. AAAI Conf. Web and Social Media, Barcelona, Spain, 2011, pp. 11–17.
    [76]
    C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang., “Abusive language detection in online user content,” in Proc. 25th Int. Conf. World Wide Web, Montreal, Canada, 2016, pp. 145–153.
    [77]
    D. W. Yin, Z. Z. Xue, L. J. Hong, B. D. Davison, A. Kontostathis, A. Edwards, and L. Edwards, “Detection of harassment on web 2.0,” Madrid, Spain, 2009.
    [78]
    Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in Proc. 31st Int. Conf. Machine Learning, Beijing, China, 2014, pp. 1188–1196.
    [79]
    N. Aulia and I. Budi, “Hate speech detection on indonesian long text documents using machine learning approach,” in Proc. 5th Int. Conf. Computing and Artificial Intelligence, Bali, Indonesia, 2019, pp. 164–169.
    [80]
    Burnap and M. L. Williams, “Cyber hate speech on Twitter: An application of machine classification and statistical modeling for policy and decision making,” Policy Internet, vol. 7, no. 2, pp. 223–242, Jun. 2015. doi: 10.1002/poi3.85
    [81]
    M. Corazza, S. Menini, E. Cabrio, S. Tonelli, and S. Villata, “A multilingual evaluation for online hate speech detection,” ACM Trans. Internet Technol., vol. 20, no. 2, pp. 1–22, May 2020.
    [82]
    N. Vishwamitra, H. X. Hu, F. Luo, and L. Cheng, “Towards understanding and detecting cyberbullying in real-world images,” in Proc. 28th Annu. Network and Distributed System Security Symp., 2021.
    [83]
    F. F. Patacsil, “Analysis of cyberbullying incidence among Filipina victims: A pattern recognition using association rule extraction,” Int. J. Intell. Syst. Appl., vol. 11, no. 11, pp. 48–57, Nov. 2019.
    [84]
    N. Potha and M. Maragoudakis, “Cyberbullying detection using time series modeling,” in Proc. IEEE Int. Conf. Data Mining Workshop, Shenzhen, China, 2014, pp. 373–382.
    [85]
    Z. Q. Zhang, D. Robinson, and J. Tepper, “Detecting hate speech on twitter using a convolution-GRU based deep neural network,” in Proc. 15th Int. Conf. the Semantic Web, Heraklion, Greece, 2018, pp. 745–760.
    [86]
    S. O. Sood, E. F. Churchill, and J. Antin, “Automatic identification of personal insults on social news sites,” J. Am. Soc. Inf. Sci. Technol., vol. 63, no. 2, pp. 270–285, Feb. 2012. doi: 10.1002/asi.21690
    [87]
    K. Wang, Y. Cui, J. W. Hu, Y. Zhang, W. Zhao, and L. M. Feng, “Cyberbullying detection, based on the fasttext and word similarity schemes,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 20, no. 1, pp. 1–15, Jan. 2021.
    [88]
    M. Dadvar and F. de Jong, “Cyberbullying detection: A step toward a safer internet yard,” in Proc. 21st Int. Conf. World Wide Web, Lyon, France, 2012.
    [89]
    K. Burn-Thornton and T. Burman, “The use of data mining to indicate virtual (email) bullying,” in Proc. 3rd Global Congr. Intelligent Systems, Wuhan, China, 2012, pp. 253–256.
    [90]
    S. O. Sood, J. Antin, and E. Churchill, “Using crowdsourcing to improve profanity detection,” in Proc. AAAI Spring Symp. Series, Palo Alto, USA, 2012.
    [91]
    U. Bretschneider, T. Wöhner, and R. Peters, “Detecting online harassment in social networks,” in Proc. 35th Int. Conf. Information Systems, Auckland, New Zealand, 2014, pp. 1–14.
    [92]
    E. Rudkowsky, M. Haselmayer, M. Wastian, M. Jenny, Š Emrich, and M. Sedlmair, “More than bags of words: Sentiment analysis with word embeddings,” Commun. Methods Meas., vol. 12, no. 2–3, pp. 140–157, Mar. 2018. doi: 10.1080/19312458.2018.1455817
    [93]
    M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong, “Improving cyberbullying detection with user context,” in Proc. 35th European Conf. Information Retrieval, Moscow, Russia, 2013, pp. 693–696.
    [94]
    M. Fortunatus, Anthony, and S. Charters, “Combining textual features to detect cyberbullying in social media posts,” Procedia Comput. Sci., vol. 176, pp. 612–621, Jan. 2020. doi: 10.1016/j.procs.2020.08.063
    [95]
    M. Dadvar, D. Trieschnigg, and F. de Jong, “Experts and machines against bullies: A hybrid approach to detect cyberbullies,” in Proc. Canadian Conf. Artificial Intelligence, Montrèal, Canada, 2014, pp. 275–281.
    [96]
    H. Watanabe, M. Bouazizi, and T. Ohtsuki, “Hate speech on twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection,” IEEE Access, vol. 6, pp. 13825–13835, Feb. 2018. doi: 10.1109/ACCESS.2018.2806394
    [97]
    L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte, “Similarity measures in scientometric research: The jaccard index versus Salton’s cosine formula,” Inf. Process. Manag., vol. 25, no. 3, pp. 315–318, May 1989. doi: 10.1016/0306-4573(89)90048-4
    [98]
    P. Blandfort, D. U. Patton, W. R. Frey, S. Karaman, S. Bhargava, F. T. Lee, S. Varia, C. Kedzie, M. B. Gaskell, R. Schifanella, K. McKeown, and S. F. Chang, “Multimodal social media analysis for gang violence prevention,” in Proc. 13th Int. AAAI Conf. Web and Social Media, Munich, Germany, 2019, pp. 114–124.
    [99]
    H. Hosseinmardi, R. I. Rafiq, R. Han, Q. Lv, and S. Mishra, “Prediction of cyberbullying incidents in a media-based social network,” in Proc. IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, San Francisco, USA, 2016, pp. 186–192.
    [100]
    V. K. Singh, S. Ghosh, and C. Jose, “Toward multimodal cyberbullying detection,” in Proc. CHI Conf. Extended Abstracts on Human Factors in Computing Systems, Denver, USA, 2019, pp. 2090–2099.
    [101]
    H. T. Zhong, H. Li, A. C. Squicciarini, S. M. Rajtmajer, C. Griffin, D. J. Miller, and C. Caragea, “Content-driven detection of cyberbullying on the instagram social network,” in Proc. 25th Int. Joint Conf. Artificial Intelligence, New York, USA, 2016, pp. 3952–3958.
    [102]
    R. Gomez, J. Gibert, L. Gomez, and D. Karatzas, “Exploring hate speech detection in multimodal publications,” in Proc. IEEE Winter Conf. Applications of Computer Vision, Snowmass, USA, 2020, 1459–1467.
    [103]
    K. Xu, J. L. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in Proc. 32nd Int. Conf. Machine Learning, Lille, France, 2015, pp. 2048–2057.
    [104]
    M. Khan, M. A. Tahir, and Z. Ahmed, “Detection of violent content in cartoon videos using multimedia content detection techniques,” in Proc. IEEE 21st Int. Multi-Topic Conf., Karachi, Pakistan, 2018, pp. 1–5.
    [105]
    S. Zerr, S. Siersdorfer, J. Hare, and E. Demidova., “Privacy-aware image classification and search,” in Proc. 35th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Portland, USA, 2012, pp. 35–44.
    [106]
    Y. Miyakoshi and S. Kato, “Facial emotion detection considering partial occlusion of face using Bayesian network,” in Proc. IEEE Symp. Computers & Informatics, Kuala Lumpur, Malaysia, 2011, pp. 96–101.
    [107]
    A. Das, J. S. Wahi, and S. Y. Li, “Detecting hate speech in multi-modal memes,” arXiv preprint arXiv: 2012.14891, 2020.
    [108]
    J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proc. Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota, 2018, pp. 4171–4186.
    [109]
    F. Perronnin and C. Dance, “Fisher kernels on visual vocabularies for image categorization,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Minneapolis, USA, 2017, pp. 1–8.
    [110]
    D. K. Zhang, J. Yin, X. Q. Zhu, and C. Q. Zhang, “Network representation learning: A survey,” IEEE Trans. Big Data, vol. 6, no. 1, pp. 3–28, Mar. 2020. doi: 10.1109/TBDATA.2018.2850013
    [111]
    N. Tahmasbi and A. Fuchsberger, “Challenges and future directions of automated cyberbullying detection,” in Proc. 24th Americas Conf. Information Systems, New Orleans, USA, 2018.
    [112]
    Fortuna and S. Nunes, “A survey on automatic detection of hate speech in text,” ACM Comput. Surv., vol. 51, no. 4, pp. 1–30, Jul. 2018.
    [113]
    A. Q. Wang and K. Potika, “Cyberbullying classification based on social network analysis,” in Proc. IEEE 7th Int. Conf. Big Data Computing Service and Applications, Oxford, United Kingdom, 2021, pp. 87–95.
    [114]
    V. K. Singh, Q. J. Huang, and P. K. Atrey, “Cyberbullying detection using probabilistic socio-textual information fusion,” in Proc. IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, San Francisco, USA, 2016, pp. 884–887.
    [115]
    T. G. Dietterich, “Ensemble methods in machine learning,” in Proc. 1st Int. Workshop on Multiple Classifier Systems, Cagliari, Italy, 2000, pp. 1–15.
    [116]
    M. F. López-Vizcaíno, F. J. Nóvoa, V. Carneiro, and F. Cacheda, “Early detection of cyberbullying on social media networks,” Future Gener. Comput. Syst., vol. 118, pp. 219–229, May 2021. doi: 10.1016/j.future.2021.01.006
    [117]
    F. Cacheda, D. Fernandez, F. J. Novoa, and V. Carneiro, “Early detection of depression: Social network analysis and random forest techniques,” J. Med. Internet Res., vol. 21, no. 6, p. e12554, Jun. 2019.
    [118]
    Z. H. Zhou, “A brief introduction to weakly supervised learning,” Natl. Sci. Rev., vol. 5, no. 1, pp. 44–53, Jan. 2018. doi: 10.1093/nsr/nwx106
    [119]
    E. Raisi and B. Huang, “Cyberbullying detection with weakly supervised machine learning,” in Proc. IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Sydney, Australia, 2017, pp. 409–416.
    [120]
    E. Raisi and B. Huang., “Co-trained ensemble models for weakly supervised cyberbullying detection,” in Proc. 31st Conf. Neural Information Processing Systems, Long Beach, USA, 2017.
    [121]
    T. Kohonen, “Self-organizing maps: Ophmization approaches,” in Artificial Neural Networks, T. Kohonen, K. Mäkisara, O. Simula, and J. KANGAS, Eds. Amsterdam: Elsevier, 1991, pp. 981–990.
    [122]
    M. Di Capua, E. Di Nardo, and A. Petrosino, “Unsupervised cyber bullying detection in social networks,” in Proc. 23rd Int. Conf. Pattern Recognition, Cancun, Mexico, 2016, pp. 432–437.
    [123]
    A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data,” IEEE Trans. Neural Netw., vol. 13, no. 6, pp. 1331–1341, Nov. 2002. doi: 10.1109/TNN.2002.804221
    [124]
    S. Das, A. Kim, and S. Karmakar, “Change-point analysis of cyberbullying-related twitter discussions during COVID-19,” in Proc. 16th Annu. Social Informatics Research Symp., 2020.
    [125]
    H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE Trans. Acoust. Speech Signal Process., vol. 26, no. 1, pp. 43–49, Feb. 1978. doi: 10.1109/TASSP.1978.1163055
    [126]
    E. J. Keogh and M. J. Pazzani, “Derivative dynamic time warping,” in Proc. SIAM Int. Conf. Data Mining, Chicago, USA, 2001.
    [127]
    L. Cheng, R. C. Guo, Y. N. Silva, D. Hall, and H. Liu, “Modeling temporal patterns of cyberbullying detection with hierarchical attention networks,” ACM/IMS Trans. Data Sci., vol. 2, no. 2, pp. 1–23, May 2021.
    [128]
    L. Cheng, R. C. Guo, Y. Silva, D. Hall, and H. Liu, “Hierarchical attention networks for cyberbullying detection on the instagram social network,” in Proc. SIAM Int. Conf. Data Mining, Calgary, Canada, 2019, pp. 235–243.
    [129]
    D. Soni and V. Singh, “Time reveals all wounds: Modeling temporal characteristics of cyberbullying,” in Proc. 12th Int. AAAI Conf. Web and Social Media, Stanford, USA, 2018.
    [130]
    S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010. doi: 10.1109/TKDE.2009.191
    [131]
    M. A. Rizoiu, T. Y. Wang, G. Ferraro, and H. Suominen, “Transfer learning for hate speech detection in social media,” arXiv preprint arXiv: 1906.03829, 2019.
    [132]
    S. Agrawal and A. Awekar, “Deep learning for detecting cyberbullying across multiple social media platforms,” in Proc. 40th European Conf. Information Retrieval, Grenoble, France, 2018, pp. 141–153.
    [133]
    J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in Proc. Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1532–1543.
    [134]
    R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural Language processing (almost) from scratch,” J. Mach. Learn. Res., vol. 12, pp. 2493–2537, 2011.
    [135]
    D. Y. Tang, F. R. Wei, N. Yang, M. Zhou, T. Liu, and B. Qin, “Learning sentiment-specific word embedding for twitter sentiment classification,” in Proc. 52nd Annu. Meeting of the Association for Computational Linguistics, Baltimore, Maryland, 2014, pp. 1555–1565.
    [136]
    T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv: 1611.07308, 2016.
    [137]
    A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, USA, 2016, pp. 855–864.
    [138]
    K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409.1556, 2014.
    [139]
    K. Kumari and J. P. Singh, “Identification of cyberbullying on multi-modal social media posts using genetic algorithm,” Trans. Emerging Telecommun. Technol., vol. 32, no. 2, p. e3907, Feb. 2021.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(12)

    Article Metrics

    Article views (75) PDF downloads(16) Cited by()

    Highlights

    • A comprehensive review of computational approaches for cyberbullying and cyberviolence detection
    • A User-Activities-Content (UAC) triangle view of the key factors in cyberbullying
    • Important features and their interactions in cyberbullying detection
    • Machine learning methods for cyberbullying detection

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return