Skip to main content
Erschienen in: Multimedia Systems 6/2022

13.07.2020 | Special Issue Paper

Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data

verfasst von: Akshi Kumar, Nitin Sachdeva

Erschienen in: Multimedia Systems | Ausgabe 6/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Automatic detection of cyberbullying in social media content is a natural language understanding and generic text classification task. The cultural diversities, country-specific trending topics hash-tags on social media, the unconventional use of typographical resources such as capitals, punctuation, emojis and easy availability of native language keyboards add to the variety and volume of user-generated content compounding the linguistic challenges. This research focuses on cyberbullying detection in the code-mix data, specifically the Hinglish, which refers to the juxtaposition of words from the Hindi and English languages. We explore the problem of cyberbullying prediction and propose MIIL-DNN, a multi-input integrative learning model based on deep neural networks. MIIL-DNN combines information from three sub-networks to detect and classify bully content in real-time code-mix data. It takes three inputs, namely English language features, Hindi language features (transliterated Hindi converted to the Hindi language) and typographic features, which are learned separately using sub-networks (capsule network for English, bi-LSTM for Hindi and MLP for typographic). These are then combined into one unified representation to be used as the input for a final regression output with linear activation. The advantage of using this model-level multi-lingual fusion is that it operates with the unique distribution of each input type without increasing the dimensionality of the input space. The robustness of the technique is validated on two datasets created by scraping data from the popular social networking sites, namely Twitter and Facebook. Experimental evaluation reveals that MIIL-DNN achieves superlative performance in terms of AUC-ROC curve on both the datasets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Kumar, A., Jaiswal, A.: Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurr. Comput. Pract. Exp. 32(1), e5107 (2020)MathSciNetCrossRef Kumar, A., Jaiswal, A.: Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurr. Comput. Pract. Exp. 32(1), e5107 (2020)MathSciNetCrossRef
2.
Zurück zum Zitat Kumar, A., Sharma, A.: Systematic literature review on opinion mining of big data for government intelligence. Webology 14(2), 6–47 (2017) Kumar, A., Sharma, A.: Systematic literature review on opinion mining of big data for government intelligence. Webology 14(2), 6–47 (2017)
4.
Zurück zum Zitat Campbell, M.A.: Cyber bullying: an old problem in a new guise? J. Psychol. Couns. Sch. 15(1), 68–76 (2005) Campbell, M.A.: Cyber bullying: an old problem in a new guise? J. Psychol. Couns. Sch. 15(1), 68–76 (2005)
5.
Zurück zum Zitat Child Rights and You (CRY): Online Safety and Internet Addiction (A Study Conducted Amongst Adolescents in Delhi-NCR). Child Rights and You, New Delhi (2020) Child Rights and You (CRY): Online Safety and Internet Addiction (A Study Conducted Amongst Adolescents in Delhi-NCR). Child Rights and You, New Delhi (2020)
6.
Zurück zum Zitat Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78(17), 23973–24010 (2019)CrossRef Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78(17), 23973–24010 (2019)CrossRef
7.
Zurück zum Zitat Patra, B.G., Das, D., Das, A.: Sentiment analysis of code-mixed Indian languages: an overview of SAIL_Code-Mixed Shared Task@ ICON-2017. arXiv preprint. arXiv:1803.06745 (2018) Patra, B.G., Das, D., Das, A.: Sentiment analysis of code-mixed Indian languages: an overview of SAIL_Code-Mixed Shared Task@ ICON-2017. arXiv preprint. arXiv:1803.06745 (2018)
8.
Zurück zum Zitat Parshad, R.D., Bhowmick, S., Chand, V., Kumari, N., Sinha, N.: What is India speaking? Exploring the “Hinglish” invasion. Phys. A 449, 375–389 (2016)MathSciNetCrossRefMATH Parshad, R.D., Bhowmick, S., Chand, V., Kumari, N., Sinha, N.: What is India speaking? Exploring the “Hinglish” invasion. Phys. A 449, 375–389 (2016)MathSciNetCrossRefMATH
10.
Zurück zum Zitat Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P.C., Carvalho, J.P., Oliveira, S., Trancoso, I.: Automatic cyberbullying detection: a systematic review. Comput. Hum. Behav. 93, 333–345 (2019)CrossRef Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P.C., Carvalho, J.P., Oliveira, S., Trancoso, I.: Automatic cyberbullying detection: a systematic review. Comput. Hum. Behav. 93, 333–345 (2019)CrossRef
11.
Zurück zum Zitat Salawu, S., He, Y., Lumsden, J.: Approaches to automated detection of cyberbullying: a survey. IEEE Trans. Affect. Comput. 1, 1–20 (2017) Salawu, S., He, Y., Lumsden, J.: Approaches to automated detection of cyberbullying: a survey. IEEE Trans. Affect. Comput. 1, 1–20 (2017)
12.
Zurück zum Zitat Reynolds, K., Kontostathis. A., Edwards, L.: Using machine learning to detect cyberbullying. In: Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference, vol. 2, pp. 241–244. IEEE (2011) Reynolds, K., Kontostathis. A., Edwards, L.: Using machine learning to detect cyberbullying. In: Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference, vol. 2, pp. 241–244. IEEE (2011)
13.
Zurück zum Zitat Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: International AAAI Conference on Web and Social Media, North America, July 2011 (2016) Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: International AAAI Conference on Web and Social Media, North America, July 2011 (2016)
14.
Zurück zum Zitat Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European Conference on Information Retrieval, pp. 693–696. Springer, Berlin, Heidelberg (2013) Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European Conference on Information Retrieval, pp. 693–696. Springer, Berlin, Heidelberg (2013)
15.
Zurück zum Zitat Dadvar, M., Trieschnigg, D., de Jong, F.: Experts and machines against bullies: a hybrid approach to detect cyberbullies. In: Canadian Conference on Artificial Intelligence, pp. 275–281. Springer, Cham (2014) Dadvar, M., Trieschnigg, D., de Jong, F.: Experts and machines against bullies: a hybrid approach to detect cyberbullies. In: Canadian Conference on Artificial Intelligence, pp. 275–281. Springer, Cham (2014)
16.
Zurück zum Zitat Kontostathis, A., Reynolds, K., Garron, A., Edwards, L.: Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th Annual ACM web Science Conference, pp. 195–204 (2013) Kontostathis, A., Reynolds, K., Garron, A., Edwards, L.: Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th Annual ACM web Science Conference, pp. 195–204 (2013)
17.
Zurück zum Zitat Potha, N., Maragoudakis, M., Lyras, D.: A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl. Based Syst. 96, 134–155 (2016)CrossRef Potha, N., Maragoudakis, M., Lyras, D.: A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl. Based Syst. 96, 134–155 (2016)CrossRef
18.
Zurück zum Zitat Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media based social network. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 186–192 (2016) Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media based social network. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 186–192 (2016)
19.
Zurück zum Zitat Hammer, H.L.: Automatic detection of hateful comments in online discussion. In: International Conference on Industrial Networks and Intelligent Systems, pp 164–173. Springer, Cham (2016) Hammer, H.L.: Automatic detection of hateful comments in online discussion. In: International Conference on Industrial Networks and Intelligent Systems, pp 164–173. Springer, Cham (2016)
20.
Zurück zum Zitat Sarna, G., Bhatia, M.P.: Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int. J. Mach. Learn. Cybern. 8(2), 677–689 (2017)CrossRef Sarna, G., Bhatia, M.P.: Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int. J. Mach. Learn. Cybern. 8(2), 677–689 (2017)CrossRef
21.
Zurück zum Zitat Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu, H., Luo, F., Macbeth, J., Dillon, E.: Cyberbullying detection with a pronunciation based convolutional neural network. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 740–745 (2016) Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu, H., Luo, F., Macbeth, J., Dillon, E.: Cyberbullying detection with a pronunciation based convolutional neural network. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 740–745 (2016)
22.
Zurück zum Zitat Zhao, R., Mao, K.: Cyberbullying detection based on semantic-enhanced marginalized denoising autoencoder. IEEE Trans. Affect. Comput. 8(3), 328–339 (2017)CrossRef Zhao, R., Mao, K.: Cyberbullying detection based on semantic-enhanced marginalized denoising autoencoder. IEEE Trans. Affect. Comput. 8(3), 328–339 (2017)CrossRef
23.
Zurück zum Zitat Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, pp. 43–48 (2016) Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, pp. 43–48 (2016)
24.
Zurück zum Zitat Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 409–416. ACM (2017) Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 409–416. ACM (2017)
25.
Zurück zum Zitat Rakib, T.B., Soon, L.K.: Using the Reddit Corpus for cyberbully detection. In: Asian Conference on Intelligent Information and Database Systems, p. 180. Springer, Cham (2018) Rakib, T.B., Soon, L.K.: Using the Reddit Corpus for cyberbully detection. In: Asian Conference on Intelligent Information and Database Systems, p. 180. Springer, Cham (2018)
26.
Zurück zum Zitat Ptaszynski, M., Pieciukiewicz, A., Dybała, P.: Results of the PolEval 2019 shared task 6: first dataset and open shared task for automatic cyberbullying detection in Polish Twitter. In: Proceedings of the PolEval2019 Workshop, p. 89 (2019) Ptaszynski, M., Pieciukiewicz, A., Dybała, P.: Results of the PolEval 2019 shared task 6: first dataset and open shared task for automatic cyberbullying detection in Polish Twitter. In: Proceedings of the PolEval2019 Workshop, p. 89 (2019)
27.
Zurück zum Zitat Gordeev, D.: Automatic detection of verbal aggression for Russian and American image boards. Procedia Soc. Behav. Sci. 236, 71–75 (2016)CrossRef Gordeev, D.: Automatic detection of verbal aggression for Russian and American image boards. Procedia Soc. Behav. Sci. 236, 71–75 (2016)CrossRef
28.
Zurück zum Zitat Ibrohim, M.O., Budi, I.: Multi-label hate speech and abusive language detection in Indonesian Twitter. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 46–57 (2019) Ibrohim, M.O., Budi, I.: Multi-label hate speech and abusive language detection in Indonesian Twitter. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 46–57 (2019)
29.
Zurück zum Zitat Pratiwi, N.I., Budi, I., Jiwanggi, M.A.: Hate Speech Identification using the Hate Codes for Indonesian Tweets. In: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, pp. 128–133 (2019) Pratiwi, N.I., Budi, I., Jiwanggi, M.A.: Hate Speech Identification using the Hate Codes for Indonesian Tweets. In: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, pp. 128–133 (2019)
30.
Zurück zum Zitat Haidar, B., Chamoun, M., Serhrouchni, A.: Multilingual cyberbullying detection system: detecting cyberbullying in Arabic content. In: 2017 1st Cyber Security in Networking Conference (CSNet), pp. 1–8. IEEE (2017) Haidar, B., Chamoun, M., Serhrouchni, A.: Multilingual cyberbullying detection system: detecting cyberbullying in Arabic content. In: 2017 1st Cyber Security in Networking Conference (CSNet), pp. 1–8. IEEE (2017)
31.
Zurück zum Zitat Haidar, B., Chamoun, M., Serhrouchni, A.: A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst J. 2(6), 275–284 (2017)CrossRef Haidar, B., Chamoun, M., Serhrouchni, A.: A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst J. 2(6), 275–284 (2017)CrossRef
32.
Zurück zum Zitat Pawar, R., Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 040–044. IEEE (2019) Pawar, R., Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 040–044. IEEE (2019)
33.
Zurück zum Zitat Arreerard, R., Senivongse, T.: Thai defamatory text classification on social media. In: 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science and Engineering (BCD), pp. 73–78. IEEE (2018) Arreerard, R., Senivongse, T.: Thai defamatory text classification on social media. In: 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science and Engineering (BCD), pp. 73–78. IEEE (2018)
34.
Zurück zum Zitat Tarwani, S., Jethanandani, M., Kant, V.: Cyberbullying detection in Hindi–English code-mixed language using sentiment classification. In: International Conference on Advances in Computing and Data Sciences, pp. 543–551. Springer, Singapore (2019) Tarwani, S., Jethanandani, M., Kant, V.: Cyberbullying detection in Hindi–English code-mixed language using sentiment classification. In: International Conference on Advances in Computing and Data Sciences, pp. 543–551. Springer, Singapore (2019)
35.
Zurück zum Zitat Bohra, A., Vijay, D., Singh, V., Akhtar, S.S., Shrivastava, M.: A dataset of Hindi–English code-mixed social media text for hate speech detection. In: Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pp. 36–41 (2018) Bohra, A., Vijay, D., Singh, V., Akhtar, S.S., Shrivastava, M.: A dataset of Hindi–English code-mixed social media text for hate speech detection. In: Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pp. 36–41 (2018)
36.
Zurück zum Zitat Singh, V., Varshney, A., Akhtar, S. S., Vijay, D., Shrivastava, M.: Aggression detection on social media text using deep neural networks. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) ,pp. 43–50 (2018) Singh, V., Varshney, A., Akhtar, S. S., Vijay, D., Shrivastava, M.: Aggression detection on social media text using deep neural networks. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) ,pp. 43–50 (2018)
37.
Zurück zum Zitat Santosh, T.Y.S.S., Aravind, K.V.S.: Hate speech detection in Hindi–English code-mixed social media text. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 310–313 (2019) Santosh, T.Y.S.S., Aravind, K.V.S.: Hate speech detection in Hindi–English code-mixed social media text. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 310–313 (2019)
38.
Zurück zum Zitat Gupta, V.K.: “Hinglish” language-modeling a messy code-mixed language. arXiv preprint. arXiv:1912.13109 (2019) Gupta, V.K.: “Hinglish” language-modeling a messy code-mixed language. arXiv preprint. arXiv:1912.13109 (2019)
39.
Zurück zum Zitat Haidar, B., Chamoun, M., Yamout, F.: Cyberbullying detection: a survey on multilingual techniques. In: 2016 European Modelling Symposium (EMS), pp. 165–171. IEEE (2016) Haidar, B., Chamoun, M., Yamout, F.: Cyberbullying detection: a survey on multilingual techniques. In: 2016 European Modelling Symposium (EMS), pp. 165–171. IEEE (2016)
40.
Zurück zum Zitat Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. In: 6th International Conference on Computer Science and Information Technology (2019) Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. In: 6th International Conference on Computer Science and Information Technology (2019)
41.
Zurück zum Zitat Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)CrossRef Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)CrossRef
42.
Zurück zum Zitat Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. arXiv preprint. arXiv:1908.10063 (2019) Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. arXiv preprint. arXiv:1908.10063 (2019)
43.
Zurück zum Zitat Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017) Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
44.
Zurück zum Zitat Kumar, A., Srinivasan, K., Cheng, W.H., Zomaya, A.Y.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)CrossRef Kumar, A., Srinivasan, K., Cheng, W.H., Zomaya, A.Y.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)CrossRef
45.
Zurück zum Zitat Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, vol. 1, pp. 63–70. Association for Computational Linguistics (2002) Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, vol. 1, pp. 63–70. Association for Computational Linguistics (2002)
46.
Zurück zum Zitat Knight, K., Graehl, J.: Machine transliteration. Comput. Linguist. 24(4), 599–612 (1998) Knight, K., Graehl, J.: Machine transliteration. Comput. Linguist. 24(4), 599–612 (1998)
47.
Zurück zum Zitat Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on Twitter. Multimed. Tools Appl. 78(20), 29529–29553 (2019)CrossRef Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on Twitter. Multimed. Tools Appl. 78(20), 29529–29553 (2019)CrossRef
48.
Zurück zum Zitat Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014) Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
49.
Zurück zum Zitat Zhao, W., Ye, J., Yang, M., Lei, Z., Zhang, S., Zhao, Z.: Investigating capsule networks with dynamic routing for text classification. arXiv preprint. arXiv:1804.00538 (2018) Zhao, W., Ye, J., Yang, M., Lei, Z., Zhang, S., Zhao, Z.: Investigating capsule networks with dynamic routing for text classification. arXiv preprint. arXiv:1804.00538 (2018)
50.
Zurück zum Zitat Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273–278. IEEE (2013) Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273–278. IEEE (2013)
51.
Zurück zum Zitat Srivastava, S., Khurana, P., Tewari, V.: Identifying aggression and toxicity in comments using capsule network. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 98–105 (2018) Srivastava, S., Khurana, P., Tewari, V.: Identifying aggression and toxicity in comments using capsule network. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 98–105 (2018)
Metadaten
Titel
Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data
verfasst von
Akshi Kumar
Nitin Sachdeva
Publikationsdatum
13.07.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 6/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-020-00672-7

Weitere Artikel der Ausgabe 6/2022

Multimedia Systems 6/2022 Zur Ausgabe