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Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech

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Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 721))

Abstract

Deep learning is a feasible technology and is the best replacement for traditional means to prevent fake news, cyberbullying and hate speech. Traditional methods to prevent fake news, cyberbullying, and hate speech include using real life personnel to go through messages and remove them. The research analyses other researchers’ discoveries relative to deep learning. It is important to conduct this research so that we are all aware of how close we are to be able to protect our future generations of children and adults from having their mental and physical health affected. This research aims to analyse the current deep learning techniques used to prevent fake news, cyberbullying and hate speech. A comparison is included in this research to identify the state-of-the-art technique.

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References

  1. Simpson, J.: How machine learning and social media are expanding access to mental health. Geo. L. Tech. Rev. 2, 137 (2017)

    Google Scholar 

  2. Hammar, K., Jaradat, S., Dokoohaki, N., Matskin, M.: Deep text classification of Instagram data using word embeddings and weak supervision. In: Web Intelligence. IOS Press, vol. 18, no. 1, pp. 53–67 (2020)

    Google Scholar 

  3. Islam, M.R., Liu, S., Wang, X., Xu, G.: Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc. Netw. Anal. Min. 10(1), 1–20 (2020). https://doi.org/10.1007/s13278-020-00696-x

    Article  Google Scholar 

  4. Ibrahim, Y.: The social psychology of hate online: from cyberbullying to gaming. In: Technologies of Trauma, pp. 93–113. Emerald Publishing Limited (2022)

    Google Scholar 

  5. Kaliyar, R.K., Goswami, A., Narang, P.: “Fakebert: fake news detection in social media with a BERT-based deep learning approach. Multimedia Tools Appl. 80(8), 11:765–11:788 (2021)

    Google Scholar 

  6. Guthold, R., et al.: The importance of mental health measurement to improve global adolescent health. J. Adolesc. Health 72(1), S3–S6 (2023)

    Article  Google Scholar 

  7. Agarwal, R., Gupta, S., Chatterjee, N.: Profiling fake news spreaders on twitter: a clickbait and linguistic feature based scheme. In: Rosso, P., Basile, V., Martínez, R., Métais, E., Meziane, F. (eds.) NLDB 2022. LNCS, vol. 13286, pp. 345–357. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08473-7_32

  8. Kalkenings, M., Mandl, T.: University of Hildesheim at SemEval-2022 task 5: combining deep text and image models for multimedia misogyny detection. In: Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pp. 718–723. Association for Computational Linguistics, Seattle July 2022. https://aclanthology.org/2022.semeval-1.98

  9. Del Vicario, M., et al.: The spreading of misinformation online. Proc. Natl. Acad. Sci. 113(3), 554–559 (2016)

    Article  Google Scholar 

  10. Kumar, S., Shah, N.: False information on web and social media: a survey. CoRR, abs/1804.08559 (2018). http://arxiv.org/abs/1804.08559

  11. Gorrell, G., et al.: SemEval-2019 task 7: RumourEval, determining rumour veracity and support for rumours. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 845–854. Association for Computational Linguistics, Minneapolis June 2019. https://aclanthology.org/S19-2147

  12. Vosoughi, S., Mohsenvand, M.N., Roy, D.: Rumor gauge: predicting the veracity of rumors on twitter. ACM Trans. Knowl. Discov. Data (TKDD) 11(4), 1–36 (2017)

    Article  Google Scholar 

  13. Zhou, X., Zafarani, R.: Fake news: a survey of research, detection methods, and opportunities. CoRR, abs/1812.00315 (2018). http://arxiv.org/abs/1812.00315

  14. Ghosh, S., Shah, C.: Towards automatic fake news classification. Proc. Assoc. Inf. Sci. Technol. 55(1), 805–807 (2018)

    Article  Google Scholar 

  15. Ruchansky, N., Seo, S., Liu, Y.: CSI: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, ser. CIKM ’17, pp. 797–806. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3132847.3132877

  16. Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: FakenewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3), 171–188 (2020)

    Article  Google Scholar 

  17. Dadvar, M., Eckert, K.: Cyberbullying detection in social networks using deep learning based models. In: Song, M., Song, I.-Y., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2020. LNCS, vol. 12393, pp. 245–255. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59065-9_20

    Chapter  Google Scholar 

  18. Tokunaga, R.S.: Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput. Hum. Behav. 26(3), 277–287 (2010). https://www.sciencedirect.com/science/article/pii/S074756320900185X

  19. Van Hee, C., et al.: Automatic detection of cyberbullying in social media text. PLoS ONE 13(10), 1–22 (2018). https://doi.org/10.1371/journal.pone.0203794

    Article  Google Scholar 

  20. Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. CoRR, abs/1801.06482 (2018). http://arxiv.org/abs/1801.06482

  21. Zhou, Y., Yang, Y., Liu, H., Liu, X., Savage, N.: Deep learning based fusion approach for hate speech detection. IEEE Access 8, 128:923–128:929 (2020)

    Google Scholar 

  22. De Gibert, O., Perez, N., García-Pablos, A., Cuadros, M.: Hate speech dataset from a white supremacy forum. arXiv preprint arXiv:1809.04444 (2018)

  23. Davidson, T., Bhattacharya, D., Weber, I.: Racial bias in hate speech and abusive language detection datasets. arXiv preprint arXiv:1905.12516 (2019)

  24. Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A.: Affective computing and sentiment analysis. In: Cambria, E., Das, D., Bandyopadhyay, S., Feraco, A. (eds.) A Practical Guide to Sentiment Analysis. Socio-Affective Computing, vol. 5, pp. 1–10. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55394-8_1

  25. Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)

    Google Scholar 

  26. Bojkovsky, M., Pikuliak, M.: Stufiit at semeval-2019 task 5: multilingual hate speech detection on twitter with muse and elmo embeddings. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 464–468 (2019)

    Google Scholar 

  27. Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proceedings of the NAACL Student Research Workshop, pp. 88–93 (2016)

    Google Scholar 

  28. Burnap, P., Williams, M.L.: Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2), 223–242 (2015)

    Article  Google Scholar 

  29. Sarzynska-Wawer, J., et al.: Detecting formal thought disorder by deep contextualized word representations. Psychiatry Res. 304, 114135 (2021)

    Article  Google Scholar 

  30. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  31. Mozafari, M., Farahbakhsh, R., Crespi, N.: A BERT-based transfer learning approach for hate speech detection in online social media. In: Cherifi, H., Gaito, S., Mendes, J.F., Moro, E., Rocha, L.M. (eds.) COMPLEX NETWORKS 2019. SCI, vol. 881, pp. 928–940. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36687-2_77

    Chapter  Google Scholar 

  32. Yin, X., Huang, Y., Zhou, B., Li, A., Lan, L., Jia, Y.: Deep entity linking via eliminating semantic ambiguity with BERT. IEEE Access 7, 169:434–169:445 (2019)

    Google Scholar 

  33. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018)

  34. Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820 (2015)

  35. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  36. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  37. Liang, B., Li, H., Su, M., Bian, P., Li, X., Shi, W.: Deep text classification can be fooled. arXiv preprint arXiv:1704.08006 (2017)

  38. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2014). https://arxiv.org/abs/1412.6572

  39. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)

  40. Salehi, B., Cook, P., Baldwin, T.: A word embedding approach to predicting the compositionality of multiword expressions. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 977–983 (2015)

    Google Scholar 

  41. Yang, Y.-T., Feng, L., Dai, L.-C.: A BERT-based interactive attention network for aspect sentiment analysis. J. Comput. 32(3), 30–42 (2021)

    Google Scholar 

  42. Srivastava, A., Makhija, P., Gupta, A.: Noisy text data: Achilles’ heel of BERT. In: Proceedings of the Sixth Workshop on Noisy User-Generated Text (W-NUT 2020), pp. 16–21 (2020)

    Google Scholar 

  43. Cheng, Y., Yao, L., Xiang, G., Zhang, G., Tang, T., Zhong, L.: Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism. IEEE Access 8, 134:964–134: 975 (2020)

    Google Scholar 

  44. Zhong, Z., Jin, L., Huang, S.: Deeptext: a new approach for text proposal generation and text detection in natural images. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1208–1212 (2017)

    Google Scholar 

  45. Zhong, Z., Jin, L., Huang, S.: Deeptext: a new approach for text proposal generation and text detection in natural images. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1208–1212. IEEE (2017)

    Google Scholar 

  46. Karasoy, O., Ballı, S.: Spam SMS detection for Turkish language with deep text analysis and deep learning methods. Arab. J. Sci. Eng. 47(8), 9361–9377 (2022)

    Article  Google Scholar 

  47. Khan, R.H., Shihavuddin, A., Syeed, M.M., Haque, R.U., Uddin, M.F.: Improved fake news detection method based on deep learning and comparative analysis with other machine learning approaches. In: 2022 International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–6. IEEE (2022)

    Google Scholar 

  48. Devika, S., Pooja, M., Arpitha, M., Ravi, V.: BERT transformer-based fake news detection in Twitter social media. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds.) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems, pp. 95–102. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-6004-8_8

  49. Jang, B., Kim, M., Harerimana, G., Kang, S.-U., Kim, J.W.: Bi-LSTM model to increase accuracy in text classification: combining word2vec CNN and attention mechanism. Appl. Sci. 10(17), 5841 (2020)

    Article  Google Scholar 

  50. Di Gennaro, G., Buonanno, A., Palmieri, F.A.: Considerations about learning word2vec. J. Supercomput. 77(11), 12:320–12:335 (2021)

    Google Scholar 

  51. Lilleberg, J., Y., Zhu, Y., Zhang, Y.: Support vector machines and word2vec for text classification with semantic features. In: 2015 IEEE 14th International Conference on Cognitive Informatics and Cognitive Computing (ICCI* CC), pp. 136–140. IEEE (2015)

    Google Scholar 

  52. Church, K.W.: Word2vec. Nat. Lang. Eng. 23(1), 155–162 (2017)

    Article  Google Scholar 

  53. Rong, X.: Word2vec parameter learning explained. arXiv preprint arXiv:1411.2738 (2014)

  54. Huang, G.K.W., Lee, J.C.: Hyperpartisan news and articles detection using BERT and ELMO. In: 2019 International Conference on Computer and Drone Applications (IConDA) , pp. 29–32. IEEE (2019)

    Google Scholar 

  55. Li, B., Zhou, H., He, J., Wang, M., Yang, Y., Li, L.: On the sentence embeddings from pre-trained language models. arXiv preprint arXiv:2011.05864 (2020)

  56. Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMO on ten benchmarking datasets. arXiv preprint arXiv:1906.05474 (2019)

  57. Ethayarajh, K.: How contextual are contextualized word representations? Comparing the geometry of BERT, ELMO, and gpt-2 embeddings. arXiv preprint arXiv:1909.00512 (2019)

  58. Jwa, H., Oh, D., Park, K., Kang, J.M., Lim, H.: exbake: Automatic fake news detection model based on bidirectional encoder representations from transformers (BERT). Appl. Sci. 9(19), 4062 (2019)

    Article  Google Scholar 

  59. Huang, Q., Inkpen, D., Zhang, J., Van Bruwaene, D.: Cyberbullying intervention based on convolutional neural networks. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 42–51 (2018)

    Google Scholar 

  60. Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on twitter using a convolution-GRU based deep neural network. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 745–760. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_48

    Chapter  Google Scholar 

  61. Ajao, O., Bhowmik, D., Zargari, S.: Fake news identification on twitter with hybrid CNN and RNN models. In: Proceedings of the 9th International Conference on Social Media and Society, pp. 226–230 (2018)

    Google Scholar 

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Al-Dala’in, T., Zhao, J.H.S. (2023). Overview of the Benefits Deep Learning Can Provide Against Fake News, Cyberbullying and Hate Speech. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-031-35308-6_2

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