An In-Depth Review of Anomaly Detection Techniques in Social Networks Using Machine Learning Techniques
Keywords:
Social Network Sites (SNS), NLP, RNNs, Long Short-Term Memory (LSTM), GPT, BERT, CNNsAbstract
The proliferation of social media platforms has facilitated the emergence of harmful online phenomena, including hate speech, cyberbullying, and automated bot activity, thereby undermining the safety of digital ecosystems. In response, researchers are increasingly leveraging deep learning and machine learning methodologies to develop automated detection and mitigation systems. This review synthesizes recent advancements across several key domains: sentiment analysis, cyberbullying prevention, hate speech identification, and social bot detection, with a particular focus on the evolution of ML/DL architectures such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and graph convolutional networks (GCNs). Furthermore, it critically examines persistent challenges in cyberbullying intervention systems, underscoring the necessity of integrating psychological and socio-cultural insights. The discussion extends to potential strategies for enhancing personal agency through improved support mechanisms and digital literacy education. Overall, the analysis substantiates the superior efficacy of deep learning approaches compared to traditional machine learning techniques, with the ultimate objective of informing the creation of scalable solutions to counteract detrimental online behaviors.
Downloads
References
Hande, R. Priyadharshini, B.R. Chakravarthi, Kan CMD: Kannada Code Mixed dataset for sentiment analysis and offensive language detection, in: Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media. 2020.
J. Cao, et al., A risky large group emergency decision-making method based on topic sentiment analysis, Expert Systems with Applications 195 (2022), 116527.
P.K. Roy, et al., A framework for hate speech detection using deep convolutional neural network, IEEE Access 8 (2020) 204951–204962.
H. Liu, et al., A fuzzy approach to text classification with two-stage training for ambiguous instances, IEEE Transactions on Computational Social Systems 6 (2) (2019) 227–240.
F.M. Plaza-Del-Arco, et al., A multi-task learning approach to hate speech detection leveraging sentiment analysis, IEEE Access 9 (2021) 112478–112489.
S. Modha, et al., Detecting and visualizing hate speech in social media: A cyber watchdog for surveillance, Expert Systems with Applications 161 (2020), 113725.
S. Kaur, S. Singh, S. Kaushal, Abusive content detection in online user-generated data: a survey, Procedia Computer Science 189 (2021) 274–281.
Alothali E, Hayawi K, Alashwal H (2020) Characteristics of similar-context trending hashtags in Twitter: a case study. In: International Conference on Web Services. 2020. Springer
M. Luo, X. Mu, Entity sentiment analysis in the news: A case study based on negative sentiment smoothing model (nssm), International Journal of Information Management Data Insights 2 (1) (2022), 100060.
A. Rodriguez, Y.-L. Chen, C. Argueta, FADOHS: framework for detection and integration of unstructured data of hate speech on facebook using sentiment and emotion analysis, IEEE Access 10 (2022) 22400–22419.
S. Khan, et al., HCovBi-caps: hate speech detection using convolutional and Bidirectional gated recurrent unit with Capsule network, IEEE Access 10 (2022) 7881–7894.
H. Wu, et al., Phrase dependency relational graph attention network for Aspectbased Sentiment Analysis, Knowledge-Based Systems 236 (2022), 107736.
A. Kumar, et al., BILEAT: a highly generalized and robust approach for unified aspect-based sentiment analysis: BILEAT, Applied Intelligence 52 (12) (2022) 14025–14040.
R.M. Cruz, W.V. de Sousa, G.D. Cavalcanti, Selecting and combining complementary feature representations and classifiers for hate speech detection, Online Social Networks and Media 28 (2022), 100194.
L.L. Tao, T.-H. You, A multi-criteria decision-making model for hotel selection by online reviews: Considering the traveller types and the interdependencies among criteria, Applied Intelligence 52 (11) (2022) 12436–12456.
F.R. Nascimento, G.D. Cavalcanti, M. Da Costa-Abreu, Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on M. Subramanian et al. Alexandria Engineering Journal 80 (2023) 110–121 120 social media using ensemble learning, Expert Systems with Applications 201 (2022), 117032.
H.S. Alatawi, A.M. Alhothali, K.M. Moria, Detecting white supremacist hate speech using domain specific word embedding with deep learning and BERT, IEEE Access 9 (2021) 106363–106374.
Zhao, Y. Yu, Knowledge-enabled BERT for aspect-based sentiment analysis, Knowledge-Based Systems 227 (2021), 107220.
D.R. Beddiar, M.S. Jahan, M. Oussalah, Data expansion using back translation and paraphrasing for hate speech detection, Online Social Networks and Media 24 (2021), 100153.
S. Alsafari, S. Sadaoui, Semi-supervised self-training of hate and offensive speech from social media, Applied Artificial Intelligence 35 (15) (2021) 1621–1645.
S. Kaur, S. Singh, S. Kaushal, Abusive content detection in online user-generated data: a survey,
Procedia Computer Science 189 (2021) 274–281.
M. Sharma, I. Kandasamy, V. Kandasamy, Deep learning for predicting neutralities in offensive language identification dataset, Expert Systems with Applications 185 (2021), 115458.
J. Kocon, et al., Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach, Information Processing & Management 58 (5) (2021), 102643
E. Fu, J. Xiang, C. Xiong, Deep Learning Techniques for Sentiment Analysis, Highlights in Science, Engineering and Technology 16 (2022) 1–7.
Xiang, B. and L. Zhou. Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised training. in Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers). 2014.
F.E. Ayo, et al., A probabilistic clustering model for hate speech classification in twitter, Expert Systems with Applications 173 (2021), 114762.
S.S. Jacob, R. Vijayakumar, Sentimental analysis over twitter data using clustering based machine learning algorithm, Journal of Ambient Intelligence and Humanized Computing (2021) 1–12.
Nurce, E., J. Keci, and L. Derczynski, Detecting abusive Albanian. arXiv preprint arXiv:2107.13592, 2021.
Suryawanshi, S., et al. Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. in Proceedings of the second workshop on trolling, aggression and cyberbullying. 2020.
A. Jiang, et al., SWSR: A Chinese dataset and lexicon for online sexism detection, Online Social Networks and Media 27 (2022), 100182.
Wiegand, M., M. Siegel, and J. Ruppenhofer, Overview of the germeval 2018 shared task on the identification
of offensive language. 2018.
Mollas, I., et al., Ethos: an online hate speech detection dataset. arXiv preprint arXiv:2006.08328, 2020
Salminen, J., et al. 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. 2018.
E. Fu, J. Xiang, C. Xiong, Deep Learning Techniques for Sentiment Analysis, Highlights in Science, Engineering and Technology 16 (2022) 1–7.
Albadi, N., M. Kurdi, and S. Mishra. Are they our brothers? analysis and detection of religious hate speech
in the arabic twittersphere. in 2018 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM). 2018. IEEE.
Curry, A.C., G. Abercrombie, and V. Rieser, ConvAbuse: Data, analysis, and benchmarks for nuanced abuse
detection in conversational AI. arXiv preprint arXiv:2109.09483, 2021
Caselli, T., et al. I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language. in Proceedings of the Twelfth Language Resources and Evaluation Conference. 2020.
Pamungkas, E.W., V. Basile, and V. Patti. Do you really want to hurt me? predicting abusive swearing in social media. in Proceedings of the Twelfth Language Resources and Evaluation Conference. 2020.
Fanton, M., et al., Human-in-the-loop for data collection: a multi-target counter narrative dataset to fight online hate speech. arXiv preprint arXiv:2107.08720, 2021.
Zampieri, M., et al., Predicting the type and target of offensive posts in social media. arXiv preprint arXiv:1902.09666, 2019.
Qian, J., et al., A benchmark dataset for learning to intervene in online hate speech. arXiv preprint arXiv:1909.04251, 2019.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
