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A multi-view attention-based deep learning system for online deviant content detection

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Abstract

With the exponential growth of user-generated content, policies and guidelines are not always enforced in social media, resulting in the prevalence of deviant content violating policies and guidelines. The adverse effects of deviant content are devastating and far-reaching. However, the detection of deviant content from sparse and imbalanced textual data is challenging, as a large number of stakeholders are involved with different stands and the subtle linguistic cues are highly dependent on complex context. To address this problem, we propose a multi-view attention-based deep learning system, which combines random subspace and binary particle swarm optimization (RS-BPSO) to distill content of interest (candidates) from imbalanced data, and applies the context and view attention mechanisms in convolutional neural network (dubbed as SSCNN) for the extraction of structural and semantic features. We evaluate the proposed approach on a large-scale dataset collected from Facebook, and find that RS-BPSO is able to detect whether the content is associated with marijuana with an accuracy of 87.55%, and SSCNN outperforms baselines with an accuracy of 94.50%.

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Notes

  1. https://github.com/liangyunji1984/deviant

  2. https://restfb.com/

  3. https://snap.stanford.edu/data/web-Amazon.html

  4. http://help.sentiment140.com/for-students

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Acknowledgments

This work is supported by the 2030 National Key AI Program of China under grant No.: 2018AAA0100500, by the natural science foundation of China under grant No.: 61902320, 71472175, 71602184, 71621002, and by the fundamental research funds for the central universities under grant No.:31020180QD140.

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, China

    Yunji Liang, Bin Guo, Zhiwen Yu & Zhu Wang

  2. Institute of Automation Chinese Academy of Sciences, Beijing, China

    Xiaolong Zheng

  3. Chang’an University, Xi’an, China

    Lei Tang

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  1. Yunji Liang
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  2. Bin Guo
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  3. Zhiwen Yu
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  4. Xiaolong Zheng
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  5. Zhu Wang
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  6. Lei Tang
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Correspondence to Yunji Liang.

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Liang, Y., Guo, B., Yu, Z. et al. A multi-view attention-based deep learning system for online deviant content detection. World Wide Web 24, 205–228 (2021). https://doi.org/10.1007/s11280-020-00840-9

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