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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References
Bai, S., Kolter, J.Z., Koltun, V.: Learning. arXiv:1803.01271 (2018)
Burnap, P., Colombo, G., Amery, R., Hodorog, A., Scourfield, J.: Online Soc Netw Media 2, 32 (2017)
Chancellor, S., Kalantidis, Y., Pater, J.A., De Choudhury, M., Shamma, D.A.: In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17. ACM, pp. 3213–3226 (2017)
Chancellor, S., Kalantidis, Y., Pater, J.A., De Choudhury, M., Shamma, D.A.: In: CHI Conference, pp. 3213–3226 (2017)
Chancellor, S., Lin, Z.J., De Choudhury, M.: In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI ’16, pp. 1157–1162. ACM (2016)
Chancellor, S., Pater, J.A., Clear, T., Gilbert, E., De Choudhury, M.: In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW ’16, pp. 1201–1213. ACM (2016)
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., Chua, T.: In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 6298–6306
Cheng, Q., Li, T.M.H., Kwok, C.L., Zhu, T., Yip, P.S.F.: Journal of Medical Internet Research 19(7) (2017)
Chorowski, J., Weiss, R.J., Bengio, S., van den Oord, A.: IEEE/ACM Trans. Audio, Speech and Lang. Proc. 27(12), 2041–2053 (2019)
Clark, E.M., Jones, C.A., Williams, J.R., Kurti, A.N., Norotsky, M.C., Danforth, C.M., Dodds, P.S.: Plos One 11(7), 1 (2016)
De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., Kumar, M.: In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI ’16, pp. 2098–2110. ACM, New York (2016)
Desmet, B., Hoste, V.: Inform. Sci. 61, 439–440 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), 4171–4186. Association for Computational Linguistics, Minneapolis (2019)
Dong, W., Zhou, M.: IEEE Trans. Syst. Man Cybern. Syst. 47 (7), 1135 (2017)
Dutta, A., Dasgupta, P.: IEEE Trans. Syst. Man Cybern. Syst. 47(11), 2933 (2017)
Frenay, B., Verleysen, M.: IEEE Trans Neural Netw Learn Syst 25(5), 845 (2014)
Grässer, F., Kallumadi, S., Malberg, H., Zaunseder, S.: In: Proceedings of the 2018 International Conference on Digital Health, DH ’18, pp. 121–125. ACM, New York (2018)
Greff, K., Srivastava, R.K., Koutník, J, Steunebrink, B.R., Schmidhuber, J.: IEEE Trans Neural Netw Learn Syst 28(10), 2222 (2017)
Hassanpour, S., Tomita, N., Delise, T., Crosier, B.S., Marsch, L.A.: Neuropsychopharmacology 44(3), 487 (2019)
He, H., Garcia, E.: IEEE Trans. Knowl. Data Eng. 21(9), 1263 (2009)
Ho, T.K.: IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832 (1998)
Information Fusion 52, 290 (2019)
Iqbal, F., Fung, B.C.M., Debbabi, M., Batool, R., Marrington, A.: IEEE Access 7, 22740 (2019)
Jia, Y., Chen, W., Gu, T., Zhang, H., Yuan, H., Lin, Y., Yu, W., Zhang, J.: IEEE Trans. Syst. Man Cybern. Syst. 48(9), 1607 (2018)
Jiang, B., Li, Z., Chen, H., Cohn, A.G.: IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5643 (2018)
Katsuki, T., Mackey, T.K., Cuomo, R.E.: Journal of Medical Internet Research 17(12) (2015)
Kim, Y.: In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha (2014)
Kshetri, N., Voas, J.: Computer 52(4), 64 (2019)
Liang, Y., Zheng, X., Zeng, D.D.: Inf. Fusion 52, 290 (2019)
Liang, Y., Zheng, X., Zeng, D.D., Zhou, X., Leischow, S.J., Chung, W.: J. Med. Internet Res. 17(1), e24 (2015)
Liang, Y., Zheng, X., Zeng, D., Zhou, X., Leischow, S., Chung, W.: Scientific Reports 5 (2015)
Liang, Y., Zhou, X., Zeng, D.D., Guo, B., Zheng, X., Yu, Z.: IEEE Syst. J. 10(3), 1193 (2014)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150. Association for Computational Linguistics, Portland (2011)
Mackey, T.K.: J Med Internet Res 20(4) (2018)
Mackey, T., Kalyanam, J., Klugman, J., Kuzmenko, E., Gupta, R.: J. Med. Internet Res. 20(4), e10029 (2018)
Mahata, D., Friedrichs, J., Shah, R., Jiang, J.: IEEE Intell. Syst. 33(4), 87 (2018)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: In: Procedding of the 2013 International Conference on Learning Representations, ICLR’13 (2013)
Nobles, A.L., Glenn, J.J., Kowsari, K., Teachman, B.A., Barnes. L.E.: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18, pp. 413:1–413:11. ACM, New York (2018)
Pan, S.J., Yang, Q.: IEEE Trans. Knowl. Data Eng. 22(10), 1345 (2010)
Pater, J.A., Haimson, O.L., Andalibi, N., Mynatt, E.D.: In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, CSCW ’16, pp. 1185–1200. ACM (2016)
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers). 2227–2237. Association for Computational Linguistics, New Orleans (2018)
Qiu, C., Squicciarini, A., Griffin, C., Umar, P.: International Foundation for Autonomous Agents and Multiagent Systems, AAMAS ’18, pp. 202–210. Richland, SC (2018)
Semwal, T., Yenigalla, P., Mathur, G., Nair, S.B.: In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 513–521 (2018)
Sinha, P.P., Mishra, R., Sawhney, R., Mahata, D., Shah, R.R., Liu, H.: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM ’19. Association for Computing Machinery, New York, pp. 941–950 (2019)
Skurichina, M., Duin, R.P.W.: Pattern. Anal. Appl. 5(2), 121 (2002)
Sun, S., Luo, C., Chen, J.: Inf. Fusion 36, 10 (2017)
Tadesse, M.M., Lin, H., Xu, B., Yang, L.: IEEE Access 7, 44883 (2019)
Trotzek. M, Koitka, S., Friedrich, C.M.: IEEE Trans. Knowl. Data Eng., 1–1 (2019)
Tsakalidis, G., Vergidis, K.: IEEE Trans. Syst. Man Cybern. Syst. 49(4), 710 (2019)
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., De Pauw, G., Daelemans, W., Hoste, V.: Plos One 13(10), 1 (2018)
Wardle, J.: Int. J. Drug Policy 26(5), 522 (2015)
Zhao, R., Mao, K.: IEEE Trans. Affect. Comput. 8(3), 328 (2017)
Zhao, Z., Wu, Y.: In: 17th Annual Conference of the International-Speech-Communication- Association (INTERSPEECH 2016), pp. 705–709. San Francisco, CA (2016)
Zhou, Z.: Ensemble Methods: Foundations and Algorithms. Taylor & Francis (2012)
Zhou, C., Sun, C., Liu, Z., Lau, F.C.M.: CoRR arXiv:1511.08630 (2015)
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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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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DOI: https://doi.org/10.1007/s11280-020-00840-9