Abstract
Opioid abuse epidemics is a major public health emergency in the US. Social media platforms have facilitated illicit drug trading, with significant amount of drug advertisement and selling being carried out online. In order to understand dynamics of drug abuse epidemics and design efficient public health interventions, it is essential to extract and analyze data from online drug markets. In this paper, we present a computational framework for automatic detection of illicit drug ads in social media, with Google+ being used for a proof-of-concept. The proposed SVM- and CNN-based methods have been extensively validated on the large dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that our methods can efficiently identify illicit drug ads with high accuracy. Both approaches have been extensively validated using the dataset containing millions of posts collected using Google+ API. Experimental results demonstrate that both methods allow for accurate identification of illicit drug ads.
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References
Centers for Disease Control and Prevention: Provisional Drug Overdose Death Counts. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm. Accessed 3 Feb 2019
Stevens, J.: Internet Stats & Facts for 2019. https://hostingfacts.com/internet-facts-stats/. Accessed 17 Dec 2018
Wu, H.C., Luk, R.W.P., Wong, K.F., Kwok, K.L.: Interpreting TF-IDF term weights as making relevance decisions. ACM Trans. Inf. Syst. (TOIS) 26(3), 13 (2008)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Mackey, T.K., Liang, B.A.: Global reach of direct-to-consumer advertising using social media for illicitonline drug sales. J. Med. Internet Res. 15(5), e105 (2013)
Stroppa, A., di Stefano, D., Parrella, B.: Social media and luxury goods counterfeit: a growing concern for government, industry and consumers worldwide. The Washington Post (2016)
Hu, X., Tang, J., Zhang, Y., Liu, H.: Social spammer detection in microblogging. In: Twenty-Third International Joint Conference on Artificial Intelligence (2013)
Zheng, X., Zeng, Z., Chen, Z., Yu, Y., Rong, C.: Detecting spammers on social networks. Neurocomputing 159, 27–34 (2015)
Agrawal, M., Velusamy, R.L.: R-SALSA: a spam filtering technique for social networking sites. In: 2016 IEEE Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–7. IEEE (2016)
Jain, G., Sharma, M., Agarwal, B.: Spam detection in social media using convolutional and long short term memory neural network. Ann. Math. Artif. Intell. 85(1), 21–44 (2019)
Hull, D.A.: Stemming algorithms: a case study for detailed evaluation. J. Am. Soc. Inf. Sci. 47(1), 70–84 (1996)
Salton, G., McGill, M.J.: Introduction to modern information retrieval (1986)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Zhang, Y., Wallace, B.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820 (2015)
Wu, Y., Skums, P., Zelikovsky, A., Rendon, D.C., Liao, X.: Predicting opioid epidemic by using Twitter data. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds.) ISBRA 2018. LNCS, vol. 10847, pp. 314–318. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94968-0_30
Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation (2011)
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Zhao, F. et al. (2019). Detecting Illicit Drug Ads in Google+ Using Machine Learning. In: Cai, Z., Skums, P., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2019. Lecture Notes in Computer Science(), vol 11490. Springer, Cham. https://doi.org/10.1007/978-3-030-20242-2_15
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DOI: https://doi.org/10.1007/978-3-030-20242-2_15
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