Exploring Text Mining and Analytics for Applications in Public Security: an in-depth dive into a systematic literature review
DOI:
https://doi.org/10.51359/2965-4661.2023.259008Keywords:
text mining, public security, systematic literature review, technologies, future research directionsAbstract
Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article.References
Aboluwarin, O., Andriotis, P., Takasu, A., & Tryfonas, T. (2016). Optimizing Short Message Text Sentiment Analysis for Mobile Device Forensics. In Proceedings of the 12th IFIP International Conference on Digital Forensics (pp. 69–87). https://doi.org/10.1007/978-3-319-46279-0_4
Adikara, P. P., Adinugroho, S., & Insani, S. (2020). Detection of cyber harassment (cyberbullying) on Instagram using Naïve Bayes classifier with bag of words and lexicon based features. Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology, 64–68. https://doi.org/10.1145/3427423.3427436
Aghababaei, S., & Makrehchi, M. (2018). Mining Twitter data for crime trend prediction. Intelligent Data Analysis, 22(1), 117–141. https://doi.org/10.3233/IDA-163183
Agrawal, H., & Kaushal, R. (2016). Analysis of Text Mining Techniques over Public Pages of Facebook. Proceedings of the IEEE 6th International Conference on Advanced Computing (IACC), 9–14. https://doi.org/10.1109/IACC.2016.12
Al-Khalisy, M. A. E., & Jehlol, H. B. (2018). Terrorist affiliations identifying through Twitter social media analysis using data mining and web mapping techniques. Journal of Engineering and Applied Sciences, 13(17), 7459–7464. https://doi.org/10.36478/jeasci.2018.7459.7464
Al-Nabki, M. W., Fidalgo, E., Alegre, E., & Fernández-Robles, L. (2020). Improving named entity recognition in noisy user-generated text with local distance neighbor feature. Neurocomputing, 382, 1–11. https://doi.org/10.1016/j.neucom.2019.11.072
Al-Ramahi, M., Alsmadi, I., & Davenport, J. (2020). Exploring hackers assets. Proceedings of the 7th Symposium on Hot Topics in the Science of Security, 1–4. https://doi.org/10.1145/3384217.3385619
AL-Saif, H., & Al-Dossari, H. (2018). Detecting and Classifying Crimes from Arabic Twitter Posts using Text Mining Techniques. International Journal of Advanced Computer Science and Applications, 9(10), 377–387. https://doi.org/10.14569/IJACSA.2018.091046
Alagheband, M. R., Mashatan, A., & Zihayat, M. (2020). Time-based Gap Analysis of Cybersecurity Trends in Academic and Digital Media. ACM Transactions on Management Information Systems, 11(4), 1–20. https://doi.org/10.1145/3389684
Alakrot, A., Murray, L., & Nikolov, N. S. (2018). Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic. Procedia Computer Science, 142, 174–181. https://doi.org/10.1016/j.procs.2018.10.473
Alami, S., & Elbeqqali, O. (2015). Cybercrime profiling: Text mining techniques to detect and predict criminal activities in microblog posts. Proceedings of the 10th International Conference on Intelligent Systems: Theories and Applications (SITA), 1–5. https://doi.org/10.1109/SITA.2015.7358435
Alami, S., & Elbeqqali, O. (2016). Text Mining for Suspicious Contents in Mobile Cloud Computing Environment. In M. Sabir, E and Medromi, H and Sadik (Ed.), Advances in Ubiquitous Networking (pp. 117–128). https://doi.org/10.1007/978-981-287-990-5_10
Alatrista-Salas, H., Morzán-Samamé, J., & Nunez-del-Prado, M. (2020). Crime Alert! Crime Typification in News Based on Text Mining. In Lecture Notes in Networks and Systems, Vol. 69, pp. 725–741. https://doi.org/10.1007/978-3-030-12388-8_50
Alguliyev, R. M., Aliguliyev, R. M., & Niftaliyeva, G. Y. (2018). Filtration of Terrorism-Related Texts in the E-Government Environment. International Journal of Cyber Warfare and Terrorism, 8(4), 35–48. https://doi.org/10.4018/IJCWT.2018100103
Almehmadi, A., Joudaki, Z., & Jalali, R. (2017). Language usage on Twitter predicts crime rates. Proceedings of the 10th International Conference on Security of Information and Networks - SIN ’17, 307–310. https://doi.org/10.1145/3136825.3136854
Alothman, B., & Rattadilok, P. (2017). Android botnet detection: An integrated source code mining approach. Proceedings of the 12th International Conference for Internet Technology and Secured Transactions (ICITST), 111–115. https://doi.org/10.23919/ICITST.2017.8356358
Alruily, M., Ayesh, A., & Zedan, H. (2014). Crime profiling for the Arabic language using computational linguistic techniques. Information Processing & Management, 50(2), 315–341. https://doi.org/10.1016/j.ipm.2013.09.001
Andleeb, S., Ahmed, R., Ahmed, Z., & Kanwal, M. (2019). Identification and Classification of Cybercrimes using Text Mining Technique. Proceedings of the 2019 International Conference on Frontiers of Information Technology (FIT), 227–2275. https://doi.org/10.1109/FIT47737.2019.00050
Andriansyah, M., Purwanto, I., Subali, M., Sukowati, A. I., Samos, M., & Akbar, A. (2018). Developing Indonesian corpus of pornography using simple NLP-text mining (NTM) approach to support government anti-pornography program. Proceedings of the Second International Conference on Informatics and Computing (ICIC), 1–4. https://doi.org/10.1109/IAC.2017.8280618
Angenent, M. N., Barata, A. P., & Takes, F. W. (2020). Large-scale machine learning for business sector prediction. Proceedings of the 35th Annual ACM Symposium on Applied Computing, 1143–1146. https://doi.org/10.1145/3341105.3374084
Anwar, T., & Abulaish, M. (2014a). A social graph based text mining framework for chat log investigation. Digital Investigation, 11(4), 349–362. https://doi.org/10.1016/j.diin.2014.10.001
Anwar, T. & Abulaish, M. (2014b). Namesake alias mining on the Web and its role towards suspect tracking. Information Sciences, 276, 123–145. https://doi.org/10.1016/j.ins.2014.02.050
Ariffin, N., Zainal, A., Maarof, M. A., & Kassim, M. N. (2018). A Conceptual Scheme for Ransomware Background Knowledge Construction. Proceedings of the 2018 Cyber Resilience Conference (CRC), 1–4. https://doi.org/10.1109/CR.2018.8626868
Badii, A., Tiemann, M., Adderley, R., Seidler, P., Evangelio, R. H., Senst, T., … Peters, I. (2014). MOSAIC: Multimodal analytics for the protection of critical assets. Proceedings of the 2014 International Conference on Signal Processing and Multimedia Applications (SIGMAP), 311–320. https://doi.org/10.0000/ieeexplore.ieee.org/7514528
Balim, C., & Gunal, E. S. (2019). Automatic Detection of Smishing Attacks by Machine Learning Methods. Proceedings of the 1st International Informatics and Software Engineering Conference (UBMYK), 1–3. https://doi.org/10.1109/UBMYK48245.2019.8965429
Barbon Jr., S., Igawa, R. A., & Zarpelão, B. B. (2017). Authorship verification applied to detection of compromised accounts on online social networks. Multimedia Tools and Applications, 76(3), 3213–3233. https://doi.org/10.1007/s11042-016-3899-8
Bhardwaj, A., & Gupta, R. (2018). Qualitative analysis of financial statements for fraud detection. Proceedings of the 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 318–320. https://doi.org/10.1109/ICACCCN.2018.8748478
Basilio, M. P., Pereira, V., & Brum, G. (2019). Identification of operational demand in law enforcement agencies. Data Technologies and Applications, 53(3), 333–372. https://doi.org/10.1108/DTA-12-2018-0109
Basilio, M. P., Brum, G. S., & Pereira, V. (2020). A model of policing strategy choice. Journal of Modelling in Management, 15(3), 849–891. https://doi.org/10.1108/JM2-10-2018-0166
Battaglia, E., Bioglio, L., & Pensa, R. G. (2020). Towards Content Sensitivity Analysis. In Lecture Notes in Computer Science (Vol. 12080, pp. 67–79). https://doi.org/10.1007/978-3-030-44584-3_6
Behmer, E.-J., Chandramouli, K., Garrido, V., Mühlenberg, D., Müller, D., Müller, W., … Vargas, C. (2019). Ontology Population Framework of MAGNETO for Instantiating Heterogeneous Forensic Data Modalities. In J. MacIntyre, I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations (Vol. 559, pp. 520–531). https://doi.org/10.1007/978-3-030-19823-7_44
Birks, D., Coleman, A., & Jackson, D. (2020). Unsupervised identification of crime problems from police free-text data. Crime Science, 9(1). https://doi.org/10.1186/s40163-020-00127-4
Bisgin, H., Arslan, H., & Korkmaz, Y. (2019). Analyzing the Dabiq Magazine: The Language and the Propaganda Structure of ISIS. In R. Thomson, H. Bisgin, C. Dancy, & A. Hyder (Eds.), Social, Cultural, and Behavioral Modeling (Vol. 11549 LNCS, pp. 1–11). https://doi.org/10.1007/978-3-030-21741-9_1
Bisio, F., Meda, C., Zunino, R., Surlinelli, R., Scillia, E., & Ottaviano, A. (2015). Real-time monitoring of Twitter traffic by using semantic networks. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM ’15, 966–969. https://doi.org/10.1145/2808797.2809371
Bozyiğit, A., Utku, S., & Nasibov, E. (2021). Cyberbullying detection: Utilizing social media features. Expert Systems with Applications, 179, 115001. https://doi.org/10.1016/j.eswa.2021.115001
Calderon, M. H. H., Palad, E. B. B., & Tangkeko, M. S. (2020). Filipino Online Scam Data Classification using Decision Tree Algorithms. Proceedings of the 2020 International Conference on Data Science and Its Applications (ICoDSA), 1–6. https://doi.org/10.1109/ICoDSA50139.2020.9212929
Cardoza, C., & Wagh, R. (2017). Text analysis framework for understanding cyber-crimes. International Journal of Advanced and Applied Sciences, 4(10), 58–63. https://doi.org/10.21833/ijaas.2017.010.010
Castillo-Zúñiga, I., Luna-Rosas, F. J., Rodríguez-Martínez, L. C., Muñoz-Arteaga, J., López-Veyna, J. I., & Rodríguez-Díaz, M. A. (2020). Internet Data Analysis Methodology for Cyberterrorism Vocabulary Detection, Combining Techniques of Big Data Analytics, NLP and Semantic Web. International Journal on Semantic Web and Information Systems, 16(1), 69–86. https://doi.org/10.4018/IJSWIS.2020010104
Cataldo, R., Galasso, R., Grassia, M. G., & Marina, M. (2017). #Theterrormood: Studying the World Mood After the Terror Attacks on Paris and Bruxelles. In N. C. Lauro, E. Amaturo, M. G. Grassia, B. Aragona, & M. Marino (Eds.), Data Science and Social Research (Vol. 2, pp. 185–192). https://doi.org/10.1007/978-3-319-55477-8_17
Chandra, N., Khatri, S. K., & Som, S. (2017). Anti social comment classification based on kNN algorithm. Proceedings of the 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 348–354. https://doi.org/10.1109/ICRITO.2017.8342450
Chang, H.-C., & Wang, C.-Y. (2015). Cloud Incident Data Analytics: Change-Point Analysis and Text Visualization. Proceedings of the 48th Hawaii International Conference on System Sciences, 2015-March, 5320–5330. https://doi.org/10.1109/HICSS.2015.626
Chen, L.-C., Hsu, C.-L., Lo, N.-W., Yeh, K.-H., & Lin, P.-H. (2017). Fraud Analysis and Detection for Real-Time Messaging Communications on Social Networks. IEICE Transactions on Information and Systems, E100.D(10), 2267–2274. https://doi.org/10.1587/transinf.2016INI0003
Chen, W., Aspinall, D., Gordon, A. D., Sutton, C., & Muttik, I. (2016a). A text-mining approach to explain unwanted behaviours. Proceedings of the 9th European Workshop on System Security - EuroSec ’16, 1–6. https://doi.org/10.1145/2905760.2905763
Chen, W., Aspinall, D., Gordon, A. D., Sutton, C., & Muttik, I. (2016b). On Robust Malware Classifiers by Verifying Unwanted Behaviours. In E. Abraham & M. Huisman (Eds.), Integrated Formal Methods, pp. 326–341. https://doi.org/10.1007/978-3-319-33693-0_21
Choi, Y.-J., Jeon, B.-J., & Kim, H.-W. (2021). Identification of key cyberbullies: A text mining and social network analysis approach. Telematics and Informatics, 56, 101504. https://doi.org/10.1016/j.tele.2020.101504
Choudhary, S. P., & Vidyarthi, M. D. (2015). A Simple Method for Detection of Metamorphic Malware using Dynamic Analysis and Text Mining. Procedia Computer Science, 54, 265–270. https://doi.org/10.1016/j.procs.2015.06.031
Chung, W., Liu, J., Tang, X., & Lai, V. S. K. (2018). Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking. Proceedings of the 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), 244–246. https://doi.org/10.1109/ISI.2018.8587364
Cichosz, P. (2018). A Case Study in Text Mining of Discussion Forum Posts: Classification with Bag of Words and Global Vectors. International Journal of Applied Mathematics and Computer Science, 28(4), 787–801. https://doi.org/10.2478/amcs-2018-0060
Concepción-Sánchez, J. A., Molina-Gil, J., Caballero-Gil, P., & Santos-Gonzalez, I. (2018). Fuzzy Logic System for Identity Theft Detection in Social Networks. Proceedings of the 4th International Conference on Big Data Innovations and Applications (Innovate-Data), 65–70. https://doi.org/10.1109/Innovate-Data.2018.00017
Correa, J. C., García-Chitiva, M. del P., & García-Vargas, G. R. (2018). A Text Mining Approach to the Text Difficulty of Latin American Peace Agreement. Revista Latinoamericana de Psicología, 50(1), 61–70. https://doi.org/10.14349/rlp.2018.v50.n1.6
Costa, E., Ferreira, R., Brito, P., Bittencourt, I. I., Holanda, O., MacHado, A., & Marinho, T. (2012). A framework for building web mining applications in the world of blogs: A case study in product sentiment analysis. Expert Systems with Applications, 39(5), 4813–4834. https://doi.org/10.1016/j.eswa.2011.09.135
Das, P. & Das, A. K. (2017a). A two-stage approach of named-entity recognition for crime analysis. Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–5. https://doi.org/10.1109/ICCCNT.2017.8203949
Das, P. & Das, A. K. (2017b). An application of strength Pareto evolutionary algorithm for feature selection from crime data. Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1–6. https://doi.org/10.1109/ICCCNT.2017.8203948
Das, P. & Das, A. K. (2017c). Crime analysis against women from online newspaper reports and an approach to apply it in dynamic environment. Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), 312–317. https://doi.org/10.1109/ICBDACI.2017.8070855
Das, P. & Das, A. K. (2019). Graph-based clustering of extracted paraphrases for labelling crime reports. Knowledge-Based Systems, 179, 55–76. https://doi.org/10.1016/j.knosys.2019.05.004
Dastjerdi, A. R., Foroghi, D., & Kiani, G. H. (2019). Detecting manager’s fraud risk using text analysis: evidence from Iran. Journal of Applied Accounting Research, 20(2), 154–171. https://doi.org/10.1108/JAAR-01-2018-0016
de Boer, M. H. T., Bakker, B. J., Boertjes, E., Wilmer, M., Raaijmakers, S., & van der Kleij, R. (2019). Text Mining in Cybersecurity: Exploring Threats and Opportunities. Multimodal Technologies and Interaction, 3(3), 62. https://doi.org/10.3390/mti3030062
de Carvalho, V.D.H. & Costa, A.P.C.S. (2022). Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review. SciELO Preprints. https://doi.org/10.1590/SciELOPreprints.3518
de la Torre, C. J., Sánchez, D., Blanco, I., & Martín-Bautista, M. J. (2018). Text mining: Techniques, applications, and challenges. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 26(4), 553–582. https://doi.org/10.1142/S0218488518500265
Ding, S. H. H., Fung, B. C. M., & Debbabi, M. (2015). A Visualizable Evidence-Driven Approach for Authorship Attribution. ACM Transactions on Information and System Security, 17(3), 12:2-12:30. https://doi.org/10.1145/2699910
Dong, F., Yuan, S., Ou, H., & Liu, L. (2018). New Cyber Threat Discovery from Darknet Marketplaces. Proceedings of the 2018 IEEE Conference on Big Data and Analytics (ICBDA), 62–67. https://doi.org/10.1109/ICBDAA.2018.8629658
Dong, W., Liao, S., & Liang, L. (2016). Financial statement fraud detection using text mining: A Systemic Functional Linguistics theory perspective. Proceedings of the Pacific Asia Conference on Information Systems (PACIS 2016). Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011024654&partnerID=40&md5=79ef7365d7cd18674dbd1fd346bb5a60
Dong, W., Xu, Y., Liao, S. S., & Feng, X. (2016). Leading effect of social media for financial fraud disclosure: A text mining based analytics. Proceedings of the 22nd Americas Conference on Information Systems. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987638462&partnerID=40&md5=84c1e1ba793ddbbce05ae6f4db558ea1
Elkhawas, A. I. & Abdelbaki, N. (2018). Malware Detection using Opcode Trigram Sequence with SVM. Proceedings of the 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 1–6. https://doi.org/10.23919/SOFTCOM.2018.8555738
Fa, Z., Geng, G.-G., Yan, Z.-W., & Lee, X.-D. (2017). A robust internet abuse detection method. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), 2018-January, 1712–1715. https://doi.org/10.1109/BigData.2017.8258113
Fontanarava, J., Pasi, G., & Viviani, M. (2017). An ensemble method for the credibility assessment of user-generated content. Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence, 863–868. https://doi.org/10.1145/3106426.3106464
Gravanis, G., Vakali, A., Diamantaras, K., & Karadais, P. (2019). Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications, 128, 201–213. https://doi.org/10.1016/j.eswa.2019.03.036
Giacalone, M., Cusatelli, C., Romano, A., Buondonno, A., & Santarcangelo, V. (2018). Big Data and forensics: An innovative approach for a predictable jurisprudence. Information Sciences, 426, 160–170. https://doi.org/10.1016/j.ins.2017.10.036
Gil, V. D., Betancur, J. D., Puerta, I. C., Montoya, L. M., & Sepulveda, J. M. (2018). The Femicide in Colombia and Mexico: A Text Mining Analysis. The Turkish Online Journal of Design, Art and Communication, 2018(SI), 170–177. https://doi.org/10.7456/1080MSE/021
Glancy, F., Biros, D. P., Liang, N., & Luse, A. (2020). Classification of malicious insiders and the association of the forms of attacks. Journal of Criminal Psychology, 10(3), 233–247. https://doi.org/10.1108/JCP-03-2020-0012
Gomes, T., & Ladeira, M. (2020). A new conceptual framework for enhancing legal information retrieval at the Brazilian Superior Court of Justice. Proceedings of the 12th International Conference on Management of Digital EcoSystems, 26–29. https://doi.org/10.1145/3415958.3433087
Gowri, S., Anandha Mala, G. S., & Divya, G. (2014). Enhancing the digital data retrieval system using novel techniques. Journal of Theoretical and Applied Information Technology, 66(2), 481–489. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906493905&partnerID=40&md5=a9ec4a97aabddb9d470ec2136b43cc17
Hadad, T., Puzis, R., Sidik, B., Ofek, N., & Rokach, L. (2018). Application Marketplace Malware Detection by User Feedback Analysis. In P. Mori, S. Furnell, & O. Camp (Eds.), Information Systems Security and Privacy (Vol. 867, pp. 1–19). https://doi.org/10.1007/978-3-319-93354-2_1
Hadad, T., Sidik, B., Ofek, N., Puzis, R., & Rokach, L. (2017). User Feedback Analysis for Mobile Malware Detection. Proceedings of the 3rd International Conference on Information Systems Security and Privacy, 83–94. https://doi.org/10.5220/0006131200830094
Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139–152. https://doi.org/10.1016/j.knosys.2017.05.001
Halouzka, K., & Burita, L. (2019). Cyber Security Strategic Documents Analysis. Proceedings of the 2019 International Conference on Military Technologies (ICMT), 1–6. https://doi.org/10.1109/MILTECHS.2019.8870088
Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems). Retrieved from http://www.amazon.co.uk/Data-Mining-Concepts-Techniques-Management/dp/0123814790
Hao, J., & Dai, H. (2016). Social media content and sentiment analysis on consumer security breaches. Journal of Financial Crime, 23(4), 855–869. https://doi.org/10.1108/JFC-01-2016-0001
Henseler, H., & Hyde, J. (2019). Technology assisted analysis of timeline and connections in digital forensic investigations. CEUR Workshop Proceedings, 2484, 32–37. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076051990&partnerID=40&md5=f2e474c504642f0d296c932a51e9f399
Hicks, C., Beebe, N. L., & Haliscak, B. (2016). Extending web mining to digital forensics text mining. Proceedings of the 22nd Americas Conference on Information Systems. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987624224&partnerID=40&md5=aa7e54ff003d8549a16fb8cc13c41e5c
Hernandez-Castro, J., & Roberts, D. L. (2015). Automatic detection of potentially illegal online sales of elephant ivory via data mining. PeerJ Computer Science, 1(7). https://doi.org/10.7717/peerj-cs.10
Hou, J., Li, X., Yao, H., Sun, H., Mai, T., & Zhu, R. (2020). BERT-Based Chinese Relation Extraction for Public Security. IEEE Access, 8, 132367–132375. https://doi.org/10.1109/ACCESS.2020.3002863
Hultgren, M., Whitney, J., Jennex, M. E., & Elkins, A. (2018). A Knowledge Management Approach to Identify Victims of Human Sex Trafficking. Communications of the Association for Information Systems, 42(1), 602–620. https://doi.org/10.17705/1CAIS.04223
Husari, G., Al-Shaer, E., Ahmed, M., Chu, B., & Niu, X. (2017). TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources. Proceedings of the 33rd Annual Computer Security Applications Conference, Part F1325, 103–115. https://doi.org/10.1145/3134600.3134646
Husari, G., Niu, X., Chu, B., & Al-Shaer, E. (2018). Using Entropy and Mutual Information to Extract Threat Actions from Cyber Threat Intelligence. Proceedings of the 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), 1–6. https://doi.org/10.1109/ISI.2018.8587343
Iftikhar, A., Jaffry, S. W. U. Q., & Malik, M. K. (2019). Information Mining From Criminal Judgments of Lahore High Court. IEEE Access, 7, 59539–59547. https://doi.org/10.1109/ACCESS.2019.2915352
Ishara Amali, H. M. A., & Jayalal, S. (2020). Classification of Cyberbullying Sinhala Language Comments on Social Media. Proceedings of the 2020 Moratuwa Engineering Research Conference (MERCon), 266–271. https://doi.org/10.1109/MERCon50084.2020.9185209
Jackson, P., & Moulinier, I. (2002). Natural language processing for online applications: text retrieval, extraction, and categorization. John Benjamins Publishing Company.
Johnston, A. H., & Weiss, G. M. (2017). Identifying Sunni extremist propaganda with deep learning. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1–6. https://doi.org/10.1109/SSCI.2017.8280944
Kabwe, F., & Phiri, J. (2020). Identity attributes metric modelling based on mathematical distance metrics models. International Journal of Advanced Computer Science and Applications, 11(7), 450–464. https://doi.org/10.14569/IJACSA.2020.0110759
Kakavand, M., Mustapha, A., Tan, Z., Yazdani, S. F., & Arulsamy, L. (2019). O-ADPI: Online Adaptive Deep-Packet Inspector Using Mahalanobis Distance Map for Web Service Attacks Classification. IEEE Access, 7, 167141–167156. https://doi.org/10.1109/ACCESS.2019.2953791
Kao, A., & Poteet, S. R. (2007). Natural Language Processing and Text Mining. Springer London. https://doi.org/10.1007/978-1-84628-754-1
Karystianis, G., Adily, A., Schofield, P., Knight, L., Galdon, C., Greenberg, D., … Butler, T. (2018). Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study. Journal of Medical Internet Research, 20(9), e11548. https://doi.org/10.2196/11548
Karystianis, G., Adily, A., Schofield, P. W., Greenberg, D., Jorm, L., Nenadic, G., & Butler, T. (2019). Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study. Journal of Medical Internet Research, 21(3), e13067. https://doi.org/10.2196/13067
Karystianis, G., Simpson, A., Adily, A., Schofield, P., Greenberg, D., Wand, H., Nenadic, G., & Butler, T. (2020). Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study. Journal of Medical Internet Research, 22(12), e23725. https://doi.org/10.2196/23725
Kavita, Mahani, P., & Ruhil, N. (2016). Web data mining: A perspective of research issues and challenges. 3rd International Conference on Computing for Sustainable Global Development, 3235–3238. Retrieved from http://ieeexplore-ieee-org.ez9.periodicos.capes.gov.br/stamp/stamp.jsp?tp=&arnumber=7724863&isnumber=7724213
Kaur, H., Choudhury, T., Singh, T. P., & Shamoon, M. (2019). Crime Analysis using Text Mining. Proceedings of the 2019 International Conference on Contemporary Computing and Informatics (IC3I), 283–288. https://doi.org/10.1109/IC3I46837.2019.9055606
Kim, Y., Cho, S., Han, S., & You, I. (2018). A software classification scheme using binary-level characteristics for efficient software filtering. Soft Computing, 22(2), 595–606. https://doi.org/10.1007/s00500-016-2357-x
Kitchenham, B. & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. In EBSE Technical Report EBSE-2007-01.
Kuang, D., Brantingham, P. J., & Bertozzi, A. L. (2017). Crime topic modeling. Crime Science, 6(1), 12. https://doi.org/10.1186/s40163-017-0074-0
Kumar, A. S. & Palanisamy, N. (2008). Challenges for Web Mining. Proceedings of the 2008 International Conference on Computing, Communication and Networking, ICCCN 2008. https://doi.org/10.1109/ICCCNET.2008.4787768
Kumar, G. R., Mangathayaru, N., & Narasimha, G. (2015). An approach for Intrusion Detection using Text Mining Techniques. Proceedings of the International Conference on Engineering & MIS 2015, 24-26-Sept, 1–6. https://doi.org/10.1145/2832987.2833076
Lal, S., Tiwari, L., Ranjan, R., Verma, A., Sardana, N., & Mourya, R. (2020). Analysis and Classification of Crime Tweets. Procedia Computer Science, 167, 1911–1919. https://doi.org/10.1016/j.procs.2020.03.211
Lee, P. S., Owda, M., & Crockett, K. (2018). Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards. International Journal of Advanced Computer Science and Applications, 9(1), 1–10. https://doi.org/10.14569/IJACSA.2018.090101
Lee, T.-H., Sung, W.-K., & Kim, H.-W. (2016). A text mining approach to the analysis of information security awareness: Korea, United States, and China. Proceedings of the Pacific Asia Conference on Information Systems. Retrieved from https://aisel.aisnet.org/pacis2016/69
Lekea, I., & Karampelas, P. (2017). Are We Really That Close Together? Tracing and Discussing Similarities and Differences between Greek Terrorist Groups Using Cluster Analysis. Proceedings of the 2017 European Intelligence and Security Informatics Conference (EISIC), 159–162. https://doi.org/10.1109/EISIC.2017.33
Li, L., Xiao, W., Dai, C., Tong, H., & Song, Z. (2014). Mining the Association of Multiple Virtual Identities Based on Multi-Agent Interaction. In H. Wang & M. A. Sharaf (Eds.), Databases Theory and Applications (Vol. 8506 LNCS, pp. 172–179). https://doi.org/10.1007/978-3-319-08608-8_15
Li, W., Chen, H., & Nunamaker, J. F. (2016). Identifying and Profiling Key Sellers in Cyber Carding Community: AZSecure Text Mining System. Journal of Management Information Systems, 33(4), 1059–1086. https://doi.org/10.1080/07421222.2016.1267528
Li, X., Xu, W., & Tian, X. (2014). How to protect investors? A GA-based DWD approach for financial statement fraud detection. Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3548–3554. https://doi.org/10.1109/SMC.2014.6974480
Liang, N., Biros, D.P., & Luse, A. (2016). An Empirical Validation of Malicious Insider Characteristics. Journal of Management Information Systems, 33(2), 361–392. https://doi.org/10.1080/07421222.2016.1205925
Liang, N. & Biros, D. (2016). Validating Common Characteristics of Malicious Insiders: Proof of Concept Study. Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS), 3716–3726. https://doi.org/10.1109/HICSS.2016.463
Lyu, Y., Chow, J. C.-C., & Hwang, J.-J. (2020). Exploring public attitudes of child abuse in mainland China: A sentiment analysis of China’s social media Weibo. Children and Youth Services Review, 116. https://doi.org/10.1016/j.childyouth.2020.105250
Ma, R., Basumallik, S., & Eftekharnejad, S. (2020). A PMU-Based Data-Driven Approach for Classifying Power System Events Considering Cyberattacks. IEEE Systems Journal, 14(3), 3558–3569. https://doi.org/10.1109/JSYST.2019.2963546
Maktabar, M., Zainal, A., Maarof, M. A., & Kassim, M. N. (2018). Content Based Fraudulent Website Detection Using Supervised Machine Learning Techniques. In A. Abraham, P. K. Muhuri, A. K. Muda, & N. Gandhi (Eds.), Hybrid Intelligent Systems, Vol. 734, pp. 294–304. https://doi.org/10.1007/978-3-319-76351-4_30
Malim, N. H. A. H., Sagadevan, S., & Ridzuwan, N. I. (2019). Criminality recognition using machine learning on Malay language tweets. Pertanika Journal of Science and Technology, 27(4), 1803–1820. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074112842&partnerID=40&md5=26ec0607eb6aa010d3f52d95a1db2777
Mansour, S. (2018). Social Media Analysis of User’s Responses to Terrorism Using Sentiment Analysis and Text Mining. Procedia Computer Science, 140, 95–103. https://doi.org/10.1016/j.procs.2018.10.297
Margono, H., Yi, X., & Raikundalia, G. K. (2014). Mining Indonesian Cyber Bullying Patterns in Social Networks. Proceedings of the 37th Australasian Computer Science Conference, 147, 115–124. Retrieved from https://dl.acm.org/doi/abs/10.5555/2667473.2667487
Marivate, V., & Moiloa, P. (2016). Catching crime: Detection of public safety incidents using social media. Proceedings of the 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 1–5. https://doi.org/10.1109/RoboMech.2016.7813140
Martin, A., Calleja, A., Menendez, H. D., Tapiador, J., & Camacho, D. (2016). ADROIT: Android malware detection using meta-information. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. https://doi.org/10.1109/SSCI.2016.7849904
Martinelli, F., Marulli, F., & Mercaldo, F. (2017). Evaluating Convolutional Neural Network for Effective Mobile Malware Detection. Procedia Computer Science, 112, 2372–2381. https://doi.org/10.1016/j.procs.2017.08.216
Marulli, F., & Mercaldo, F. (2017). Let’s Gossip: Exploring Malware Zero-Day Time Windows by Social Network Analysis. Proceedings of the 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), 704–709. https://doi.org/10.1109/WAINA.2017.114
Miah, M.W.R., Yearwood, J., & Kulkarni, S. (2015). Constructing an inter-post similarity measure to differentiate the psychological stages in offensive chats. Journal of the Association for Information Science and Technology, 66(5), 1065–1081. https://doi.org/10.1002/asi.23247
Mine, T., Hirokawa, S., & Suzuki, T. (2019). Does Crime Activity Report Reveal Regional Characteristics? In S. Lee, R. Ismail, & H. Choo (Eds.), Advances in Intelligent Systems and Computing, Vol. 935, pp. 582–598. https://doi.org/10.1007/978-3-030-19063-7_46
Miranda, E., Aryuni, M., Fernando, Y., & Kibtiah, T. M. (2020). A study of radicalism contents detection in Twitter: Insights from support vector machine technique. Proceedings of the 2020 International Conference on Information Management and Technology (ICIMTech 2020), 549–554. https://doi.org/10.1109/ICIMTech50083.2020.9211229
Mishra, S., Shukla, P. K., & Agarwal, R. (2020). Location wise opinion mining of real time Twitter data using Hadoop to reduce cyber crimes. Proceedings of the 2nd International Conference on Data, Engineering and Applications (IDEA 2020). https://doi.org/10.1109/IDEA49133.2020.9170700
Mohasseb, A., Aziz, B., Jung, J., & Lee, J. (2019). Predicting CyberSecurity Incidents using Machine Learning Algorithms: A Case Study of Korean SMEs. Proceedings of the 5th International Conference on Information Systems Security and Privacy, 230–237. https://doi.org/10.5220/0007309302300237
Monish, H., & Pandey, A. C. (2020). A comparative assessment of data mining algorithms to predict fraudulent firms. Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, 117–122. https://doi.org/10.1109/Confluence47617.2020.9057968
Mukherjee, S., & Sarkar, K. (2020). Analyzing Large News Corpus Using Text Mining Techniques for Recognizing High Crime Prone Areas. Proceedings of the 2020 IEEE Calcutta Conference (CALCON 2020), 444–450. https://doi.org/10.1109/CALCON49167.2020.9106554
Nedeljkovic, S., Nikolic, V., Cabarkapa, M., Misic, J., & Randelovic, D. (2019). An Advanced Quick-Answering System Intended for the e-Government Service in the Republic of Serbia. Acta Polytechnica Hungarica, 16(4), 153–174. https://doi.org/10.12700/APH.16.4.2019.4.8
Netsuwan, T., & Kesorn, K. (2017). Unify framework for crime data summarization using RSS feed service. Walailak Journal of Science and Technology, 14(10), 769–781.
Neumann, M., & Sartor, N. (2016). A Semantic Network Analysis of Laundering Drug Money. Journal of Tax Administration, 2(1), 73–94.
Niekerk, B. van, Ramluckan, T., & Duvenage, P. (2019). An analysis of selected cyber intelligence texts. Proceedings of the 18th European Conference on Cyber Warfare and Security, 2019-July, 551–559. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070014329&partnerID=40&md5=c12a9cf42b009feec6f7b2eaf4da79fd
Nikolić, V., Markoski, B., Ivkovic, M., Kuk, K., & Djikanovic, P. (2015). Information retrieval for unstructured text documents in Serbian into the crime domain. Proceedings of the 16th IEEE International Symposium on Computational Intelligence and Informatics, 267–271. https://doi.org/10.1109/CINTI.2015.7382934
Ning, Y., Muthiah, S., Rangwala, H., & Ramakrishnan, N. (2016). Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, San Francisco, 1095–1104. https://doi.org/10.1145/2939672.2939802
Nguyen, A., Hoang, Q., Nguyen, H., Nguyen, D., & Tran, T. (2017). Evaluating marijuana-related tweets on Twitter. Proceedings of the IEEE 7th Annual Computing and Communication Workshop and Conference, 1–7. https://doi.org/10.1109/CCWC.2017.7868364
Noel, G. E. & Peterson, G. L. (2014). Applicability of Latent Dirichlet Allocation to multi-disk search. Digital Investigation, 11(1), 43–56. https://doi.org/10.1016/j.diin.2014.02.001
Noviantho, Isa, S. M., & Ashianti, L. (2017). Cyberbullying classification using text mining. Proceedings of the 1st International Conference on Informatics and Computational Sciences (ICICoS), 241–246. https://doi.org/10.1109/ICICOS.2017.8276369
Nwafor, E., Chowdhary, P., & Chandra, A. (2016). A Policy-Driven Framework for Document Classification and Enterprise Security. Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom, 949–953. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0149
Overbeck, M. (2015). Observers turning into participants: Shifting perspectives on religion and armed conflict in western news coverage. Tocqueville Review, 36(2), 95–124. Retrieved from https://muse.jhu.edu/article/610761
Owda, M., Lee, P. S., & Crockett, K. (2017). Financial Discussion Boards Irregularities Detection System (FDBs-IDS) using information extraction. Proceedings of the 2017 Intelligent Systems Conference (IntelliSys), 1078–1082. https://doi.org/10.1109/IntelliSys.2017.8324262
Öztürk, N. & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136–147. https://doi.org/10.1016/j.tele.2017.10.006
Palad, E. B. B., Tangkeko, M. S., Magpantay, L. A. K., & Sipin, G. L. (2019). Document Classification of Filipino Online Scam Incident Text using Data Mining Techniques. Proceedings of the 19th International Symposium on Communications and Information Technologies, ISCIT 2019, 232–237. https://doi.org/10.1109/ISCIT.2019.8905242
Palad, E. B. B., Burden, M. J. F., Torre, C. R. Dela, & C. Uy, R. B. (2020). Performance evaluation of decision tree classification algorithms using fraud datasets. Bulletin of Electrical Engineering and Informatics, 9(6), 2518–2525. https://doi.org/10.11591/eei.v9i6.2630
Parapar, J., Losada, D. E., & Barreiro, Á. (2014). Combining psycho-linguistic, content-based and chat-based features to detect predation in chatrooms. Journal of Universal Computer Science, 20(2), 213–239. https://doi.org/10.3217/jucs-020-02-0213
Park, J., Park, C., Kim, J., Cho, M., & Park, S. (2019). ADC: Advanced document clustering using contextualized representations. Expert Systems with Applications, 137, 157–166. https://doi.org/10.1016/j.eswa.2019.06.068
Percy, I., Balinsky, A., Balinsky, H., & Simske, S. (2018). Text Mining and Recommender Systems for Predictive Policing. Proceedings of the ACM Symposium on Document Engineering 2018, 1–4. https://doi.org/10.1145/3209280.3229112
Petrovskiy, M. & Chikunov, M. (2019). Online Extremism Discovering through Social Network Structure Analysis. Proceedings of the IEEE 2nd International Conference on Information and Computer Technologies (ICICT), 243–249. https://doi.org/10.1109/INFOCT.2019.8711254
Pina-Sánchez, J., Roberts, J. V, & Sferopoulos, D. (2019a). Does the Crown Court Discriminate Against Muslim-named Offenders? a Novel Investigation Based on Text Mining Techniques. The British Journal of Criminology, 59(3), 718–736. https://doi.org/10.1093/bjc/azy062
Pina-Sánchez, J., Grech, D., Brunton-Smith, I., & Sferopoulos, D. (2019b). Exploring the origin of sentencing disparities in the Crown Court: Using text mining techniques to differentiate between court and judge disparities. Social Science Research, 84, 102343. https://doi.org/10.1016/j.ssresearch.2019.102343
Pires, M., & Georgieva, P. (2020). An Intelligent Tool for Detection of Phishing Messages. In Advances in Intelligent Systems and Computing (Vol. 942, pp. 116–125). https://doi.org/10.1007/978-3-030-17065-3_12
Po, L. & Rollo, F. (2018). Building an Urban Theft Map by Analyzing Newspaper Crime Reports. Proceedings of the 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 13–18. https://doi.org/10.1109/SMAP.2018.8501866
Qazi, N. & Wong, B. L. W. (2019). An interactive human centered data science approach towards crime pattern analysis. Information Processing & Management, 56(6). https://doi.org/10.1016/j.ipm.2019.102066
Rabuzin, K., & Modrušan, N. (2019). Prediction of public procurement corruption indices using machine learning methods. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 3, 333–340. https://doi.org/10.5220/0008353603330340
Rehman, Z. U., Abbas, S., Khan, M. A., Mustafa, G., Fayyaz, H., Hanif, M., & Saeed, M. A. (2020). Understanding the language of ISIS: An empirical approach to detect radical content on Twitter using machine learning. Computers, Materials and Continua, 66(2), 1075–1090. https://doi.org/10.32604/cmc.2020.012770
Ristea, A., Kurland, J., Resch, B., Leitner, M., & Langford, C. (2018). Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets. ISPRS International Journal of Geo-Information, 7(2), 43. https://doi.org/10.3390/ijgi7020043
Robinson, P. H., & Dubber, M. D. (2007). The American Model Penal Code: A Brief Overview. New Criminal Law Review, 10(3), 319-341. https://doi.org/10.1525/nclr.2007.10.3.319
Roopa, V. & Induja, K. (2019). Customized Visualization of Email Using Sentimental and Impact Analysis in R. In M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, T. Ören, & R. Kashyap (Eds.), Advances in Computing and Data Sciences, Vol. 1046, pp. 144–154. https://doi.org/10.1007/978-981-13-9942-8_14
Ruano-Ordás, D., Fdez-Riverola, F., & Méndez, J. R. (2018). Concept drift in e-mail datasets: An empirical study with practical implications. Information Sciences, 428, 120–135. https://doi.org/10.1016/j.ins.2017.10.049
Saha, P., Bose, I., & Mahanti, A. (2016). A knowledge based scheme for risk assessment in loan processing by banks. Decision Support Systems, 84, 78–88. https://doi.org/10.1016/j.dss.2016.02.002
Sahu, S., Agrawal, S., & Baraskar, R. (2019). The effect of best first search optimization on credit card fraudulent transaction detection. International Journal of Innovative Technology and Exploring Engineering, 8(12), 1939–1946. https://doi.org/10.35940/ijitee.L2894.1081219
Saini, J. K., & Bansal, D. (2019). A Comparative Study and Automated Detection of Illegal Weapon Procurement over Dark Web. Cybernetics and Systems, 50(5), 405–416. https://doi.org/10.1080/01969722.2018.1553591
Saldana, M., Escobar, C., Galvez, E., Torres, D., & Toro, N. (2020). Mapping of the Perception of Theft Crimes from Analysis of Newspaper Articles Online. Proceedings of the 15th Iberian Conference on Information Systems and Technologies (CISTI), 2020-June, 1–7. https://doi.org/10.23919/CISTI49556.2020.9141154
Sameera, K., & Vishwakarma, P. (2019). Cybercrime: To Detect Suspected User’s Chat Using Text Mining. In S. C. Satapathy & A. Joshi (Eds.), Information and Communication Technology for Intelligent Systems (Vol. 106, pp. 381–390). https://doi.org/10.1007/978-981-13-1742-2_37
Samtani, S., Chinn, R., Chen, H., & Nunamaker, J. F. (2017). Exploring Emerging Hacker Assets and Key Hackers for Proactive Cyber Threat Intelligence. Journal of Management Information Systems, 34(4), 1023–1053. https://doi.org/10.1080/07421222.2017.1394049
Samtani, S., Yu, S., Zhu, H., Patton, M., Matherly, J., & Chen, H. (2018). Identifying SCADA Systems and Their Vulnerabilities on the Internet of Things: A Text-Mining Approach. IEEE Intelligent Systems, 33(2), 63–73. https://doi.org/10.1109/MIS.2018.111145022
Samtani, S., Zhu, H., & Chen, H. (2020). Proactively Identifying Emerging Hacker Threats from the Dark Web. ACM Transactions on Privacy and Security, 23(4), 1–33. https://doi.org/10.1145/3409289
Sankar, K., Jackovich, J., & Richards, R. (2020). Applied AI and Natural Language Processing Workshop. Packt Publishing Ltd.
Sarwar, S., Qayyum, Z. U., Safyan, M., Iqbal, M., & Mahmood, Y. (2019). Graphs Resemblance based Software Birthmarks through Data Mining for Piracy Control. Programming and Computer Software, 45(8), 581–589. https://doi.org/10.1134/S0361768819080152
Savaliya, B. R., & Philip, C. G. (2017). Email fraud detection by identifying email sender. Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 1420–1422. https://doi.org/10.1109/ICECDS.2017.8389678
Savaş, S. & Topaloğlu, N. (2019). Data analysis through social media according to the classified crime. Turkish Journal of Electrical Engineering & Computer Sciences, 27(1), 407–420. https://doi.org/10.3906/elk-1712-17
Seidler, P., Adderley, R., Badii, A., & Raffaelli, M. (2014). MOSAIC: Criminal network analysis for multi-modal surveillance and decision support. Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 257–260. https://doi.org/10.1109/ASONAM.2014.6921593
Sharef, N. M., & Martin, T. (2015). Evolving fuzzy grammar for crime texts categorization. Applied Soft Computing, 28, 175–187. https://doi.org/10.1016/j.asoc.2014.11.038
Sharmin, S., & Zaman, Z. (2017). Spam Detection in Social Media Employing Machine Learning Tool for Text Mining. Proceedings of the 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 137–142. https://doi.org/10.1109/SITIS.2017.32
Siering, M., Muntermann, J., & Grčar, M. (2021). Design principles for robust fraud detection: The case of stock market manipulations. Journal of the Association for Information Systems, 22(1), 156–178. https://doi.org/10.17705/1jais.00657
Silomon, J. A. M. & Roeling, M. P. (2018). Assessing Opinions on Software as a Weapon in the Context of (Inter)national Security. In M. L. Gavrilova, C. J. K. Tan, & A. Sourin (Eds.), Transactions on Computational Science XXXII (Vol. 10830 LNCS, pp. 43–56). https://doi.org/10.1007/978-3-662-56672-5_4
Slamet, C., Krismunandar, A., Maylawati, D. S., Jumadi, Amin, A. S., & Ramdhani, M. A. (2020). Deep learning approach for bullying classification on Twitter social media with Indonesian language. Proceedings of the 6th International Conference on Wireless and Telematics (ICWT 2020), 1-5. https://doi.org/10.1109/ICWT50448.2020.9243653
Sonowal, G. & Kuppusamy, K. S. (2018). SmiDCA: An Anti-Smishing Model with Machine Learning Approach. The Computer Journal, 61(8), 1143–1157. https://doi.org/10.1093/comjnl/bxy039
Spitters, M., Klaver, F., Koot, G., & Staalduinen, M. van. (2015). Authorship Analysis on Dark Marketplace Forums. Proceedings of the 2015 European Intelligence and Security Informatics Conference, 1–8. https://doi.org/10.1109/EISIC.2015.47
Suarez-Tangil, G., Tapiador, J. E., Peris-Lopez, P., & Blasco, J. (2014). Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families. Expert Systems with Applications, 41(4), 1104–1117. https://doi.org/10.1016/j.eswa.2013.07.106
Subhan, M., Sudarsono, A., & Barakbah, A. (2017). Preprocessing of radicalism dataset to predict radical content in Indonesia. Proceedings of the 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 270–275. https://doi.org/10.1109/KCIC.2017.8228598
Sundarkumar, G. G., Ravi, V., Nwogu, I., & Govindaraju, V. (2015). Malware detection via API calls, topic models and machine learning. Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE), 1212–1217. https://doi.org/10.1109/CoASE.2015.7294263
Sudha, T. S., & Rupa, C. (2019). Analysis and Evaluation of Integrated Cyber Crime Offences. Proceedings of the 2019 Innovations in Power and Advanced Computing Technologies, i-PACT 2019. https://doi.org/10.1109/i-PACT44901.2019.8960187
Tajuddin, T., Manaf, A. A., Fatimah Awang, N., Muhamat Dawam, S. R., Rasidah Ali, N., & Amat, R. (2019). Crime Suspect Profiling (CSP) for Forensic Investigation on Smartphone. Proceedings of the 4th International Conference and Workshops on Recent Advances and Innovations in Engineering: Thriving Technologies. https://doi.org/10.1109/ICRAIE47735.2019.9037772
Talib, R., Kashif, M., Ayesha, S., & Fatima, F. (2016). Text Mining: Techniques, Applications and Issues. International Journal of Advanced Computer Science and Applications, 7(11), 414–418. https://doi.org/10.14569/IJACSA.2016.071153
Tayal, K., & Ravi, V. (2016). Particle Swarm Optimization Trained Class Association Rule Mining. Proceedings of the International Conference on Informatics and Analytics, 25-26-August, 1–8. https://doi.org/10.1145/2980258.2980291
Thao, T. P., Yamada, A., Murakami, K., Urakawa, J., Sawaya, Y., & Kubota, A. (2017). Classification of Landing and Distribution Domains Using Whois’ Text Mining. Proceedings of the 2017 IEEE Trustcom/BigDataSE/ICESS, 1–8. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.213
Tran, Y. H., & Tran, Q. N. (2018). Estimating Public Opinion in Social Media Content Using Aspect-Based Opinion Mining. In J. Hu, I. Khalil, Z. Tari, & S. Wen (Eds.), Mobile Networks and Management. https://doi.org/10.1007/978-3-319-90775-8
Trovati, M., Hill, R., & Bessis, N. (2015). A Non-genuine Message Detection Method Based on Unstructured Datasets. Proceedings of the 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 597–600. https://doi.org/10.1109/3PGCIC.2015.108
Tutun, S., Khasawneh, M. T., & Zhuang, J. (2017). New framework that uses patterns and relations to understand terrorist behaviors. Expert Systems with Applications, 78, 358–375. https://doi.org/10.1016/j.eswa.2017.02.029
Venčkauskas, A., Karpavičius, A., Damaševičius, R., Marcinkevičius, R., Kapočiūtė-Dzikienė, J., & Napoli, C. (2017). Open Class Authorship Attribution of Lithuanian Internet Comments using One-Class Classifier. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, 11, 373–382. https://doi.org/10.15439/2017F461
Ventirozos, F. K., Varlamis, I., & Tsatsaronis, G. (2018). Detecting Aggressive Behavior in Discussion Threads Using Text Mining. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing (Vol. 10762 LNCS, pp. 420–431). https://doi.org/10.1007/978-3-319-77116-8_31
Vidyarthi, D., Choudhary, S. P., Rakshit, S., & Kumar, C. R. S. (2017). Malware Detection by Static Checking and Dynamic Analysis of Executables. International Journal of Information Security and Privacy, 11(3), 29–41. https://doi.org/10.4018/IJISP.2017070103
Wajid, F., & Samet, H. (2016). CrimeStand: Spatial Tracking of Criminal Activity. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 1–4. https://doi.org/10.1145/2996913.2997006
Wang, K., Xiong, Q., Wu, C., Gao, M., & Yu, Y. (2020). Multi-modal cyberbullying detection on social networks. Proceedings of the International Joint Conference on Neural Networks, 1-8. https://doi.org/10.1109/IJCNN48605.2020.9206663
Wei, K., Lin, Y.-R., & Yan, M. (2020). Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants. Proceedings of The Web Conference 2020, 2443–2454. https://doi.org/10.1145/3366423.3380307
Xia, Y., Cai, T., & Zhong, H. (2019). Effect of judges’ gender on rape sentencing: A data mining approach to analyze judgment documents. China Review, 19(2), 125–149. Retrieved from https://muse.jhu.edu/article/726726#info_wrap
Xianghui, Z., Yong, P., Zan, Z., Yi, J., & Yuangang, Y. (2015). Research on Parallel Vulnerabilities Discovery Based on Open Source Database and Text Mining. Proceedings of the 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 327–332. https://doi.org/10.1109/IIH-MSP.2015.84
Xylogiannopoulos, K., Karampelas, P., & Alhajj, R. (2017). Text Mining in Unclean, Noisy or Scrambled Datasets for Digital Forensics Analytics. Proceedings of the 2017 European Intelligence and Security Informatics Conference (EISIC), 2017-Janua, 76–83. https://doi.org/10.1109/EISIC.2017.19
Yang, Y., Manoharan, M., & Barber, K. S. (2014). Modelling and Analysis of Identity Threat Behaviors through Text Mining of Identity Theft Stories. Proceedings of the 2014 IEEE Joint Intelligence and Security Informatics Conference, 184–191. https://doi.org/10.1109/JISIC.2014.35
Yang, B., Jiang, J., & Li, N. (2016). Towards Discovering Covert Communication Through Email Spam. In Z. Shi, S. Vadera, & G. Li (Eds.), Intelligent Information Processing VIII (Vol. 486, pp. 191–201). https://doi.org/10.1007/978-3-319-48390-0_20
Zaeem, R. N., Manoharan, M., Yang, Y., & Barber, K. S. (2017). Modeling and analysis of identity threat behaviors through text mining of identity theft stories. Computers & Security, 65, 50–63. https://doi.org/10.1016/j.cose.2016.11.002
Zahra, K., Azam, F., Butt, W. H., & Ilyas, F. (2018). A Framework for User Characterization based on Tweets Using Machine Learning Algorithms. Proceedings of the II International Conference on Network, Communication and Computing, 11–16. https://doi.org/10.1145/3301326.3301373
Zainal, K., Jali, M. Z., & Hasan, A. B. (2018). Comparative Analysis of Danger Theory Variants in Measuring Risk Level for Text Spam Messages. In Advances in Intelligent Systems and Computing (Vol. 753, pp. 133–152). https://doi.org/10.1007/978-3-319-78753-4_11
Zainol, Z., Jaymes, M. T. H., & Nohuddin, P. N. E. (2018). VisualUrText: A Text Analytics Tool for Unstructured Textual Data. Journal of Physics: Conference Series, 1018, 012011. https://doi.org/10.1088/1742-6596/1018/1/012011
Zaki, M., & Theodoulidis, B. (2014). Analyzing stock market fraud cases using a linguistics-based text mining approach. In A. García-Crespo, J. M. G. Berbís, M. Radzimski, J. L. S. Cervantes, S. Coppens, K. Hammar, … M. Vander Sande (Eds.), Joint Proceedings of the Second International Workshop on Semantic Web Enterprise Adoption and Best Practice and Second International Workshop on Finance and Economics on the Semantic Web, Vol. 1240, pp. 63–74. Anissaras: CEUR-WS.
Zareapoor, M., & Seeja, K. R. R. (2015). Text Mining for Phishing E-mail Detection. In Intelligent Computing, Communication and Devices (Vol. 308 AISC, pp. 65–71). https://doi.org/10.1007/978-81-322-2012-1_8
Zhao, R., Zhou, A., & Mao, K. (2016). Automatic detection of cyberbullying on social networks based on bullying features. Proceedings of the 17th International Conference on Distributed Computing and Networking (ICDCN ’16), 1–6. https://doi.org/10.1145/2833312.2849567
Zhao, R., & Mao, K. (2017). Cyberbullying Detection Based on Semantic-Enhanced Marginalized Denoising Auto-Encoder. IEEE Transactions on Affective Computing, 8(3), 328–339. https://doi.org/10.1109/TAFFC.2016.2531682
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