References
- Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104. doi: https://doi.org/10.1145/2818717
- Stahl, F., Gaber, M. M., & Adedoyin-Olowe, M. (2014). “A survey of data mining techniques for social media analysis”. Journal of Data Mining & Digital Humanities, 2014.
- Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013, May). “Faking sandy: characterizing and identifying fake images on Twitter during hurricane sandy.” In Proceedings of the 22nd international conference on World Wide Web (pp. 729-736).
- Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2012). “Detecting automation of Twitter accounts: Are you a human, bot, or cyborg?”. IEEE Transactions on Dependable and Secure Computing, 9(6), 811-824. doi: https://doi.org/10.1109/TDSC.2012.75
- Schuchard, R., Crooks, A. T., Stefanidis, A., & Croitoru, A. (2019). “Bot stamina: examining the influence and staying power of bots in online social networks”. Applied Network Science, 4(1), 55. doi: https://doi.org/10.1007/s41109-019-0164-x
- Zhang, C. M., & Paxson, V. (2011, March). “Detecting and analyzing automated activity on Twitter”. In International Conference on Passive and Active Network Measurement (pp. 102-111). Springer, Berlin, Heidelberg.
- Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016, April). “Botornot: A system to evaluate social bots”. In Proceedings of the 25th international conference companion on world wide web (pp. 273-274).
- Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. doi: https://doi.org/10.1126/science.aap9559
- Cheng, Y. D., He, J. D., & Hu, F. G. (2017). Quantitative risk analysis method of information security-Combining fuzzy comprehensive analysis with information entropy. Journal of Discrete Mathematical Sciences and Cryptography, 20(1), 149-165. doi: https://doi.org/10.1080/09720529.2016.1178913
- Efthimion, P. G., Payne, S., & Proferes, N. (2018). Supervised machine learning bot detection techniques to identify social twitter bots. SMU Data Science Review, 1(2), 5.
- Rajak, A., Shrivastava, A. K., & Vidushi. (2020). Applying and comparing machine learning classification algorithms for predicting the results of students. Journal of Discrete Mathematical Sciences and Cryptography, 23(2), 419-427. doi: https://doi.org/10.1080/09720529.2020.1728895
- Loyola-González, O., Monroy, R., Rodríguez, J., López-Cuevas, A., & Mata-Sánchez, J. I. (2019). Contrast pattern-based classification for bot detection on twitter. IEEE Access, 7, 45800-45817. doi: https://doi.org/10.1109/ACCESS.2019.2904220
- Kaur, A., & Sinha, A. (2021). Multi-contextual spammer detection for online social networks. Journal of Discrete Mathematical Sciences and Cryptography, 1-10.
- Gaharwar, R. S., & Gupta, R. (2020). Review of cyber security threats and proposed trustworthy memory acquisition mechanism. Journal of Discrete Mathematical Sciences and Cryptography, 23(1), 137-144. doi: https://doi.org/10.1080/09720529.2020.1721877
- Rahul Kedia, P., Sarangi, S., & Monika. (2020). Analysis of machine learning models for malware detection. Journal of Discrete Mathematical Sciences and Cryptography, 23(2), 395-407 doi: https://doi.org/10.1080/09720529.2020.1721870