# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 5 Conclusions and Future Work  
The wide-spread use of social media has heavily impacted the lives of people in communities. Nowadays, extremist organizations often use social media to disseminate their viewpoints to a larger community; either to generate sympathy for their cause or to recruit people. In this work, our objective is to use social media website Twitter-based tweets data in combination with machine learning approaches to automatically identify user tweets having extreme contents. Many such approaches have been proposed in the literature and they often were able to achieve predictive accuracy of around 80% [10; 14; 15; 16; 17; 18]. Most of the earlier reported work is related to ISIS and to our knowledge, this is the novel reported research work in the context of the Afghanistan war zone to predict tweets having extreme and neutral contents. The analysis involves processing tweets data to generate TF-IDF features extracted from 1-g, 2-g, 3-g, etc., and PCA-based reduced features. A two-step analysis was performed: Exploratory Data Analysis (EDA) and Classification Modelling. In terms of EDA, we highlighted the importance of an exploratory data analysis in defining a hypothesis that can be useful in getting better predictive ability of the underlying predictive classification problem. We also demonstrate that simplest extracted features from natural language processing domain can help to identify between extreme and neutral tweet content but are not good enough to differentiate between sub-classes of  
Figure 6: \(n\)-Gram impact on classification model accuracy.  
Figure 7: Impact of Sample size on classification model accuracy.  
extreme (i.e., pro-Afghan government or pro-Taliban). In a classification modelling process, various classification models were applied and SVM classification model has shown a predictive accuracy 84% using TF-IDF features extracted from bi-gram features of tweets.  
The analysis suggests that in order to get better predictive ability in terms of extreme sub-groups, the tweets content semantic knowledge is required in the analysis. We plan to include semantic knowledge in our future work for this purpose. This is important because many tweets contain similar words but have a different context or even opposite opinions. In aspect-based sentiment analysis, the semantics are required to be included to reduce such limitations.  
Data curation, W.S.; Formal analysis, S.M.; Funding acquisition, G.S.C.; Investigation, S.M. and T.A. and G.S.C.; Methodology, S.M. and W.S.; Project administration, S.M. and G.S.C.; Software, W.S. and O.R.; Supervision, S.M. and G.S.C.; Writing--original draft, W.S. and S.M.; Writing--review & editing, S.M., Z.S., T.A. and M.H.  
This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion), and the 2018 Yeungnam University Research Grant.  
The authors declare no conflict of interest.