# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 2 Related Work  
In the context of extremism and terrorism-related sentiment analysis, most of the previous work is performed on right-wing extremism or on a terrorist group ISIS. A report on terrorism published by USA Center for Cyber and Homeland Security describes that the social media is a key tool to promote radicalization and the study also reveals that this is a widely adopted way of recruiting new members, in extremist groups, across the world particularly focusing on users from Europe and USA [27]. Another study [28] reported that approximately 89% of organized terrorism on the internet take place through social media networks. In response to a terrorist attack in London on 3 June 2017, British Prime Minister blamed social media site in her dialogue: \"We cannot allow this ideology the safe space it needs to breed--yet that is precisely what the internet and the big companies that provide Internet-based services provide\" [29]. Therefore, predicting terrorism from social media becomes an essential job because the terrorism poses a serious threat to society and security of citizens. In order to limit this cyber-terrorism, several researchers show their efforts by predicting radicalization content from the social media platform (see Figure 1). The area of Natural Language Processing (NLP) and sentiment analysis evolve gradually year by year since the last few decades. Figure 1 depicts the machine learning techniques used over a timeline in the existing literature to inspect extreme contents from the web. KNN, naive Bayes', EDA, data clustering, decision tree, Gradient Boosted Decision Tree (GBDT), and Deep Neural Network (DNN) are the most adopted techniques for radicalization detection on social media site(s) [30; 31; 32; 33; 34; 35; 36]. In [12], the authors present a model for detecting radical content from web forums. The model was built using _SentiWordNet_, _WordNet_, and python based Natural Language ToolKit (NLTK) to predict polarity and intensity of radicalization on a web forum. Two different Arabic web forums _Mantada_ and _Qawem_ were used to perform experiments. A dataset containing 500 sentences was selected from each forum and translated manually into the English language. Each sentence was broken into tokens and then stored in a bag-of-words form, after preprocessing POS tagging was used to assign a tag to each word. To assign a score to each word, they used a lexicon approach: _WordNet_ and _SentiWordNet_. The sentence score was then calculated by taking an average of word scores in a sentence. In [14], the authors illustrated a sentiment analysis approach to classify radical text including radical right and radical Islamic on the web. The work presented in the paper is based on a machine learning approach for classifying websites as pro-extremist and anti-extremist. They used a custom-written web crawler called Terrorism and Extremism Network Extractor (TENE) to collect data from 102 online websites. POS tags were used to find high occurrence nouns in the targeted webpages. For assigning sentiment to each page, they used _SentiStrength_ dictionary to evaluate sentiment based on the specific keywords. After labeling with _SentiStrength_, decision tree algorithm was used for classification task. The accuracy of the two-class classification model (i.e., two categories of extremists) was observed to be higher compared with three-class (i.e., three categories of extremists), and four class (i.e., four categories of extremists) classification tasks respectively. In [15], the authors identified the most radical users from the dark web using lexicon-based approach. Four popular web forums (i.e., Gawaher, Islamic Network, Islamic Awakening and Turn to Islam) are investigated that allow discussion on jihadists and terrorism. A list of 400 keywords from these web forums was prepared using POS tagging and then this list was used to get sentiment score of each post of a user.