# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 2 Related Work
Sentiment analysis on user's online posts and comments considering the POS-based generated dictionary into account was carried out using statistical analysis and named this tool as Sentiment-based Identification of Radical Authors (SIRA). The SIRA predicts the level of extremism in users by analyzing the average sentiment score of posts, volume, severity and duration of any negative posts. In [22], the authors proposed a computational framework that combines social network analysis with sentiment analysis tools to analyze radical groups on YouTube. The data (i.e., comments, profile) were crawled from a group of 700 suspected YouTube accounts. The authors investigated these users on different topics with respect to polarity in order to identify the sign of intolerance and extremism. A lexicon-based module was used to determine the target topic, and then the sentiment analysis technique was applied to get the opinion of users towards these topics. Two different results for males and females are drawn to represent the most positive and negative topics in both categories. The results demonstrate that females are more positive toward _al-Qaida_ and negative toward _Judiasm_. Whereas, for the males, the results demonstrate higher positivity on _Islam_. In [23], Brookings Institution conducted a study to understand the relationship between social media and terrorism. A combined approach of machine learning and manual processing was adopted to analyze population of ISIS supporters on Twitter. The work presented in this paper investigates  
Figure 1: Machine learning techniques used for online radicalization and terrorist detection. 20,000 suspected Twitter accounts and claims that 93% of all examining accounts are supporting ISIS. Furthermore, a minimum threshold of 46,000 and max bound of 90,000 was estimated for existing ISIS-supporting accounts from October to November 2014. Where each account had an average of 1000 followers, considerably higher than any ordinary Twitter user. A hybrid approach of sentiment analysis was proposed in [10] to predict ISIS supporter accounts on Twitter. Herein, real-time tweets are collected using specific query words (i.e., ISIS, bomb, terrorist) with _Twitter Streaming API_. After collecting data, a lexicon-based approach was used for labelling purpose, where _SentiWordNet_ dictionary was used to calculate the score of each tweet. For classification task, naive Bayes' algorithm was used. In this work a two-fold approach was applied where, after classifying every user, more tweets were collected from the same user account and verification of assigned sentiment score was performed using tweet history. Another approach was presented in [24] where tweets were grouped into various radical groups based on the presence of special keywords such as \"Al-Qaida\", \"Jihad\", \"Terrorist Operations\" and \"Extremism\" through a lexical-based approach. A dictionary of semantically related terms (categories) was created by observing hashtags in tweets. For classification task, tweets were vectored based on dictionary related words, such as if any tweets contain dictionary related words, the score of tweets will be 1 otherwise 0. A set of rules was defined for each category and a vector representing each tweet was then compared with all rules to assign the most appropriate category to a tweet having radical content. Most of the work in the literature regarding extremism detection through social media content analysis using machine learning techniques was done related to ISIS. In the context of Afghanistan, much of the region has been in wars in the last fifty years and hence the literacy rate is not much high and a small community use Twitter as a medium of communication. Our research focuses on analyzing Twitter-based data, generated by the people of Afghanistan, in order to identify users having extreme views or neutral views that highly affects the mode of society.