# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 1 Introduction  
In recent years, the internet has become a global platform for communication and dissemination of information. Today, popular social media sites have a huge global reach and audience, with Facebook having more than 2.27 billion monthly active users [1], while YouTube boosting almost 1 billion users every month [2]. Similarly, Twitter has an average of 335 million monthly active users [3] and it increased with 14% per day over the last few years [4]. Some other popular social media are Instagram, LinkedIn, Tumblr, WeChat and WhatsApp. The registered number of total users on these social sites are in the billions [5]. Nowadays, due to the extensive use of social media, a large amount of data is generated on a daily basis. The data generated from these sites is in an unstructured form that creates opportunities, as well as challenges for processing and analysis to make a useful understanding of the underlying hidden patterns. The data gathered from the social sites are used for various purposes such as;  
* Marketing & Promotions
* Recommendation system
* Sentiment analysis
* Review system
* Fraud detection
* Crime monitoring
* Terrorism/Extremism detection  
Cyberspace offers freedom of flows of communication and opinion expressing, since there are some groups that are exploiting the capability of social media to spread distorted belief and negative influence on other people. A number of research studies explored that the current social media is regularly being misused by many hate groups to promote radicalization (also stated as cyber-crime, cyber-extremism, and cyber-hate-propaganda) [6; 7; 8; 9]. Many people use these social media platforms to promote harmful ideology by spreading extreme contents among their audience. These radical groups post offensive and violent messages, comments, and hateful speeches focusing on their objectives. They even communicate with other such existing virtual communities to extend their network on social media based on sharing a similar agenda. Social media has now become the easiest way for these extremist groups to recruit new members in their groups by reaching a world-wide audience and then these groups gradually tend to influence newly recruited members in spreading violence and extremism. A study presented in [10; 11] shows that in 2015, more than 125,000 accounts were detected from Twitter through human judgments that were associated with terrorist activities. As a consequence of growing extremism content on Twitter, the company developed a team of professionals in various countries (United States of America (USA), Ireland, etc.,) in order to observe suspected accounts and to block them when identified. The increasing trend of social media for expressing views requires special attention of language processing and machine learning research community to devise techniques that can help government agencies to automatically identify users who have extreme views. Such efforts will surely help the agencies to control crime as the presence of such content on social media in massive volume is one of the biggest concerns nowadays. The issues discussed above make an investigation of online extreme contents, as an important area of research. Researchers from various disciplines such as social science, psychology, and computer science are working hard to develop new tools and techniques to counter and combat the problems of identification of online extremism. Specifically, the need for computer science researcher's attention has risen to a great extent to develop an automated system(s) that help to identify users/groups who are posting extreme views on social media platforms. In order to accomplish this task, most of the researchers used approaches like term frequency and lexicon-based dictionaries such as _SentiStrength_ and _SentiWordNet_[12; 13; 14]. However, the outcome of such techniques is not reliable because human written language is more than word frequencies, since language also has semantic orientations.