# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 1 Introduction
The traditional lexicon approaches (also stated as bag-of-words) have some good outcomes on general sentiment analysis such as topic modeling and product or movie review system. The lexicon-based approaches are not considered efficient in sensing extremism due to the limitation of ignoring the semantic orientation, because these approaches perceive the meaning of a textual content using the term frequency only. Therefore, these approaches do not seem to be successful in distinguishing between the tweets having either extreme or neutral views. Unlike dictionary-based approaches, machine learning can also be used to predict social media extreme content. As a common practice, this problem is handled in machine learning using supervised approaches that require pre-defined labels for training a classification model. This limitation is often resolved using sentiment-based dictionaries in order to label each sample. We observed that quite often such dictionary-based approaches to label the training data give false labels due to the limitation of dictionaries to sense the context of sentences. We applied dictionary-based approaches to label tweets, either extreme or neutral, for our Twitter dataset. After carefully observing each tweet and its label, we observed that around 70% of dictionary-based assigned labels, for a sub sample data of 1000 tweets, were incorrect. This has raised some serious questions on earlier work that was often based on a dataset with labels assigned using dictionary-based approaches before applying any machine learning-based classifier [10; 14; 15]. Therefore, for generating training data, we used a dataset where we carefully read each tweet and then assigned a suitable label to it. The need to improve the automatic labelling procedure requires more attention of researchers than ever before. Over the last few decades, machine learning has been widely used for various problems like movie and product review systems, authorship attribution, proteomic and genomic studies, sentiment analysis, etc. However, analysis of the extreme or radicalized social media content studies are very few. The earlier published work of identifying extremism on social media platforms is mostly related to a terrorist group called the Islamic State of Iraq and Syria (ISIS). Very little work has been done in the context of Taliban using social media analysis to identify radicalized content (see Table 1). This study involves analysis of the microblogging site Twitter data (crawled using a set of keywords) using machine learning approach to understand that if a tweet or set of tweets have extreme/radicalized content. This study takes Afghanistan as a region of interest for the purpose of analysis. Among all social media platforms, Twitter is chosen since it is widely used by all communities ranging from common man to celebrities with the short form of text content in every tweet. The maximum size of an earlier tweet message was 140 characters per tweet which had been extended to 250 characters since November 2017. The short length of tweets, however, makes the problem more challenging because users often tweet on a topic giving very limited contextual information making it hard to perform sentiment analysis when compared with other social media platform contents [25]. The main contribution of the proposed methodology are as follows:  
* Importance of Data Labeling: To build a sentiment classification model, a labelled data set is needed to train and test the machine learning model. Commonly, as mentioned earlier, researchers use a dictionary-based method (i.e., SentiWordNet, SentiStrength) for this purpose. We reported that in aspect-based sentiment analysis the current labeling method has a lack of correctness and reliability.