Analysis sentiment about islamophobia when Christchurch attack on social media

Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare Supervised Machine Learning Naïve Bayes and SVM algorithm. Addition of SMOTE for Imbalanced Data. As result, SVM with SMOTE is proven the highest performance value and short processing time

Predicting Online Islamophobic Behavior after #ParisAttack

The tragic Paris terrorist attacks of November 13, 2015 sparked a massive global discussion on Twitter and other social media, with millions of tweets in the first few hours after the attacks. Most of these tweets were condemning the attacks and showing support for Parisians. One of the trending debates related to the attacks concerned possible association between Muslims and terrorism, which resulted in a world-wide debate between those attacking and those defending Islam. In this paper, we use this incident as a case study to examine using online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person’s online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We then quantitatively analyzed users’ network interactions and historical tweets to predict their attitudes towards Islam and Muslims. We provide a description of the quantitative results based on the tweet content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users’ stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms

Stance detection on social media: State of the art and trends

Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on social media, including the task definition, different types of targets in stance detection, features set used, and various machine learning approaches applied. The survey reports state-of-the-art results on the existing benchmark datasets on stance detection, and discusses the most effective approaches. In addition, this study explores the emerging trends and different applications of stance detection on social media. The study concludes by discussing the gaps in the current existing research and highlights the possible future directions for stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this paper. Please withdraw this article before we finish the new versio

Stance characterization and detection on social media

Stance detection refers to the task of identifying a viewpoint as either supporting or opposing a given topic. The current research on socio-political opinion mining on social media is still in its infancy. Most computational approaches in this field are limited to the independent use of textual elements of a user’s posts from social factors such as homophily and network structure. This thesis provides a thorough study of stance detection on social media and assesses various online signals to identify the stance and understand its association with the analysed topic. We explore the task of detecting stance on Twitter, which is a well-known social media platform where people often express stance implicitly or explicitly. First, we examine the relation between sentiment and stance and analyse the inter-play between sentiment polarity and expressed stance. For this purpose, we extend the current SemEval stance dataset by annotating tweets related to four new topics with sentiment and stance labels. Then, we evaluate the effectiveness of sentiment analysis methods on stance prediction using two stance datasets. Second, we examine the multi-modal representation of stance on social media by evaluating multiple stance detection models using textual content and online interactions. The finding of this chapter suggests that using social interactions along with other textual features can improve the stance detection model. Moreover, we show how an unconscious social interaction can reveal the stance. Next, we design an online framework to preserve users’ privacy concerning the implicitly inferred stance on social media. Thus, we evaluate the effectiveness of the two stance obfuscation methods and use different stance detection models to measure the overall performance of the proposed framework. Finally, we study the dynamics of polarized stance to understand the factors that influence online stance. Particularly, we extend the analysis of online stance signals and examine the interplay between stance and automated accounts (bots). Furthermore, we pose the problem of gauging the bots’ effect on polarized stance through a sole focus on the diffusion of bots on the online social network