Social Tension and Crime Related Events Detection Method on Twitter

Abstract

Social tension detection methods using social media textual data have been extensively proposed by the researchers. However, most of the high resourced language which is English. There is limited study on social tension detection methods on low resourced language such as Malay language. In fact, the study of social tension using non-standardized Malay language from social media text such as Twitter is remained unexplored. Textual data on Twitter suffers from inconsistencies and high language ambiguity due to the limited permissible character provided for the users to use. Majority of the existing sentiment analysis systems for social tension detection are based on machine learning approach which depends on the static general-purposes sentiment lexicon and ignores the newly created words on Twitter. As word syntactic on Twitter is dynamically changing according to time and context, machine learning approaches may lead to misclassification of word meaning. This article proposes a lexicon-based sentiment analysis approach for detecting tensions and crime related events indicators on Twitter. The automatic detection of social tension and crime related events of the contents on Twitter helps to discover the indicator of tensions amongst the civilians towards the existing situation and predict the potential crime related events.

The Current State of Social Commerce Fraud in Malaysia and the Mitigation Strategies

“…While such studies emphasised the positive aspects of social commerce development, the literature, to date, offers little empirical investigation into social commerce fraud. The advent of the Internet, new methods of communication and related technology has provided new opportunities for online crimes and frauds [15], [16].…”

“…There are numerous different types of fraud activities carried out online. Button et al [16] identified that the most common types of online fraud in the UK and Wales are job scams, investment scams, relationship scams, and purchasing of goods scams. Cross et al [19] also confirmed that there are various different ways in which online fraud can be perpetrated such as advanced fee fraud, employment opportunities, phishing emails, and romance fraud.…”