Exploiting Social Networks for the Prediction of Social and Civil Unrest: A Cloud Based Framework

Abstract

The current worldwide recession has led to a reduction in spending and a tightening of budget at all levels. Measures such as cuts in wages, lower pension pay-outs and rising unemployment seem to go hand-in-hand with politically motivated violence and social instability. In recent times, certain areas of Europe have been met with widespread protests, strikes and riots such as the ones in United Kingdom (UK), Spain, and Greece. Events over the last few years in the UK have demonstrated that people are willing to go to extreme lengths for their voice to be heard. Researchers in this area are still unclear about what leads to social instability and violent protests. How can these events be predicted? What tactics can be deployed by law enforcement agencies to manage these events? Social Networks such as Twitter and Facebook have been proven to be useful tools for demonstrators to organise themselves. Instead of limiting access to these services during any future disorders, filtered information fed from these media can be used by law enforcement agencies not only to prevent using them for criminal behaviour, but also to predict these events and develop tactics to manage future protests. This papers reviews the most cited research in this area and proposes a novel theoretical framework based on digital forensics principles combined with Cloud technology, followed by a sample practical implementation for illustration.

An Online Framework for Civil Unrest Prediction Using Tweet Stream Based on Tweet Weight and Event Diffusion

“…An increase in the recent collective level of knowledge of a future protest is correlated with the occurrence of the onset of a protest in the near future (Wu & Gerber, 2018). Benkhelifa et al (2014) also proved that future unrest events can also be predicted by comparing the current record with historical records of activities during normal and unrest periods in social networking platforms. Similarly, the proposed framework estimates the weight ( ) in future dates (prediction period, F) using historical (window size, H) weights (…”

“…protest is correlated with the 018). Benkhelifa et al (2014) aring the current record with social networking platforms.…”

“…In view of this limitation, other authors use regression techniques like logistics and Least Absolute Shrinkage and Selection Operator (LASSO) regression (Cadena et al, 2015;Korkmaz et al, 2015;Korolov et al, 2016;Qiao & Wang, 2015;Ramakrishnan et al, 2014;Wu & Gerber, 2018), HMM (Hidden Markov Model) (Qiao et al, 2017), random forest (Singh & Pal, 2018) and Long Short-Term Memory Networks (LSTM) (Galla & Burke, 2018) in predicting tasks that use historical event information. The rest of the authors introduce their own prediction models like data mining frameworks based indication and warning assessment, recognition system (IWARS) (Benkhelifa, Rowe, Kinmond, Adedugbe, & Welsh, 2014), graph-based non-parametric heterogeneous graph scan (NPHGS) model (Chen & Neill, 2014) and multi-task learning framework based Multi-Task Feature Learning (MTFL) model (Zhao et al, 2017), Bayesian model fusion framework (Hoegh, Leman, Saraf, & Ramakrishnan, 2015), Naïve Bayes (Hossny & Mitchell, 2018), and keyword frequency-based model (Manrique et al, 2013).…”

“…Some of the articles also extracted temporal expressions like tomorrow, January 1, 7.00 a.m., etc., from news contents (Compton et al, 2014;Muthiah et al, 2015;Ramakrishnan et al, 2014;Wu & Gerber, 2018;Xu et al, 2014), while some of the articles used the frequency of news in their framework (Cadena et al, 2015;Chen & Neill, 2014;van Noord et al, 2016;Wu & Gerber, 2018;Zhao et al, 2017). Another important feature termed as the sentiment hidden in a post was extracted from a few articles for a more in-depth analysis of social media content to improve prediction performance (Benkhelifa et al, 2014;Chen & Neill, 2014;van Noord et al, 2016).…”

Recommendation System for Criminal Behavioral Analysis on Social Network using Genetic Weighted K-Means Clustering

“…The role of civil unrest on social network were examined by Elhadj Benkhelifa [10] due to the similarity in monitoring online social media and digital cloud forensics, a framework was developed which spawned as a combination of these. This framework is applied in order to analyse such networks and produce datasets in order to potentially predict new incidents of civil unrest.…”