Crime and social media

Abstract

Purpose The purpose of this paper is to complement the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012. Design/methodology/approach The empirical evidence is based on ordinary least squares (OLS), Tobit and quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the data set is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa (MENA), Sub-Saharan Africa and Latin America. Findings From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime quantile. Further, when the data set is decomposed into regions and income levels, the negative relationship is evident in the MENA while a positive relationship is confirmed for Sub-Saharan Africa. Policy implications are discussed. Originality/value Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available data set on Facebook.

Covid-19 Pandemic and Outward Foreign Direct Investment: A Preliminary Note

“…Since the study relies on cross-sectional averaged data, Ordinary least square (OLS) is used for the study to follow the extant literature (Andrés, 2006;Kodila-Tedika & Asongu, 2015;Asongu et al, 2019). Following the study of Stoddard and Noy (2015), Dornean and Oanea (2015), equation 1expresses the relationship between FDI outflows and COVID-19 variables including control variables.…”

“…For robustness check, we employ Quantile regression (QR) in order to mitigate the static assumption of conditional mean of the dependent variable under OLS technique. The QR enables us to estimate the parameter throughout the conditional distribution of the dependent variable (Koenker & Bassett, 1978;Asongu et al, 2019). Using this technique, we are able to estimate the lower, intermediate and higher level of FDI outflow.…”

“…X_i is the vector of explanatory variables. QR minimizes the weighted summation of absolute deviations for different quantiles (Asongu et al 2019).…”

Social Discourse of Cyber Hate in Indonesia: The Potential Transition from Hate to Crime

“…Part of the reason for the potential exposure to cyber hate in Indonesia is that internet usage is dominated by communication activities via messages (24.7%) and social media (18.9%). Although more research is needed, there are indications that crime is relevant to social media (Asongu, Nwachukwu, Orim, & Pyke, 2019). Indeed, according to Wang, Yu, Liu, and Young (2019), the two can be linked by using social media as a tool for crime control.…”

The Effect of social media on Individuals’ Perception and Criminal Behavior

“…The relationship may be very complicated. Taking Facebook as an example, Asongu and his colleagues conducted research focusing on the correlation between Facebook penetration and crime rate [7]. It was found that the two factors are actually negatively correlated, which means a higher level of Facebook penetration is correlated with a lower level of violent crime.…”