Matthew L. Williams’s research while affiliated with Cardiff University and other places

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members’ Twitter adoption, (b) whether the influences are mediated by the member’s activity variety and self-reported efficacy with mobile technology, and (c) whether the members’ traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members’ Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals’ Twitter adoption.

This paper tests disruption strategies in Twitter networks containing malicious URLs used in drive-by download attacks. Cybercriminals use popular events that attract a large number of Twitter users to infect and propagate malware by using trending hashtags and creating misleading tweets to lure users to malicious webpages. Due to Twitter’s 280 character restriction and automatic shortening of URLs, it is particularly susceptible to the propagation of malware involved in drive-by download attacks. Considering the number of online users and the network formed by retweeting a tweet, a cybercriminal can infect millions of users in a short period. Policymakers and researchers have struggled to develop an efficient network disruption strategy to stop malware propagation effectively. We define an efficient strategy as one that considers network topology and dependency on network resilience, where resilience is the ability of the network to continue to disseminate information even when users are removed from it. One of the challenges faced while curbing malware propagation on online social platforms is understanding the cybercriminal network spreading the malware. Combining computational modelling and social network analysis, we identify the most effective strategy for disrupting networks of malicious URLs. Our results emphasise the importance of specific network disruption parameters such as network and emotion features, which have proved to be more effective in disrupting malicious networks compared to random strategies. In conclusion, disruption strategies force cybercriminal networks to become more vulnerable by strategically removing malicious users, which causes successful network disruption to become a long-term effort.

Despite the increasing prevalence of cybercrime and its study by criminologists, very little research has examined the extent, nature, and impact of fear of cybercrime. In this study, we conducted a multilevel analysis of the 2018 Eurobarometer Cybersecurity Survey to test the applicability of routine activities theory on fear of economic cybercrime. We contribute to the literature by demonstrating that: (1) fear of economic cybercrime varies across EU member states; (2) country-level infrastructure development and income inequality are predictive of individual-level fear; (3) individual-level routine activities and sociodemographic variables are associated with fear; (4) country-level infrastructure development moderates the effects of individual-level guardianship. This paper concludes by emphasizing the importance of including country-level and individual-level determinants in fear of cybercrime research.

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs, and recruit new members. Under- standing the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers net- works and three hateful retweet networks of Twitter users who post content subsequently classified by hu- man annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other ”risky” networks, which can be seen as a risk in terms of the likelihood of expo- sure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.

In light of issues such as increasing unit nonresponse in surveys, several studies argue that social media sources such as Twitter can be used as a viable alternative. However, there are also a number of shortcomings with Twitter data such as questions about its representativeness of the wider population and the inability to validate whose data you are collecting. A useful way forward could be to combine survey and Twitter data to supplement and improve both. To do so, consent within a survey is first needed. This study explores the consent decisions in three large representative surveys of the adult British population to link Twitter data to survey responses and the impact that demographics and survey mode have on these outcomes. Findings suggest that consent rates for data linkage are relatively low, and this is in part mediated by mode, where face-to-face surveys have higher consent rates than web versions. These findings are important to understand the potential for linking Twitter and survey data but also to the consent literature generally.

Twitter has emerged as one of the most popular platforms to get updates on entertainment and current events. However, due to its 280-character restriction and automatic shortening of URLs, it is continuously targeted by cybercriminals to carry out drive-by download attacks, where a user’s system is infected by merely visiting a Web page. Popular events that attract a large number of users are used by cybercriminals to infect and propagate malware by using popular hashtags and creating misleading tweets to lure users to malicious Web pages. A drive-by download attack is carried out by obfuscating a malicious URL in an enticing tweet and used as clickbait to lure users to a malicious Web page. In this article, we answer the following two questions: Why are certain malicious tweets retweeted more than others? Do emotions reflecting in a tweet drive virality? We gathered tweets from seven different sporting events over 3 years and identified those tweets that were used to carry to out a drive-by download attack. From the malicious ( N = 105, 642) and benign ( N = 169, 178) data sample identified, we built models to predict information flow size and survival. We define size as the number of retweets of an original tweet, and survival as the duration of the original tweet’s presence in the study window. We selected the zero-truncated negative binomial (ZTNB) regression method for our analysis based on the distribution exhibited by our dependent size measure and the comparison of results with other predictive models. We used the Cox regression technique to model the survival of information flows as it estimates proportional hazard rates for independent measures. Our results show that both social and content factors are statistically significant for the size and survival of information flows for both malicious and benign tweets. In the benign data sample, positive emotions and positive sentiment reflected in the tweet significantly predict size and survival. In contrast, for the malicious data sample, negative emotions, especially fear, are associated with both size and survival of information flows.

In this article, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyze the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive for a longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors—that is, Jewish organizations—had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organizations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organizations in reducing the propagation of cyberhate and increasing trust in SM platforms.

Forensic science is constantly evolving and transforming, reflecting the numerous technological innovations of recent decades. There are, however, continuing issues with the use of digital data, such as the difficulty of handling large-scale collections of text data. As one way of dealing with this problem, we used machine-learning techniques, particularly natural language processing and Latent Dirichlet Allocation (LDA) topic modeling, to create an unsupervised text reduction method that was then used to study social reactions in the aftermath of the 2017 Manchester Arena bombing. Our database was a set of millions of messages posted on Twitter in the first 24 hours after the attack. The findings show that our method improves on the tools presently used by law enforcement and other agencies to monitor social media, particularly following an event that is likely to create widespread social reaction. For example, it makes it possible to track different types of social reactions over time and to identify subevents that have a significant impact on public perceptions.