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Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan’s Politically Incorrect board (/pol/), and Gab, over the course of 13 months. We group the images posted on fringe Web communities (/pol/, Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that /pol/ substantially influences the meme ecosystem with the number of memes it produces, while td has a higher success rate in pushing them to other communities.

In recent decades, information technology went through an explosive evolution, revolutionizing the way communication takes place, on the one hand enabling the rapid, easy and almost costless digital interaction, but, on the other, easing the adoption of more aggressive communication styles. It is crucial to regulate and attenuate these behaviors, especially in the digital context, where these emerge at a fast and uncontrollable pace and often cause severe damage to the targets. Social networks and other entities tend to channel their efforts into minimizing hate speech, but the way each one handles the issue varies. Thus, in this thesis, we investigate the problem of hate speech detection in social networks, focusing directly on Twitter. Our first goal was to conduct a systematic literature review of the topic, targeting mostly theoretical and practical approaches. We exhaustively collected and critically summarized mostly recent literature addressing the topic, highlighting popular definitions of hate, common targets and different manifestations of such behaviors. Most perspectives tackle the problem by adopting machine learning approaches, focusing mostly on text mining and natural language processing techniques, on Twitter. Other authors present novel features addressing the users themselves. Although most recent approaches target Twitter, we noticed there were few tools available that would address this social network platform or tweets in particular, considering their informal and specific syntax. Thus, our second goal was to develop a tokenizer able to split tweets into their corresponding tokens, taking into account all their particularities. We performed two binary hate identification experiments, having achieved the best f-score in one of them using our tokenizer. We used our tool in the experiments conducted in the following chapters. As our third goal, we proposed to assess which text-based features and preprocessing techniques would produce the best results in hate speech detection. During our literature review, we collected the most common preprocessing, sentiment and vectorization features and extracted the ones we found suitable for Twitter in particular. We concluded that preprocessing the data is crucial to reduce its dimensionality, which is often a problem in small datasets. Additionally, the f-score also improved. Furthermore, analyzing the tweets’ semantics and extracting their character n-grams were the tested features that better improved the detection of hate, enhancing the f-score by 1.5% and the hate recall by almost 5% on unseen testing data. On the other hand, analyzing the tweets’ sentiment didn’t prove to be helpful. Our final goal derived from a lack of user-based features in the literature. Thus, we investigated a set of features based on profiling Twitter users, focusing on several aspects, such as the gender of authors and mentioned users, their tendency towards hateful behaviors and other characteristics related to their accounts (e.g. number of friends and followers). For each user, we also generated an ego network, and computed graph-related statistics (e.g. centrality, homophily), achieving significant improvements – f-score and hate recall increased by 5.7% and 7%, respectively.

The development of the Internet and social media has exploded in the last couple of years. Digital environments such as social media and discussion forums provide an effective method of communication and are used by various groups in our societies.  For example, violent extremist groups use social media platforms for recruiting, training, and communicating with their followers, supporters, and donors. Analyzing social media is an important task for law enforcement agencies in order to detect activity and individuals that might pose a threat towards the security of the society.

In this thesis, a set of different technologies that can be used to analyze digital environments from a security perspective are presented. Due to the nature of the problems that are studied, the research is interdisciplinary, and knowledge from terrorism research, psychology, and computer science are required. The research is divided into three different themes. Each theme summarizes the research that has been done in a specific area. The first theme focuses on analyzing digital environments and phenomena. The theme consists of three different studies. The first study is about the possibilities to detect propaganda from the Islamic State on Twitter.  The second study focuses on identifying references to a narrative containing xenophobic and conspiratorial stereotypes in alternative immigration critic media. In the third study, we have defined a set of linguistic features that we view as markers of a radicalization.

Extreme militant groups use their media campaigns to share information, recruit and radicalize followers, share worldviews, and seek public diplomacy ends. While previous research documents that various on-the-ground events correspond to changes in the groups’ messaging strategies, studies of how competing militant groups influence one another’s media campaigns are nascent. This study helps fill that gap by examining how successful attacks by one militant group correspond to changes in both the perpetrating and competing groups’ visual media messaging strategies. It examines attack success through the lens of violent acts that result in direct impact (measured through death counts) and indirect impact (measured through traditional media coverage levels). The study utilizes a content analysis of 1882 authority-related images in AQAP’s al-Masra newsletter and ISIS’s al-Naba’ newsletter appearing three issues before and after each attack, and a chi-square analysis comparing four ISIS attack conditions (high death/high media, high death/low media, low death/high media, and low death/low media). The findings show that a high number of resulting deaths, rather than a high level of media coverage, correspond to changes in the media campaigns of both the perpetrators and the competing groups, with key differences in visual content based on group identity.

The rise of technology has brought with it many benefits but also the potential for great dangers. In particular, Information Communication Technology (ICT) is involved in many facets of life-influencing systems, which range from power plants to airports. Terrorists are now realising the great possibilities of interfering with critical infrastructure. Remote access, reduced costs, automation, replication, speed, direct effect, varied targets and anonymity are all benefits that make attacking computers and networks in cyberspace an attractive solution. ICT could thus serve as a powerful instrument to advance political and ideological viewpoints. The ICT landscape now faces an emerging threat in the form of cyberterrorists. However, it is important not to incorrectly perceive ordinary cyber attacks as cyberterrorism. Cyberterrorism is different from cybercrime in that is has differing motives, attack goals, techniques and intended effects. The motivation for cyberterrorism largely stems from political and ideological views (religious, social activism, retributional). Cyber attacks are mainly driven by financial theft, fraud or espionage, whereas cyberterrorism aims to create publicity for a cause and leave a high impact. In this study, a Cyberterrorism Life Cycle (CLC) Model is developed in order to demonstrate the various factors that lead to the establishment and growth of cyberterrorism. The model depicts the various strategic and technical issues that are relevant to the field. Overall, this model aims to structure the dynamic interaction of the behavioural and technological factors that influence the development of cyberterrorism. Throughout the research, various factors that are influential to cyberterrorism are investigated. The research follows a systematic approach of revealing various underlying issues and thereafter compiling the holistic CLC model to depict these critical issues. Part 1 introduces cyberterrorism and provides the background to the field by discussing incidents and example groups. Initially, the concept of cyberterrorism is explored and the proposed definition tested. Part 2 looks at investigating cyberterrorism more deeply. A conceptual framework is presented that introduces the most pertinent factors in the field of cyberterrorism. Next, the traditional and innovative use of the Internet to carry out and support terrorism is explored. Then, the study addresses the determination of additional social factors using Partial Least Squares Path Modelling. In Part 3, the field of cyberterrorism is more intensely studied. Cyberterrorism is mapped to the Observe-Orient- Decide-Act (OODA) loop, which will form the basis of the CLC model. Thereafter, the most influential concepts essential to the field of cyberterrorism are applied in order to classify attacks as cyberterrorism using ontologies. Furthermore, in Part 3, countermeasures are discussed to look at ways to combat cyberterrorism. Part 4 forms the crux of the research. The CLC model is presented as a structured representation of the various influential factors relevant to cyberterrorism. Thereafter, the CLC model is simulated to show the field more dynamically. Overall, the CLC model presented in this study aims to show the interaction of the various strategic, behavioural and technical issues. The CLC model can help elucidate the reasons for attraction into extremist groups and how attacks are carried out.

Terrorists use cyberspace and social media technology to create fear and spread violent ideologies, which pose a significant threat to public security. Researchers have documented the importance of the application of law and regulation in dealing with the criminal activities in cyberspace. Using routine activity theory, this article assessed the effectiveness of technological approaches to mitigating the expansion and organization of terrorism in cyberspace. Data collection included open-source documents, government threat assessments, legislation, policy papers, and peer-reviewed academic literature and semistructured interviews with fifteen security experts in Nigeria. The key findings were that the new generation of terrorists who are more technological savvy are growing, cybersecurity technologies are effective, and bilateral/multilateral cooperation is essential to combat the expansion of terrorism in cyberspace. The data provided may be useful to stakeholders responsible for national security, counterterrorism, law enforcement on the choice of cybersecurity technologies to confront terrorist expansion in cyberspace.