Twitter as a communications instrument to support the decision making process in UAE police

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySocial media are increasingly becoming platforms of choice for communication among individuals and groups of the public, and hence organisations are interested in engaging with communities and the public through this form of media to gain intelligence from such engagements to support their decision making processes. Yet, organisations are missing on realising the potential value from using social media for this type of interaction and engagement, while paucity in research addressing practical ways to use social media communication in supporting decision making still persists. This research investigates and proposes a practical framework for using social media – specifically Twitter – as a communications instrument to support the decision making process in police organisations. The research design is based on developing and evaluating a proof of concept representing engagements between the United Arab Emirates (UAE) police as a case study of police organisations with communities and members of the public in the UAE. The proof of concept is designed based on comparing and contrasting current practices by the UAE police with models, trends, and practices discussed in related literature. The research uses the Grounded Theory methodology to guide sampling, data collection, and analysis. The contribution of the research is to both theory and practice. The research addresses a gap in the body of knowledge for a framework that guides the development of models, policies, and practices for the use of social media as a communication instrument to support decision making in governmental organisations, specifically the police. In addition, the framework offers practical insights to policy makers into using different social media to engage with the public in communication, interactions, and knowledge sharing, with the aim of supporting decision making

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

A Decade of Social Bot Detection

On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, played a central role in spreading divisive messages and disinformation, possibly contributing to Trump's victory. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Following Trump's exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects that these malicious actors seem to have on our societies. This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times, during the run-up to the 2020 US elections, the question appears as more crucial than ever. What stroke social, political and economic analysts after 2016, deception and automation, has been however a matter of study for computer scientists since at least 2010. In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavours at social bot detection can also inform strategies for detecting and mitigating the effects of other, more recent, forms of online deception, such as strategic information operations and political trolls.Comment: Forthcoming in Communications of the AC

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

A Survey on Computational Propaganda Detection

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of social networks, and the ease of automatically creating and managing coordinated networks of accounts, to reach millions of social network users with persuasive messages, specifically targeted to topics each individual user is sensitive to, and ultimately influencing the outcome on a targeted issue. In this survey, we review the state of the art on computational propaganda detection from the perspective of Natural Language Processing and Network Analysis, arguing about the need for combined efforts between these communities. We further discuss current challenges and future research directions.Comment: propaganda detection, disinformation, misinformation, fake news, media bia

Politische Maschinen: Maschinelles Lernen für das Verständnis von sozialen Maschinen

This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations.Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ökosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen