State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging

abstract: Health systems are heavily promoting patient portals. However, limited health literacy (HL) can restrict online communication via secure messaging (SM) because patients’ literacy skills must be sufficient to convey and comprehend content while clinicians must encourage and elicit communication from patients and match patients’ literacy level. This paper describes the Employing Computational Linguistics to Improve Patient-Provider Secure Email (ECLIPPSE) study, an interdisciplinary effort bringing together scientists in communication, computational linguistics, and health services to employ computational linguistic methods to (1) create a novel Linguistic Complexity Profile (LCP) to characterize communications of patients and clinicians and demonstrate its validity and (2) examine whether providers accommodate communication needs of patients with limited HL by tailoring their SM responses. We will study >5 million SMs generated by >150,000 ethnically diverse type 2 diabetes patients and >9000 clinicians from two settings: an integrated delivery system and a public (safety net) system. Finally, we will then create an LCP-based automated aid that delivers real-time feedback to clinicians to reduce the linguistic complexity of their SMs. This research will support health systems’ journeys to become health literate healthcare organizations and reduce HL-related disparities in diabetes care.The article is published at https://www.hindawi.com/journals/jdr/2017/1348242

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components

Exploring Cyberterrorism, Topic Models and Social Networks of Jihadists Dark Web Forums: A Computational Social Science Approach

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists\u27 Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in cyberspace, and (c) ways that cyberterrorists attack critical infrastructure, including computer systems, data, websites, and servers. The second article is a methodological study examining the application of natural language processing computational techniques, specifically latent Dirichlet allocation (LDA) topic models and topic network analysis of text data. I demonstrate the potential of topic models by inductively analyzing large-scale textual data of Jihadist groups and supporters from three Dark Web forums to uncover underlying topics. The Dark Web forums are dedicated to Islam and the Islamic world discussions. Some members of these forums sympathize with and support terrorist organizations. Results indicate that topic modeling can be applied to analyze text data automatically; the most prevalent topic in all forums was religion. Forum members also discussed terrorism and terrorist attacks, supporting the Mujahideen fighters. A few of the discussions were related to relationships and marriages, advice, seeking help, health, food, selling electronics, and identity cards. LDA topic modeling is significant for finding topics from larger corpora such as the Dark Web forums. Implications for counterterrorism include the use of topic modeling in real-time classification and removal of online terrorist content and the monitoring of religious forums, as terrorist groups use religion to justify their goals and recruit in such forums for supporters. The third article builds on the second article, exploring the network structures of terrorist groups on the Dark Web forums. The two Dark Web forums\u27 interaction networks were created, and network properties were measured using social network analysis. A member is considered connected and interacting with other forum members when they post in the same threads forming an interaction network. Results reveal that the network structure is decentralized, sparse, and divided based on topics (religion, terrorism, current events, and relationships) and the members\u27 interests in participating in the threads. As participation in forums is an active process, users tend to select platforms most compatible with their views, forming a subgroup or community. However, some members are essential and influential in the information and resources flow within the networks. The key members frequently posted about religion, terrorism, and relationships in multiple threads. Identifying key members is significant for counterterrorism, as mapping network structures and key users are essential for removing and destabilizing terrorist networks. Taken together, this dissertation applies a computational social science approach to the analysis of cyberterrorism and the use of Dark Web forums by jihadists

Trends and challenges of e-government chatbots: Advances in exploring open government data and citizen participation content

This work was supported by the Spanish Ministry of Science and Innovation (PID2019-108965GB-I00) and the Regional Government of Andalusia (P20_00314 and B-SEJ-556-UGR20). The authors thank all people who participated in the reported studies.In this paper, we propose a conceptual framework composed of a number of e-government, implementation and evaluation-oriented variables, with which we jointly analyze chatbots presented in the research literature and chatbots deployed as public services in Spain at national, regional and local levels. As a result of our holistic analysis, we identify and discuss current trends and challenges in the development and evaluation of chatbots in the public administration sector, such as focusing the use of the conversational agents on the search for government information, documents and services –leaving citizen consultation and collaboration aside–, and conducting preliminary evaluations of prototypes in limited studies, lacking experiments on deployed systems, with metrics beyond effectiveness and usability –e.g., metrics related to the generation of public values. Addressing some of the identified challenges, we build and evaluate two novel chatbots that present advances in the access to open government data and citizen participation content. Moreover, we come up with additional, potential research lines that may be considered in the future for a new generation of e-government chatbots.Spanish Ministry of Science and Innovation (PID2019-108965GB-I00)Regional Government of Andalusia (P20_00314 and B-SEJ-556-UGR20

The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging

Health systems are heavily promoting patient portals. However, limited health literacy (HL) can restrict online communication via secure messaging (SM) because patients’ literacy skills must be sufficient to convey and comprehend content while clinicians must encourage and elicit communication from patients and match patients’ literacy level. This paper describes the Employing Computational Linguistics to Improve Patient-Provider Secure Email (ECLIPPSE) study, an interdisciplinary effort bringing together scientists in communication, computational linguistics, and health services to employ computational linguistic methods to (1) create a novel Linguistic Complexity Profile (LCP) to characterize communications of patients and clinicians and demonstrate its validity and (2) examine whether providers accommodate communication needs of patients with limited HL by tailoring their SM responses. We will study >5 million SMs generated by >150,000 ethnically diverse type 2 diabetes patients and >9000 clinicians from two settings: an integrated delivery system and a public (safety net) system. Finally, we will then create an LCP-based automated aid that delivers real-time feedback to clinicians to reduce the linguistic complexity of their SMs. This research will support health systems’ journeys to become health literate healthcare organizations and reduce HL-related disparities in diabetes care

Data Science for Entrepreneurship Research:Studying Demand Dynamics for Entrepreneurial Skills in the Netherlands

The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science --- data science methods --- we are now able to analyze data sets that can be huge, multidimensional, unstructured, and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012-2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population --- outside the domain of entrepreneurs --- is a rewarding subject for future research

Services approach & overview general tools and resources

The contents of this deliverable are split into three groups. Following an introduction, a concept and vision is sketched on how to establish the necessary natural language processing (NLP) services including the integration of existing resources. Therefore, an overview on the state-of-the-art is given, incorporating technologies developed by the consortium partners and beyond, followed by the service approach and a practical example. Second, a concept and vision on how to create interoperability for the envisioned learning tools to allow for a quick and painless integration into existing learning environment(s) is elaborated. Third, generic paradigms and guidelines for service integration are provided.The work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org