Online Library

As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.

The monograph focuses on public support for insurgency and terrorism and how it can be influenced. It is organised around the testing and refinement of conceptual models that seek to integrate much of what is known from relevant social science about public support.

Published on May 29, 2015
This video was streamed live by DMAPLab MIGS on 26 May 2015.

The Islamic State, also known as ISIS or ISIL, has become a household name because it films its atrocities and posts them online thanks to social media platforms such as Twitter and YouTube.

Western countries and Arab states appear to be united and see the group as a threat to international peace and security. But what do we really know about ISIS? What should the international community do to cripple ISIS’ on the battlefield?

Max Abrahms, professor of political science at Northeastern University and member at the Council on Foreign Relations, offers a unique perception of the relationship between the Islamic State’s propaganda and its success as an organisation.

This report aims to contribute to our understanding of online hate in the context of the requirements of the revised Audiovisual Media Services Directive (AVMSD) for Video Sharing Platforms (VSPs) to protect the general public from incitement to hatred or violence. However, online hate is complex and it can only be fully understood by considering issues beyond the very specific focus of these regulations. Hence, we draw on recent social and computational research to consider a range of points outside VSP regulations, such as the impact, nature and dynamics of online hate. For similar reasons,
we have considered expressions of hate across a range of online spaces, including VSPs as well as other online platforms. In particular, we have closely examined how online hate is currently addressed by industry, identifying key and emerging issues in content moderation practices. Our analyses will be relevant to a range of experts and stakeholders working to address online hate, including researchers, platforms, regulators and civil society organisations.

What characterises social media radicals? And why some people become attracted to radicalisation? To explore answers to these questions, a number of tweets posted by a group of suspected radicals tweeting in Arabic were analysed using social network analysis and machine learning. The study revealed that these suspected radicals’ networks showed significant interaction with others; but this interactivity is only significant quantitatively as the interaction is not reciprocated. With regards to why these suspected radicals became attracted to radicalisation, Topic Modelling revealed these suspected radicals’ tweets underpinned a perceived injustice that they believed the Secret Police and the Government inflicted upon them. Overall, the study has shown that data science tools have the potential to inform our understanding of online radicalisation. It is hoped this exploratory study will be the basis for a future study in which the research questions will be answered using a larger sample.

The Islamic State of Iraq and Syria (ISIS) is a Salafi jihadist militant group that has made extensive use of online social media platforms to promulgate its ideologies and evoke many individuals to support the organization. The psycho- sociological background of an individual plays a crucial role in determining his/her vulnerability of being lured into joining the organisation and indulge in terrorist activities, since his/her behavior largely depends on the society s/he was brought up in. Here, we analyse five sociological aspects – personality, values & ethics, optimism/pessimism, age and gender to understand the psycho-sociological vulnerability of individuals over Twitter. Experimental results suggest that psycho-sociological aspects indeed act as foundation to discover and differentiate between prominent and unobtrusive users in Twitter.