Expanding and Assembling Approaches to Improve Decisions on Identification and Classification of Online Terrorist Content

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Improving knowledge of online terrorist ecosystems is urgently needed to develop counter measures effectively and responsibly. The aim of this project is to develop new methods for predicting terrorist and extremist behaviour on the Internet. Accurate models of behavioural patterns will allow the predictionof communication channels and make content discovery (and removal) strategies more effective by expanding technological approaches deployed (including managing discovery- /open-source intelligence and interacting with platforms to improve decisions via machine learning) and scoping opportunities to combine approaches into ensemble models, such as combining predictive algorithms. This PhD project aims to apply state of the art and novel algorithmic techniques to classify data, and behaviours related to extremism by developing human-centred processes to clean bias and noise from the data that will be collected from a range of social media platforms. To achieve our aims, firstly, we will use the TCAP platform that will provide us support on getting access to collecting data from a range of social media platform in different media forms (PDF, URL,HTML, audio and videos). The raw data will be converted into a workable dataset such as SQLite, or csv file format. Secondly, to remove noise and bias from the data, we will facilitate a user-centred design process in which we will develop an interactive process that will enable extremist domain experts to perform complex text extraction tasks at scale, as described in [5,7]. The tool will enable users to remove noise in quantifiable ways which will consequently allow us to squeeze the feedback loop between the cleaning process and the user. Thirdly, we will apply novel algorithmic techniques to classify data, and behaviours related to extremism. This will include ensemble methods and/or deep learning techniques. An approach can be based on sentiment-based deep learning models (LSTM+CNN) to classify extremist and non-extremist content [8]. We will apply the process on a closed set of data in the following way. We will focus on analyzing the past occurrences where terrorist organisations have used different platforms to spread propaganda. This project will select one of the recent historical terrorist attacks and will analyze the use of social media by terrorist across these four stages of the attacks. Here, we can use the existing data (from TCAP) during the four different time frames of an attack. In the first step, the student will generate a dataset around user interactions during the four stages. In the second stage, an interactive user centered process will be followed that will help clean the data by involving domain experts. Note, we will consider factors to avoid overfitting during this process. In the third and final step, we will apply state-of-the-art machine learning models such as sentiment-based deep learning, and/or ensemble models to develop classifiers for identifying the type of extremist contents. User-studies will be conducted at major milestones to rate the decisionsupport provided by the machine learning models and to ensure the decisions are justifiable and do not violate the principle of freedom of speech.In summary, the following contributions are anticipated. An interactive, user-centered tool that enables extremism domain experts to assist with data cleaning and removing bias. A range of classifiers to predict the class of different type of extremist contents on a dataset which will be made available publicly to inform future research. The project will investigate the effectiveness of classification for the purpose of content restriction focused on terrorist activity and propaganda. The methodology and technology developed will be scrutinised for its possible unintended use, such attacks on democracy, undue influencing of political decisions, and other fraudulent behaviour, or deliberate introduction of biases into the social media landscape.