Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach

Hartung M, Klinger R, Schmidtke F, Vogel L. Identifying Right-Wing Extremism in German Twitter Profiles: a Classification Approach. In: Frascinar F, Ittoo A, Nguyen LM, Métais E, eds. Natural Language Processing and Information Systems: 22nd International Conference on Applications of Natural Language to Information Systems (NLDB 2017). Lecture Notes in Computer Science. Vol 10260. Springer International Publishing; 2017: 320-325.Social media platforms are used by an increasing number of extremist political actors for mobilization, recruiting or radicalization purposes. We propose a machine learning approach to support manual monitoring aiming at identifying right-wing extremist content in German Twitter profiles. We frame the task as profile classification, based on textual cues, traits of emotionality in language use, and linguistic patterns. A quantitative evaluation reveals a limited precision of 25 % with a close-to-perfect recall of 95 %. This leads to a considerable reduction of the workload of human analysts in detecting right-wing extremist users

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing casualties in the Arabian Peninsula, namely `floods'. Researching how we could achieve accurate classification suitable for short informal (colloquial) Arabic text (usually used on Twitter), which is highly inconsistent and received very little attention in this field. First, we provide a thorough technical demonstration consisting of the following stages: data collection (Twitter REST API), labelling, text pre-processing, data division and representation, and training models. This has been deployed using `R' in our experiment. We then evaluate classifiers' performance via four experiments conducted to measure the impact of different stemming techniques on the following classifiers SVM, J48, C5.0, NNET, NB and k-NN. The dataset used consisted of 1434 tweets in total. Our findings show that Support Vector Machine (SVM) was prominent in terms of accuracy (F1=0.933). Furthermore, applying McNemar's test shows that using SVM without stemming on Colloquial Arabic is significantly better than using stemming techniques

Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups

Hartung M, Klinger R, Schmidtke F, Vogel L. Ranking right-wing extremist social media profiles by similarity to democratic and extremist groups. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Stroudsburg PA: Association for Computational Linguistics; 2017: 24-33.Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extrem- ist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support manual attempts towards identifying right-wing extremist content in German Twitter profiles. Based on a fine-grained conceptualization of right- wing extremism, we frame the task as ranking each individual profile on a continuum spanning different degrees of right-wing extremism, based on a nearest neighbour approach. A quantitative evaluation reveals that our ranking model yields robust performance (up to 0.81 F1 score) when being used for predicting discrete class labels. At the same time, the model provides plausible continuous ranking scores for a small sample of borderline cases at the division of right-wing extremism and New Right political movements

Jumanji Extremism? How games and gamification could facilitate radicalization processes

While the last years have seen increased engagement with gaming in relation to extremist attacks, its potential role in facilitating radicalization has received less attention than other factors. This article makes an exploratory contribution to the theoretical foundations of the study of gaming in radicalization research. It is argued that both top-down and bottom up gamification have already impacted extremist discourse and potentially radicalization processes but that research on gamification in other contexts points to a much wider application of gamification to extremist propaganda distribution tools in the future. The potential influence of video games on radicalization processes exceeds the transfer of the popular argument that exposure to violent media leads to desensitization to the context of radicalization and includes the exploitation of pop culture references, increases in self-efficacy regarding violence, and the direct experience of retropian visions through the content of games

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page

Mining Twitter for crisis management: realtime floods detection in the Arabian Peninsula

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of doctor of Philosophy.In recent years, large amounts of data have been made available on microblog platforms such as Twitter, however, it is difficult to filter and extract information and knowledge from such data because of the high volume, including noisy data. On Twitter, the general public are able to report real-world events such as floods in real time, and act as social sensors. Consequently, it is beneficial to have a method that can detect flood events automatically in real time to help governmental authorities, such as crisis management authorities, to detect the event and make decisions during the early stages of the event. This thesis proposes a real time flood detection system by mining Arabic Tweets using machine learning and data mining techniques. The proposed system comprises five main components: data collection, pre-processing, flooding event extract, location inferring, location named entity link, and flooding event visualisation. An effective method of flood detection from Arabic tweets is presented and evaluated by using supervised learning techniques. Furthermore, this work presents a location named entity inferring method based on the Learning to Search method, the results show that the proposed method outperformed the existing systems with significantly higher accuracy in tasks of inferring flood locations from tweets which are written in colloquial Arabic. For the location named entity link, a method has been designed by utilising Google API services as a knowledge base to extract accurate geocode coordinates that are associated with location named entities mentioned in tweets. The results show that the proposed location link method locate 56.8% of tweets with a distance range of 0 – 10 km from the actual location. Further analysis has shown that the accuracy in locating tweets in an actual city and region are 78.9% and 84.2% respectively

Comprehending and Confronting Antisemitism

This volume provides a compendium of the history of and discourse about antisemitism - both as a unique cultural and religious category. Antisemitic stereotypes function as religious symbols that express and transmit a belief system of Jew-hatred, which are stored in the cultural and religious memories of the Western and Muslim worlds, migrating freely between Christian, Muslim and other religious symbolic systems