Online Radicalisation Research Challenges
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The document summarizes Miriam Fernandez's research on online radicalization and extremism. Some key points discussed include analyzing social media data to understand radicalization processes, challenges in online radicalization research like defining prohibited content and comparing approaches, and modeling different roots of radicalization at the micro, meso, and macro levels. Fernandez's work also examines detecting radicalized behavior by translating social theories into computational methods and measuring radicalization influence on social networks.
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Online Radicalisation Research Challenges
- 1. 1 Staffordshire University 5th March 2020 Online Radicalisation: Work, Challenges and Future Directions Presenting: Miriam Fernandez @miriam_fs fernandezmiriam @miriamfs
- 2. 2 Staffordshire University 5th March 2020 Targeting Societal Challenges by Analysing Social Media Data 2
- 3. 3 Staffordshire University 5th March 2020
- 4. 4 Staffordshire University 5th March 2020
- 5. 5 Staffordshire University 5th March 2020 Online Radicalisation • Is the process by which individuals are introduced to ideological messages and belief systems that encourage movement from mainstream beliefs toward extreme views, primarily through the use of online media[International Assoc of Chiefs of Police and United States of America]
- 6. 6 Staffordshire University 5th March 2020 Islamic State in Iraq and Syria (ISIS) Social Media Propaganda & Recruiting
- 7. 7 Staffordshire University 5th March 2020 ISIS on Social Media
- 8. 8 Staffordshire University 5th March 2020 What are the mechanisms that govern the process of radicalisation, and online radicalisation in particular? Social Science Factors Roots Stages Failed Integration Poverty Discrimination Pre-radicalisation Self-identification Indoctrination Jihadization Micro, or individual roots Meso, or social roots Macro, or global roots
- 9. 9 Staffordshire University 5th March 2020 Computer Science Data Collection Analysis Detection Prediction Communication flow Spiritual authorities Propaganda Language evolution Radicalisation process Radicalisation channels Lexicon-based Machine-learningUsers Content False Positives Adopt extremist content (share) Interact with extremist accounts
- 10. 10 Staffordshire University 5th March 2020 Online Radicalisation Research: Challenges Fernandez Miriam and Alani Harith (2020). Online Extremism: Challenges and Opportunities for Artificial Intelligence. Policing and Artificial Intelligence Book. Editors Dr John L.M. McDaniel and Prof Ken (In press)
- 11. 11 Staffordshire University 5th March 2020 Main Challenges lack of a common definition of prohibited radical and extremist internet activity comparison against a control group comparison against approaches (no replication studies) Content adaptation User-Account adaptation Platform adaptation Technology adaptation technology needs to comply with legislation that can sometimes be ambiguous or contradictory, particularly when it comes to the tension between security, privacy and freedom of expression.
- 12. 12 Staffordshire University 5th March 2020 Mechanisms for Data Collection, Verification and Validation
- 13. 13 Staffordshire University 5th March 2020 Data Issues
- 14. 14 Staffordshire University 5th March 2020 RQ. How can we translate the different aspects of social theories of radicalisation into computational methods to enable the automatic identification of radicalised behaviour?
- 15. 15 Staffordshire University 5th March 2020 Fernandez, Miriam, Moizzah Asif, and Harith Alani. "Understanding the roots of radicalisation on twitter." Proceedings of the 10th ACM Conference on Web Science. 2018. Fernandez, Miriam; Gonzalez-Pardo, Antonio and Alani, Harith (2019). Radicalisation Influence in Social Media. Journal of Web Science, 6 Understanding the roots of radicalisation on twitter
- 16. 16 Staffordshire University 5th March 2020 Roots of Radicalisation & Radicalisation Influence Micro or Individual roots Macro or Global roots Meso or Group roots Radicalisation Influence Content from friends Authored posts Content from news and other websites Radicalisation Influence • Individual: similarity of own content to radicalisation terminology • Social: similarity of retweeted content from followees to radicalization terminology • Global: similarity of content shared from news and websites to radicalization terminology
- 17. 17 Staffordshire University 5th March 2020 Modelling Roots of Radicalisation Feeling lonely today http://url1 RT @diabla We need to fight for our brothers! http://url2 RT @minid Allah is always with you! RT @dk Dabiq is our message! http://url2 RT @hobbes what a beautiful day today! I have brothers who pray for me! http://url3 @Calvi n Micro or Individual roots Meso or Group roots Macro or Global roots http://url1 http://url2 http://url3
- 18. 18 Staffordshire University 5th March 2020 Calculating Radicalisation Influence Radicalisation Lexicon w1 w2 w3
- 19. 19 Staffordshire University 5th March 2020 Experiments & Results General • 112 accounts manually assessed as “general” by 2 annotators • 197,743 tweets Datasets Pro-ISIS • 112 pro-ISIS accounts • 17,350 tweets
- 20. 20 Staffordshire University 5th March 2020 Social Influence The more radicalisation content a user is exposed to and shares, the more likely for them to adopt a similar language over time. • The network is the essence of social media platforms. • Harm propagates across the network, and influences recipients over time. • Need to protect and alert users to harmful influences and influencers. • Monitor and regulate the use of networking recommendation algorithms. Individual influence: similarity of own content to radicalisation terminology Socialinfluence:similarityof retweetedcontenttoradicalisation terminology Tools to measure radicalisation influence and behaviour
- 21. 21 Staffordshire University 5th March 2020 Predicting Radicalisation Influence w1 w2 w3 w4 ? W5 w1 w2 w3 w5 w6 w11 w7 w1 Present Prediction Radicalisation Influence Collaborative Filtering 1) Look for users that have a similar word-use / “rating” pattern to that of the active user 2) use the ratings of users found in step 1 to compute the predictions for the active user
- 22. 22 Staffordshire University 5th March 2020 Fernandez, Miriam and Alani, Harith (2018). Contextual Semantics for Radicalisation Detection on Twitter. In: Semantic Web for Social Good Workshop (SW4SG) at International Semantic Web Conference 2018, 9 Oct 2018, CEUR. Saif, Hassan; Dickinson, Thomas; Kastler, Leon; Fernandez, Miriam and Alani, Harith (2017). A Semantic Graph-Based Approach for Radicalisation Detection on Social Media. In: ESWC 2017: The Semantic Web - Proceedings, Part I, Lecture Notes in Computer Science, Springer, Contextual Semantics for Radicalisation Detection on Twitter
- 23. 23 Staffordshire University 5th March 2020 Radical Terminology: Usage Divergence pro-ISIS users “general” users Radical Terminology
- 24. 24 Staffordshire University 5th March 2020 Tweets Conceptual. Semantics. Extraction DBpedia Semantic.Graph. Representation Frequent.Semantic. Subgraph.Mining Classifier.Training Pipeline of detecting pro-ISIS stances using semantic sub-graph mining-based feature extraction • Extract and use the semantic interdependencies and relations between words to learn patterns of radicalisation. ISIS Syria Jihadist Group Country (Military Intervention Against ISIL, place, Syria) Entities Concepts Semantic Relations Semantic Graph-based Approach for Pro-ISIS Stance Detection
- 25. 25 Staffordshire University 5th March 2020 Semantic Graph-based Approach for Pro-ISIS Stance Detection Step 1. Conceptual Semantic Extraction Training Data: 566 pro-ISIS users / 566 anti-ISIS users (extracted using lexicons) Entity Extraction and Semantics Mapping Syria -> Country ISIL-> Jihadist Group Syria -> Country ISIL-> Jihadist Group DBpedia Step 2. Semantic Graph Representation Step 3. Sub-graph Mining CloseGraph Method (Yan and Han 2003)
- 26. 26 Staffordshire University 5th March 2020 Evaluation & Results • Baseline for comparison SVM classifiers trained from Unigrams, Topic, Sentiment and Network feature sets. • 10-Folds cross validation over 30 runs 86.3 86.3 84.8 86 91.7 84.4 84.4 81 87.1 92.8 80 82 84 86 88 90 92 94 Unigrams Sentiment Topics Network Semantics anti-ISIS pro-ISIS Exploration of semantic sub-graphs • pro-ISIS users tend to discuss about religion, historical events and ethnicity • anti-ISIS users focus more on politics, geographical locations and interventions against ISIS
- 27. 27 Staffordshire University 5th March 2020 Global (Mis)perceptions of Radicalisation
- 28. 28 Staffordshire University 5th March 2020 Global (mis)perception of radicalisation • Same messages are judged very differently in different parts of the world • Highlights differences and potential bias Not-Radical Radical Tie NA: North America SA: South America ME: Middle East AS: Asia EU: Europe AF: Africa
- 29. 29 Staffordshire University 5th March 2020 Online ‘extreme’ Misogyny Farrell Tracie, Fernandez Miriam, Novotny Jakub and Alani Harith (2019). Exploring Misogyny across the Manosphere in Reddit. 10th ACM Conference on Web Science
- 30. 30 Staffordshire University 5th March 2020 The Manosphere
- 31. 31 Staffordshire University 5th March 2020 Experiencing the Manosphere Resentment of Women Attribution of Blame
- 32. 32 Staffordshire University 5th March 2020 RQ2: Which groups express the most violent attitudes and what are the most common forms of online misogynistic violence? RQ1. How do these communities differ in terms of their use of violent or misogynistic speech? Studying Misogyny at Scale I/NON-FEMINIST “Manosphere” aligned Agents” Future emerging orms eminism Pick-up Artists (PUA) Involuntarily Celibate (INCEL) Men’s Rights Activists (MRA) Men Going Their Own Way (MGTOW) ans “Menenists” Father’s Rights
- 33. 33 Staffordshire University 5th March 2020 Feminist/Sociological • Historical/contextual approaches • Rich description of communities Existing Approaches • Small number of researchers • Mostly qualitative Computer Science • Observational studies, analysing specific data • Predictive studies • Preference for certain platforms • Less attention to sociological/feminist models
- 34. 34 Staffordshire University 5th March 2020 Our Approach 1. Grounded on exploring feminist models at scale 2. Utilising a dataset of 6M posts, 300K conversations on Reddit 3. Observing that data over a longer period of time (2011-2018)
- 35. 35 Staffordshire University 5th March 2020 Feminist Sociological Models • Misogyny includes: belittling, hostility, sexual violence, and physical violence, patriarchy (Code, 2002) • Misogyny is often accompanied by homophobic and racist rhetoric (Davies, 2008) • Misogyny is developing towards: stoicism and flipped narratives of feminism (Zuckerberg, 2018 and Ging, 2017)
- 36. 36 Staffordshire University 5th March 2020 Ingesting and Annotating Terms • Terms taken from a variety of online dictionaries for profanity, racist or homophobic abuse, violence verbs and hate speech (2,454 unique terms) • Annotated using a coding scheme developed from the literature
- 37. 37 Staffordshire University 5th March 2020 Development of the Lexicons • creation of 9 lexicons • 1300 annotated terms
- 38. 38 Staffordshire University 5th March 2020 Selection of Subreddits
- 39. 39 Staffordshire University 5th March 2020 Authors • Increase in authorship since 2016 • Sharpest in r/braincels after r/incels is quarantined
- 40. 40 Staffordshire University 5th March 2020 Studying the Manosphere at Scale Sexual Violence Hostility
- 41. 41 Staffordshire University 5th March 2020 What we learned • Authorship in the more violent communities appears to be growing. • Language within misogynistic groups appears to have become more hostile over time, which corroborates existing feminist scholarship. Non-violent groups tend to express more stoicism. • Groups who expressed the most violent attitudes toward women also express violent attitudes toward other minorities or groups that experience exclusion. Co-occurrence found in feminist scholarship. • Hostility is the most common type of misogynistic violence. RQ1. How do these communities differ in terms of their use of violent or misogynistic speech? RQ2: Which groups express the most violent attitudes and what are the most common forms of online misogynistic violence?
- 42. 42 Staffordshire University 5th March 2020 Thanks for Listening!
Editor's Notes
- Salafi jihadist militant group that follows an Islamic fundamentalist, Wahhabi doctrine of Sunni Islam who managed in less than a year to capture of large swathes of land across Syria and Iraq
- What differentiate ISIS from other extremist and radicalized groups is their effective and successful use of Social media to promote their propaganda and recruited new members online. In fact ISIS is considered by many research and governmental organizations as the most successful Jihadi group online This has reflected by large number of people from the west adopting ISIS propaganda and move to joining them in Syria and Iraq CNN has estimated that up to 11,000 foreigners have travelled to Syria to take up arms agains Bashar Al Assad's government. When Islamic majority countries aren't taken into account, then the vast majority of these come from Russia, unsurprising given its large Muslim population. Substantial numbers of fighters have also come from France, the United Kingdom and Germany - some 700, 500 and 300 respectively. Over 100 fighers have also travelled to Syria from the United States. leading organisations in the use of social media for sharing their propaganda, for raising funds, and for radicalising and recruiting individuals around the globe. According to a 2015 U.S government report,3 this organisation succeeded in recruiting more than 25,000 foreign fighters in Syria and Iraq, including 4,500 from Europe and North America.
- In 2007 the New York Police Department (NYPD) published their own model of radicalisation [33], focused on Jihadi-Salafi ideology and “the west”. This model is composed of four distinct phases. Pre-radicalisation; most individuals at this stage have lived “ordinary” lives and have little, if any criminal history. In a second stage, self-identification, individuals, influenced by both, internal and external factors, (loosing a job, alienation and discrimination, death in the close family, etc.) begin to explore Salafi Islam. In the third phase, indoctrination, individuals progressively intensify their beliefs and conclude that circumstances exist where action is required to support the cause. In the final phase, jihadisation, individuals accept their individual duty to participate in violent jihad and self-designate themselves as holy warriors.
- It is however difficult to understand how the radicalisation process tends to kickstart and evolve online, especially when the amount of traffic generated in social media is so vast. Manual analysis is impractical and thus automatic techniques need to be used. We need to look at online radicalisation as a process, and to leverage closer the knowledge of theoretical models of radicalisation to design more effective technological solutions to tracking online radicalisation.
- When a user participates in a social media platform, she can perform two main actions in terms of posting: (i) creating and posting new content and (ii) sharing content posted by someone within her network. In our work we assume that the micro (individual) root is captured by all the posts that the user has created. Similarly, the meso (or social) influence is captured by all the post that the user has shared.We are aware that a user is exposed to more information than the one that she shares. However, when a user is sharing a piece of content, it is a strong indicator that that piece of content has somehow influenced the user who is making it part of her own ideas and believes. Within the posts that a user creates or shares from her network we can also find links (URLs) to external sites (YouTube videos, news sites, blogs, etc.). These sites capture the macro (global) level of influence over an individual.
- Need to remember that FB, Instagram, Twitter, etc etc are made up of networks. network is backbone when it comes to regulating and sanitising the platforms, we need to take the network into account. We need to understand how people get influenced on these networks, and how harm propagates across them. Dataset from kaggle 112 pro-ISIS 112 general accounts who used ISIS terminology
- As discussed before, the discriminative power of features used for radicalisation detection often relies on the latent semantic interdependencies that exist between certain words in tweets. As such, the proposed approach aims to extract and use such interdependencies and relations to learn patterns of radicalisation.
- One of the first things that we noticed about the discourse around feminism within the manosphere was the lack of attention to intersectionality - the different (sometimes contradictory) perspectives, experiences and goals within the feminist community. In fact feminism was grounded on rejecting essentialism in defining what it means to be a woman. Likewise, feminists question essentialism in what it means to be a man. Their lack of attention to those differences may have contributed to the belief that feminists are contradicting themselves, avoiding or rejecting the alternative explanation that feminists might feel differently on certain issues and advocate different solutions to different problems. To help improve transparency in our OWN research on the manosphere and to explore the differences between communities, we decided to pick apart what is meant with the term misogyny and examine each of those aspects across several communities on Reddit.
- In the paper we talk about some ethnographic studies that were conducted by researchers Kendall and Lin, who spent a considerable amount of time engaging with the communities they were studying, talking to members, being a part of online discussion.
- We chose a a range of communities that are speaking about incels, ranging from more mainstream subreddits like MGTOW, to self-proclaimed non-violent incels, to groups with more extreme language. We also included two groups that are anti-incel, to be able to compare their language use with that of more extreme communities. Finally, we included two subreddits as controls, one that generally uses sexually explicit terms and discusses women’s anatomy (would be likely to use the same sexualised terms we would expect to find in other groups) and a group of self-reported women incels, who would be likely speaking about some of the same injustices as their male peers without explicitly misogynistic language