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Understanding Factors driving Extremism on Social Media

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Understanding Factors driving Extremism on Social Media

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Presentation to South Hub: Social Cybersecurity WG

Related paper:
Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate. Proc. of The 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2019).
https://dl.acm.org/doi/pdf/10.1145/3359253

Presentation to South Hub: Social Cybersecurity WG

Related paper:
Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate. Proc. of The 22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2019).
https://dl.acm.org/doi/pdf/10.1145/3359253

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Understanding Factors driving Extremism on Social Media

  1. 1. Understanding factors driving Extremism on Social Media Icons by thenounproject Slides by SlideModel Ugur Kursuncu, Amit Sheth AI Institute, University of South Carolina Team: Ugur Kursuncu, Manas Gaur, JM Berger, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth South Big Data Hub, Social Cyber Security Working Group April 2, 2020 Artificial Intelligence Institute
  2. 2. 2 ● Efforts by online platforms are inadequate. ● Governments insist that the industry has a ‘social responsibility’ to do more to remove harmful content. ● If unsolved, social media platforms will continue to negatively impact the society. Online Extremism - Ongoing Open Problem
  3. 3. 3 Online Extremism - Covid 19
  4. 4. 4 ● 1000 Americans between 1980 and 2011 (including 300 Americans since 2011) have attempted to travel or traveled. ● > 5000 individuals from Europe have traveled to Join Extremist Terrorist Groups (ISIS, Al-Qaeda) abroad through 2015, ● Most inspired and persuaded online. *George Washington University, Program on Extremism “The Travelers”
  5. 5. ● 24 year old college student from Alabama became radicalized on Twitter. After a year, moved to Syria to join ISIS. ● Self-taught, she read verses from the Qur’an, but interpreted them with others in the extremist network. ● Persuaded that when the true Islamic State is declared, it is obligatory to do hijrah, which they see as the pilgrimage to ‘the State’. 5 *New York Times: “Alabama Woman Who Joined ISIS Can’t Return Home, U.S. Says” Illustrative Case
  6. 6. (e.g., recruiter, follower) with respect to different stages of radicalization. Modeling users content and psychological process over time. Persuasive relevant to Islamist extremism. Domain Knowledge of the context (“jihad” has different meaning in different context) Multidimensionality Radicalization Challenges & Potential Solutions
  7. 7. 7 0 None Mainstream religious views and orientations Indicator: Islam; Allah; jihad (self struggle); halal; democracy, islam, salah, fatwa, hajj. 1 Low Attitudinal support for politically moderate Islamism Indicator: Hadith; Caliphate (Khilafah) justified; Sharia better (than secular law); Hypocrisy west. 2 Elevated Emergent support for exclusive rule of the Shari’a law Indicator: Shariah best; revenge (justified); jihad (against West); justify Daesh (ISIS) 3 High Support for extremist networks and travel to “Darul Islam” Indicator: Kafir; infidel; hijrah to Darul- Islam; (supporting) fatwa Al- Awlaki; mushrikeen. 4 Severe Call for action to join the fight and the use of violence. Indicator: apostate; sahwat; taghut; kill; kafir; kuffar; murtadd; tawaghit; al_baghdadi; martyrdom khilafah Radicalization Scale (Dilshod Achilov et al.)
  8. 8. 8 Analysis of content in context can provide deeper understanding of the factors characterizing the radicalization process. Non-extremist ordinary individual Radicalized extremist individual 0 1 2 4 SevereHighLowNone Elevated 3 Radicalization Process over time (Dilshod Achilov et al.)
  9. 9. Incorrect classification of non-extremist as extremist can be harmful. False alarm might potentially impact millions of innocent people. 9 Local and Global security implications - Need for reliable prediction of online terrorist activities. Cautionary Note
  10. 10. ● Verified and suspended by Twitter. ● Time frame: Oct 2010 – Aug 2017 ● Includes 538 extremist users, from two resources. (Fernandez, 2018) (Ferrara, 2016) ○ Twitter verified users by anti-abuse team. ○ Lucky Troll Club ● 538 Non-extremist users were created from an annotated muslim religious dataset that contains Muslim users. (Chen, 2014) -Miriam Fernandez, Moizzah Asif, and Harith Alani. 2018. Understanding the roots of radicalisation on twitter. In Proceedings of the 10th ACM Conference on Web Science. -Emilio Ferrara, Wen-Qiang Wang, Onur Varol, Alessandro Flammini, and Aram Galstyan. 2016. Predicting online extremism, content adopters, and interaction reciprocity. In International conference on social informatics. -Chen, L., Weber, I., & Okulicz-Kozaryn, A. (2014, November). US religious landscape on Twitter. In International Conference on Social Informatics (pp. 544-560). Springer, Cham. Dataset
  11. 11. 11 Prevalent Key Phrases Prevalent Topics isis, syria, kill, iraq, muslim, allah, attack, break, aleppo, assad, islamicstate, army, soldier, cynthiastruth, islam, support, mosul, libya, rebel, destroy, airstrike Caliphate_news, islamic_state, iraq_army, soldier_kill, iraqi_army, syria_isis, syria_iraq, assad_army, terror_group, shia_militia, isis_attack, aleppo_syria, martyrdom_operation, ahrar_sham, assad_regime, follow_support, lead_coalition, turkey_army, isis_claim, kill_isis Imam_anwar_awlaki, video_message_islamicstate, fight_islamic_state, isisclaim_responsibility_attack, muwahideen_powerful_middleeast, isis_tikrit_tikritop, amaqagency_islamicstate_fighter, sinai_explosion_target, alone_state_fighter, intelligence_reportedly_kill, khilafahnew_islamic_state, yemanqaida_commander_kill, isis_militant_hasakah, breakingnew_assad_army, isis_explode_middle, hater_trier_haleemah, trust_isis_tighten, qamishlus_isis_fighting, defeat_enemy_allah, kill_terrorist_baby, ahrar_sham_leader islamic state, syria, isis, kill, allah, video, minute propaganda video scenes, jaish islam release, restock missile, kaffir, join isis, aftermath, mercy, martyrdom operation syrian opposition, punish libya isis, syria assad, islam sunni, swat, lose head, wilayatalfurat, somali, child kill, takfir, jaish fateh, baghdad, iraq, kashmir muslim, capture, damascus, report rebel, british, qala moon, jannat, isis capture, border cross, aleppo, iranian soldier, tikrit tikrittop, lead shia military kill, saleh abdeslam refuse cooperate Green: Religion Blue: Ideology Red: Hate Corpus: 538 verified extremists Extremist Content
  12. 12. 12 ● Dimensions to define the context: ○ Based on literature and our empirical study of the data, three contextual dimensions are identified: Religion, Ideology, Hate ● The distribution of prevalent terms (i.e., words, phrases, concepts) in each dimension is different. ● Different dimensions needed to contextualize and disambiguate common ‘diagnostic’ terms (e.g., jihad). Contexts to study Extremist Content
  13. 13. 13 “Reportedly, a number of apostates were killed in the process. Just because they like it I guess.. #SpringJihad #CountrysideCleanup” “Kindness is a language which the blind can see and the deaf can hear #MyJihad be kind always” “By the Lord of Muhammad (blessings and peace be upon him) The nation of Jihad and martyrdom can never be “Jihad” can appear in tweets with different meanings in different dimensions of the context. H I R Example Tweets with “Jihad”
  14. 14. 14 ● Same term can have different meanings for each dimensions. ● Example: “Meaning of Jihad” is different for extremists and non- extremists. ○ For extremists, meaning closer to “awlaki”, “islamic state”, “aqeedah” ○ For non-extremists, closer to “muslims”, “quran”, “imams” ExtremistsNon-Extremists Ambiguity of Diagnostic terms/phrases
  15. 15. ● Different Contextual Dimensions incorporating: ○ Dimension-specific Corpora ○ Verified by Domain Expert ● Domain Specific Corpora creation: Religion: Qur’an, Hadith Ideology: Books, lectures of ideologues Hate: Hate Speech Corpus (Davidson, 2017) ● Can be applied over many social problems. 15 W2V Islamic Corpus Religious Dimension (R) R R R Contextual Dimension Modeling is a one-time learning process User Contextual Dimension based Representation W2V Ideologic al Corpus W2V Hate Corpus (...) I I I (...) H H H (...) Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017, May). Automated hate speech detection and the problem of offensive language. In Eleventh international aaai conference on web and social media. Contextual Dimension Modeling
  16. 16. Capturing similarity (and resolving ambiguity): ● Learning word similarities from a large corpora. ● A solution via distributional similarity-based representations. 16(Hate) User Representations “You shall know a word by the company it keeps” (J. R. Firth 1957: 11)
  17. 17. ● For religion: Extremist and non-extremist users are significantly similar to each other. ● For hate: Extremist and non-extremist users do not show much similarity. Religion Ideology NonExtremists Extremists 17 Religion Ideology Hate User Similarity
  18. 18. ● For religion and hate, among extremists: There seems to be a number of users that are significantly different from each other. ● Possibility of outliers. Extremists Extremists 18 Religion Ideology Hate User Similarity
  19. 19. ● A group of extremist users, form a cluster farther from other users for Religion and Hate. ● Suggesting there might be outliers in the dataset. 19 User Visualization for Dimensions
  20. 20. ● Randomly selected 10 users and visualize for each dimension. ● Repeated this selection many times, every time same users formed a separate cluster. In this case below, the users are D, A. 20 Random 10 Users User Visualization for Dimensions
  21. 21. ● Identified 99 (18%), 48 (9%) and 141 (26%) users in the extremist dataset, clustered as likely outliers for religion, ideology and hate, respectively. ● A random sample of 76 users (15% ) from the extremist dataset, to validate the identified potential likely outliers. ● Our domain expert annotated these users as likely extremist, likely extremist and unclear. Kappa Score = 82% Separation of users within the extremist dataset through clustering Mann-Whitney U-test Outlier Detection
  22. 22. ● Obtained the set of 49 outlier users in the extremist dataset. Rest is labeled as likely extremists ● Content of the outlier users contains the following prevalent concepts: marriage, Allah, bonded, silence, Islam leaders, Berjaya hilarious, cake, miss mit, kemaren, Quran, Khuda, prophet, Muhammad, Ahmad. Separation of users within the extremist dataset through clustering Outliers
  23. 23. ● Identified 148 users who had relatively sparse contextual content for at least one of the three dimensions. ● Based on the topical similarity of user content. ● Training two LDA models, one for the extremist dataset and another for non- extremist dataset. ● The ratio of intersection topics ( )over the union of the topics between sparse and dense representations ( ) for each dimension ( ). Imputation for Sparse Representations
  24. 24. 24 ● Tri-dimension model performs best. Imputation improves performance. ● Precision used as metric, to emphasize reduction on misclassification of non-extremist content. ● Precision for RIH and Recall for RH. ● Implications in a large scale application. Results
  25. 25. ● Domain Specific Knowledge plays critical role and importance of ground truth for such complex problems. ● False alarms: significantly reduced via incorporation of three domain specific dimensions. It further reduces the likelihood of an unfair mistreatment towards non-extremist individuals, in a potential real world deployment. ● Misclassification of non-extremist users can have significant implications in a large-scale application where non-extremists vastly outnumber extremists. ● Higher precision reduces potential social discrimination. 25 Key Insights
  26. 26. ● Extremist users employ religion along with hate, suggesting they employ different hate tactics for their targets. ● Each dimension plays different roles in different levels of radicalization, capturing nuances as well as linguistic and semantic cues better throughout the radicalization process. 26 Key Insights
  27. 27. 27 Questions? @UgurKursuncu Supporting Grants: NSF Award#: CNS 1513721 --Context-Aware Harassment Detection on Social Media (wiki link) is currently an interdisciplinary project at AI Institute at the University of South Carolina, formerly at the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), the Department of Psychology, and Center for Urban and Public Affairs (CUPA) at Wright State University. Carlos Castillo was supported by La Caixa (LCF) project (LCF/PR/PR16/11110009) D. Achilov was supported in part by the Universityof Notre Dame (UND) Global Religion Research Initiative (GRRI) grant through Templeton ReligionTrust (Grant ID: TRT0118). Thank You!

Editor's Notes

  • How the recruiters able to recruit others to join their extreme causes. What tools and tactics they are using.
    What are the language, cultural, emotional characteristics, that enable them to recruit others.
  • Motivation slide.
    Investing insuch efforts are expensive.
    Social responsibility.
    First amendment.
    Though continue impact society.
  • Religion may play more role first, hate later
    Cite Achilov
  • These implications may reduce trust in the system.
  • Sahwat: any sunni muslim that opposes the extremist group.
  • The surrounding words will represent the words in bold and italic.
  • Start motivating outlier detection from this slide.
  • Talk more about significance of outliers.
  • Give more insights from this slide. Implications related to these outliers.

    Validation Kappa: 69 correct and 7 incorrect matches

    non-parametric Mann-Whitney U-test for each contextual dimension.
    This outcome suggests that the variance between the
    content of the two clusters of users based on each dimension is high; hence,
    the users in these two clusters are significantly different from each other.
  • Connect to social media companies. Why they are more passive than active since it ...
    Validation Kappa: 69 correct and 7 incorrect matches

  • Why sparse. Connect to different stages of radicalization.
  • Why use precision? How 1% misclassification would translate into a big number of people being affected.
  • ×