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VOX-Pol Guest Lecture Series, Spring 2024: Studying Right-Wing Extremism

Speaker: Sam Jackson is an Assistant Professor in the College of Emergency Preparedness, Homeland Security, and Cybersecurity at the University at Albany. His research focuses on right-wing extremism in the United States; extremism online; social media, activism, and conflict; and computational social science.

Presented by Seán Looney

In this case study, I analyze the 2014 North Korean computer database hack of Sony Pictures Entertainment (SPE), a serious national security crisis of cyberterrorism. I utilize Normal Accidents theory as a lens, to help explain how the accident within one system (SPE) and later crisis lead to the interaction with a second system (U.S. Federal Government), the development of a new crisis, and the need for a crisis response from system two. The evolution of a single organization’s accident into a national security crisis does not occur without specific complex interactions that take place to connect the two systems together. To explain this interconnectedness between systems, I introduce two new constructs: 1) common denominator and 2) common goal, which expand Normal Accidents theory allowing it to account for the coupling between the two independent systems (SPE & United States Government) through non-linear interactions. Overall, this case study provides important insight for future crisis communication planning, response, and development regarding between-organization interaction during a crisis.

Part 1 of a panel discussion of February 2012 report by British House of Commons describing the internet as ‘a fertile breeding ground for terrorism’. Originally uploaded by 1IslamChannel on 28 March 2012.

The advancement in technology has brought a new era in terrorism where Online Social Networks (OSNs) have become a major platform of communication with wide range of usage from message channeling to propaganda and recruitment of new followers in terrorist groups. Meanwhile, during the terrorist attacks people use OSNs for information exchange, mobilizing and uniting and raising money for the victims. This paper critically analyses the specific usage of OSNs in the times of terrorisms attacks in developing countries. We crawled and used Twitter’s data during Westgate shopping mall terrorist attack in Nairobi, Kenya. We then analysed the number of tweets, geo-location of tweets, demographics of the users and whether users in developing countries tend to tweet, retweet or reply during the event of a terrorist attack. We define new metrics (reach and impression of the tweet) and present the models for calculating them. The study findings show that, users from developing countries tend to tweet more at the first and critical times of the terrorist occurrence. Moreover, large number of tweets originated from the attacked country (Kenya) with 73% from men and 23% from women where original posts had a most number of tweets followed by replies and retweets.

Since the 2016 US Presidential Election, extreme right-wing communities have gained extensive popularity on YouTube, spreading discourses of white supremacy and conspiracy. This paper focuses on how methods drawn from Systemic Functional Linguistics (SFL) can be used to analyze this communication and contribute to research interests within the field of media and communication studies. SFL is a social semiotic model of language concerned with systematic analysis of language choices in terms of their social context. More specifically, this paper draws upon the Appraisal and Affiliation frameworks developed within SFL, in order to understand how patterns of evaluation are expressed in language and how these function in terms of aligning ambient audiences with particular values. YouTube videos and comments about the Notre Dame Cathedral Fire are used as a case study. The aim is to illustrate how this approach can offer an additional perspective on the issues of information disorder and hate speech that does not attempt to homogenize the multiple reasons why people engage in such hateful behavior.