Since the end of the Cold War, the protection of civilians has increased its weight on the United Nations (UN) agenda. This article (1) maps the evolution of civilian protection within the UN framework as an indicator of its shifting priorities, (2) identifies breakpoints in the prevalence and character of the protection discourse, and (3) explores how internal processes of policy development and real-world triggers (namely conflicts and peacekeeping operations) shaped this transformation. The article uses Structural Topic Modeling (STM) to analyze an original corpus of Security Council and General Assembly resolutions on complex humanitarian emergencies since 1990. The analysis uncovers two distinct forms of protection, labeled as “Ground protection” and “Political–legal protection,” which are characterized by contrasting temporal and geographic trajectories. Moreover, critical junctures in the protection rhetoric (during the years 2000, 2005, and 2008) coincide with policy watersheds rather than conflict outbreaks or trends in peacekeeping deployment. This article offers a comprehensive analysis of the intricate evolution of civilian protection using text-as-data methods, which uses its theory-building design to encourage further explorations on the interplay between internal and external factors in shaping its progression within the UN framework.
This paper explores the behaviour of malicious hacker groups operating in cyberspace and how they organize themselves in structured networks. To better understand these groups, the paper uses Social Network Analysis (SNA) to analyse the interactions and relationships among several malicious hacker groups. The study uses a tested dataset as its primary source, providing an empirical analysis of the cooperative behaviours exhibited by these groups. The study found that malicious hacker groups tend to form close-knit networks where they consult, coordinate with, and assist each other in carrying out their attacks. The study also identified a "small world" phenomenon within the population of malicious actors, which suggests that these groups establish interconnected relationships to facilitate their malicious operations. The small world phenomenon indicates that the actor-groups are densely connected, but they also have a small number of connections to other groups, allowing for efficient communication and coordination of their activities.
Abstract
Taking into account YouTube’s specific role in the Russian media system and the increasing level of political polarization in the country, this study examines the role of incivility in discussions and whether discussions in an anti-government community represent a place for disagreement between pro-opposition and pro-government users. I argue that an online environment helps these sides meet each other rather than creating echo chambers of like-minded users. Moreover, in the quite restrictive Russian context for political deliberation, the incivility of messages plays a role in further involving commenters in discussions. Using the corpus of comments posted in the discussion section of opposition leader Alexei Navalny’s YouTube channel, I exploited class affinity modeling to identify pro-government and pro-opposition stances. Incivility was studied based on Google’s Perspective API toxicity classifier. I found that users avoid extreme forms of incivility when interacting with other commenters, but uncivil comments are more likely to start discussion threads. Furthermore, the level of incivility in comments gets higher over time after a video release. Pro-government sentiments, on the one hand, are associated with a subsequent response from Navalny’s supporters to the out-group criticism and, on the other hand, contribute to the further formation of hubs with a pro-government narrative. This research contributes to the extant literature on affective polarization on social media, shedding light on political discussions within an oppositional community in a non-democracy.
DOI: https://doi.org/10.5117/CCR2023.1.7.ZINN
Original article and full dataset
This compressed file (.zip) includes all the datasets (as Excel files) used in this paper. Everybody is welcome to download and use it but please acknowledge the source.
Link to the article
