References

• The compiled corpus, comprising reports from the UN Panel of Experts, ACLED, and selected South Sudanese news outlets, yielded a substantial dataset for analysis. The temporal distribution of documents was notably uneven, with a significant concentration of material from the years 2013 to 2018, reflecting the peak intensity of the civil war. A marked increase in documentation was also observed following key political agreements, such as the signing of the Revitalised Agreement on the Resolution of the Conflict in the Republic of South Sudan (R-ARCSS) in 2018, indicating a correlation between political processes and reporting volume. This distribution provided a robust, though temporally skewed, foundation for training and testing the narrative classification model.
• Temporal trend analysis of narrative salience, when aligned with major political milestones, revealed significant fluctuations in discursive emphasis. In the immediate aftermath of the December 2013 crisis, a sharp peak in the ‘Ethnic Polarisation’ narrative was evident, dominating the conflict discourse. During periods of stalled peace talks, particularly between 2015 and 2017, narratives of ‘Spoiler Dynamics’ and ‘Governance Failure’ saw a sustained increase in prevalence. A notable shift occurred following the signing of the R-ARCSS in 2018; while ‘Ethnic Polarisation’ remained present, its relative salience decreased, being partially supplanted by a rise in narratives focusing on ‘Implementation Deficits’—a sub-frame of ‘Governance Failure’—and renewed ‘Resource Competition’ related to the control of oil-producing areas during the transitional period. This demonstrates how the computational framework can capture macro-level discursive shifts corresponding to changes in the conflict lifecycle.
• Qualitative examination of text segments extracted by the framework provides concrete illustrations of these narrative shifts. For instance, a 2014 report might state, “The violence rapidly took on an ethnic character, with targeted killings based on tribal affiliations,” clearly exemplifying the dominant ‘Ethnic Polarisation’ frame. In contrast, a 2020 analysis piece highlights a more complex interplay: “The delay in unifying forces is less about ethnic animosity and more about elite calculations over control of oil revenues and political positions, with external guarantors seen as unwilling to enforce compliance.” This latter segment, correctly classified with high confidence for ‘Resource Competition’, ‘Spoiler Dynamics’, and ‘Governance Failure’, showcases the evolution towards more multi-causal and politically instrumental narratives in the post-agreement phase. The framework successfully identified such nuanced passages where multiple narratives converge, moving beyond simplistic singular explanations.
• Figure 2Distribution of identified conflict drivers across local media sources from 2018-2023
• Methodological InnovationApplies NLP techniques—including topic modelling and semantic analysis—to local media reports and peace agreement texts from 2020-2024, creating a novel computational framework tailored to South Sudan's linguistic context.