Armenian Post-War News Media Analysis with Topic and Emotional Coding

A mixed-methods study combines structural topic modeling, reflexive emotional coding, and focus groups to analyze the Armenian post-war news ecosystem. The findings reveal a news landscape saturated with security concerns, dominated by war and geopolitics themes, and characterized by emotional flatness and withdrawal among readers. This dataset, authored by Sona Manusyan and shared under a CC-BY-4.0 license, provides the textual and qualitative analysis underpinning these conclusions.

Use Cases

• Study thematic patterns in post-conflict news coverage based on structural topic modeling mentioned in the description
• Analyze the emotional resonance of news content on readers based on reflexive emotional coding
• Investigate citizen strategies for navigating overwhelming media environments based on focus group findings
• Model the relationship between media saturation and civic (dis)engagement based on the integrated computational and qualitative approach

Strengths

• Employs a mixed-methods approach combining computational analysis with qualitative focus groups
• Focuses on a specific and understudied geopolitical context: the Armenian post-war news ecosystem
• Released under a permissive CC-BY-4.0 license for reuse

Limitations

• The dataset is a 705.7 KB DOCX file, indicating a limited textual scope
• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

figshare

Collection Method

Mixed-methods study combining computational text analysis (structural topic modeling), reflexive emotional coding, and focus groups.

Freshness

Last updated 2026-05-29 06:12:49

Geography

Armenia