From Mountains to Social Media Valleys: A Thematic Analysis of Information Warfare through Telegram Data in the Nagorno-Karabakh War
Bibliographic record
Abstract
The emergence of information warfare (IW) has brought about a revolution in the realm of military affairs. Existing research has already demonstrated how successfully weaponized information can be effectively used against an adversary with the most impressive military gear like never seen before. Yet, with the ever-evolving field of information and communication technologies, the scientific community still lacks a comprehensive understanding about IW, especially in the field of social media/instant messaging (SM/IM) information dissemination platforms. The aim of this research project is to further the knowledge about IW as executed through SM/IM media, specifically in the context of the long-standing Nagorno-Karabakh war. Using Reflexive Thematic Analysis, the present study examined over 8000 individual news posts in two influential Telegram channels pertaining to the conflict. The resulting main themes were Historical, Political and Economic factors, Emotional Provocation and The Blame Game, all consistent with patterns observed in both traditional and contemporary media. The impact of the said themes on the behavioural and belief outcomes of the consumers, as well as the subsequent course of the conflict remain a subject for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".