Динаміка емоційності під час повномасштабного вторгнення у телеграм-каналах професійних та аматорських медіа
Bibliographic record
Abstract
In Ukraine, Telegram channels have become a platform for urgent news and alerts related to shelling, air strikes, government decisions and achievements of the Ukrainian armed forces. The posts of these media regardless of the topic contain one or another emotional reaction (emoji). This study tracked the dynamics of the use of these emotional responses over the two years since the start of Russian full-scale invasion. Telegram channels of various types were taken for analysis: "Hromadske", "Toronto Television" and "Ukraina Seychas". Programs written in Python were used to download, process posts, and search for emoji. This made it possible to detect all the reactions present in the posts. To identify the types of emotions, Emojipedia was used, where reactions with corresponding values were searched for by the keywords "sadness", "joy", etc. It was established that negative emotions (anger and sadness) were more popular for telegram channels of professional mass media. Instead, for the anonymous Telegram channel, positive (love and joy) dominated. In addition, it was determined that some emotions remained stable: pride, joy, gratitude for Hromadske, joy for Toronto Television; pride is for Ukraine Now. We also observed that over time the positive emotion changed to a negative one, which, of course, can be explained by the course of events during the full-scale invasion, sadness, fatigue from the events taking place at the front lines, in the country and in the world. It was also found that the environment of Telegram channels is changing the way in which news was previously created: emotions are becoming an important component of it. And although researchers and practitioners in the field of media have yet to establish to what extent the emotional reactions in the news during a full-scale invasion meet professional standards, it is noted that in general the reactions allow, firstly, to see the difference in the approaches to their expression by professional and amateur media, and secondly, to reproduce certain emotional messages for their audiences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".