Navigating the fog of war during the Russia’s invasion of Ukraine: An exploratory network analysis of tweets about an alleged chemical attack in Mariupol
Bibliographic record
Abstract
As part of our ongoing research on misinformation and disinformation of various types, we conducted an exploratory analysis of tweets discussing an unverified report that Russian forces engaged in a chemical attack in Mariupol, Ukraine. This claim was made on April 11 by Ukraine’s Azov regiment. At the time when this claim was first reported, Mariupol was surrounded by Russian troops, making it difficult, if not nearly impossible, for journalists to gain access to the city and to interview local sources. We were interested in examining how this claim was discussed on social media because if it was true, it had the potential to galvanize the world’s sentiments in support of Ukraine and against Russia. Using Twitter’s Academic Track API, we retroactively collected 246,189 public tweets posted between April 6 and 13, 2022 to analyze how Twitter users were discussing this claim. We collected tweets related to this case a few days before and after April 11 to capture speculation before the accusation, and the reaction to it. We used the search query “chemical (weapons OR weapon) (Mariupol OR Ukraine)” to collect data. (For data completeness, we kept 12,193 tweets referenced by one of the tweets in the search results.)
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".