Digital battleground: An examination of anti-refugee discourse on Twitter against Ukrainians displaced by Russia’s invasion of Ukraine
Why this work is in the frame
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Bibliographic record
Abstract
Russia’s war of aggression in Ukraine has triggered Europe’s largest refugee crisis since World War II. In this case study, we investigate the prevalence and types of anti-refugee discourse about Ukrainian refugees on Twitter. Previous studies primarily focused on public discourse and attitudes toward racialized refugees and immigrants; the Ukrainian refugee crisis is unique in that it is one of the few instances of a recent refugee crisis involving people who do not come from mostly racialized communities. Using Communalytic, a computational social science tool for studying public discourse on social media, we automatically collected and identified toxic posts mentioning Ukrainian refugees during the first year of Russia’s full-scale invasion of Ukraine. We focused on posts containing toxic language, as this is where we are most likely to find examples of anti-refugee sentiments. Based on a manual analysis of 2,045 toxic posts referencing Ukrainian refugees, the most prevalent ones were politically motivated and included partisan content (33 percent), followed by posts containing expressions countering anti-refugee narratives (20 percent). These findings highlight the escalating politicization and polarization of discussions about Ukrainian refugees both online and offline. Furthermore, 53 percent of the sample aligned with pro-Kremlin narratives against Ukraine. By exploiting anti-refugee sentiments and leveraging existing political and cultural fault lines in the West, pro-Kremlin messages on Twitter contribute to diminishing support for Ukrainian refugees, minimizing the severity of the war, and undermining international support for Ukraine.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it