Irina Ovchinnikova


pdf bib
Sentiments in Russian Medical Professional Discourse during the Covid-19 Pandemic
Irina Ovchinnikova | Liana Ermakova | Diana Nurbakova
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media

Medical discourse within the professional community has undeservingly received very sparse researchers’ attention. Medical professional discourse exists offline and online. We carried out sentiment analysis on titles and text descriptions of materials published on the Russian portal Mir Vracha (90,000 word forms approximately). The texts were generated by and for physicians. The materials include personal narratives describing participants’ professional experience, participants’ opinions about pandemic news and events in the professional sphere, and Russian reviews and discussion of papers published in international journals in English. We present the first results and discussion of the sentiment analysis of Russian online medical discourse. Based on the results of sentiment analysis and discourse analysis, we described the emotions expressed in the forum and the linguistic means the forum participants used to verbalise their attitudes and emotions while discussing the Covid-19 pandemic. The results showed prevalence of neutral texts in the publications since the medical professionals are interested in research materials and outcomes. In the discussions and personal narratives, the forum participants expressed negative sentiments by colloquial words and figurative language.

pdf bib
Covid or not Covid? Topic Shift in Information Cascades on Twitter
Liana Ermakova | Diana Nurbakova | Irina Ovchinnikova
Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)

Social media have become a valuable source of information. However, its power to shape public opinion can be dangerous, especially in the case of misinformation. The existing studies on misinformation detection hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message is quoted or receives a reply. We show a significant topic shift in information cascades on Twitter during the Covid-19 pandemic providing valuable insights for the automatic analysis of information distortion.


pdf bib
Comparative Analysis of Errors in MT Output and Computer-assisted Translation: Effect of the Human Factor
Irina Ovchinnikova | Daria Morozova
Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks