Abstract: We conduct a series of experiments on ranking scientific abstracts in response to popular science queries issued by non-expert users. We show that standard IR ranking models optimized on topical relevance are indeed ignoring the individual user’s context and background knowledge. We also demonstrate the viability of complexity-aware retrieval models that retrieve more accessible relevant documents or ensure these are ranked prior to more advanced documents on the topic. More generally, our results help remove some of the barriers to consulting scientific literature by non-experts and hold the potential to promote science literacy in the general public. Lay Summary: In a world of misinformation and disinformation, access to objective evidence-based scientific information is crucial. The general public ignores scientific information due to its perceived complexity, resorting to shallow information on the web or in social media. We analyze the complexity of scientific texts retrieved for a lay person’s topic, and find a great variation in text complexity. A proof of concept complexity-aware search engine is able to retrieve both relevant and accessible scientific information for a layperson’s information need.

Le grand public a tendance à éviter les sources fiables telles que la littérature scientifique en raison de leur langage complexe et du manque de connaissances nécessaires. Au lieu de cela, il s’appuie sur des sources superficielles, trouvées sur internet ou dans les médias sociaux et qui sont pourtant souvent publiées pour des raisons commerciales ou politiques, plutôt que pour leur valeur informative. La simplification des textes peut-elle contribuer à supprimer certains de ces obstacles à l’accès ? Cet article présente l’action « CLEF 2023 SimpleText » qui aborde les défis techniques et d’évaluation de l’accès à l’information scientifique pour le grand public. Nous fournissons des données réutilisables et des critères de référence pour la simplification des textes scientifiques et encourageons les recherches visant à faciliter à la compréhension des textes complexes.

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.

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.