This study presents a novel corpus of 15,356 Polish web articles, including articles identified as containing disinformation.Our dataset enables a multifaceted understanding of disinformation.We present a distinctive multilayered methodology for annotating disinformation in texts.What sets our corpus apart is its focus on uncovering hidden intent and manipulation in disinformative content.A team of experts annotated each article with multiple labels indicating both disinformation creators' intents and the manipulation techniques employed.Additionally, we set new baselines for binary disinformation detection and two multiclass multilabel classification tasks: manipulation techniques and intention types classification.

Fighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts' time. We also equip them with tools for semi-automatic initial verification of the credibility of the annotated content. We introduce a general framework for filtering medical statements that do not require manual evaluation by medical experts, thus focusing annotation efforts on non-credible medical statements. Our framework is based on the construction of filtering classifiers adapted to narrow thematic categories. This allows medical experts to fact-check and identify over two times more non-credible medical statements in a given time interval without applying any changes to the annotation flow. We verify our results across a broad spectrum of medical topic areas. We perform quantitative, as well as exploratory analysis on our output data. We also point out how those filtering classifiers can be modified to provide experts with different types of feedback without any loss of performance.

Citation analysis does not tell the whole story about the innovativeness of scientific papers. Works by prominent authors tend to receive disproportionately many citations, while publications by less well-known researchers covering the same topics may not attract as much attention. In this paper we address the shortcomings of traditional scientometric approaches by proposing a novel method that utilizes a classifier for predicting publication years based on latent topic distributions. We then calculate real-number innovation scores used to identify potential breakthrough papers and turnaround years. The proposed approach can complement existing citation-based measures of article importance and author contribution analysis; it opens as well novel research direction for time-based, innovation-centered research scientific output evaluation. In our experiments, we focus on two corpora of research papers published over several decades at two well-established conferences: The World Wide Web Conference (WWW) and the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), containing around 3500 documents in total. We indicate significant years and demonstrate examples of highly-ranked papers, thus providing a novel insight on the evolution of the two conferences. Finally, we compare our results to citation analysis and discuss how our approach may complement traditional scientometrics.

Irony is something most people can tell is therewhen they see it, but it is not so easy to define, let alone detectautomatically. In this paper we describe the construction of abalanced corpus of ironic vs. serious watch reviews and show thepromising results achieved by classifiers trained on this corpusin predicting the presence of irony or lack thereof in productreviews from a manually labeled corpus. We try to find commonfeatures in the two corpora and outline our next steps towardsa model which would detect ironic utterances in more general contexts.