The massive shock of the COVID-19 pandemic has already shown its negative effects on economies around the world, unprecedented in recent history. COVID-19 infections and containment measures caused a general slowdown in research and new knowledge production. Because of the link between R&D output and economic growth, it is to be expected then that a slowdown in research activities will slow in turn the global recovery from the pandemic. Many recent studies also claim an uneven impact on scientific production across gender. In this paper, we investigate the phenomenon across countries, analysing preprint depositions in main repositories. Differently from other works, that compare the number of preprint depositions before and after the pandemic outbreak, we analyse the depositions trends across geographical areas, and contrast after-pandemic outbreak depositions with expected ones. Differently from common belief and initial evidence, the decrease in research output is not more severe for women than for men.

Tracking public opinion in social media provides important information to enterprises or governments during a decision making process. In addition, identifying and extracting the causes of sentiment spikes allows interested parties to redesign and adjust strategies with the aim to attract more positive sentiments. In this paper, we focus on the problem of tracking sentiment towards different entities, detecting sentiment spikes and on the problem of extracting and ranking the causes of a sentiment spike. Our approach combines LDA topic model with Relative Entropy. The former is used for extracting the topics discussed in the time window before the sentiment spike. The latter allows to rank the detected topics based on their contribution to the sentiment spike. To evaluate our methodology, we built a collection of tweets which are grouped by topics and labeled based on the polarity and strength of the sentiment they express.

In this paper, we address the problem of answering complex questions formulated by users in natural language. Since traditional information retrieval systems are not suitable for complex questions, these questions are usually run over knowledge bases, such as Wikidata or DBpedia. We propose a semi-automatic approach for transforming a natural language question into a SPARQL query that can be easily processed over a knowledge base. The approach applies classification techniques to associate a natural language question with a proper query template from a set of predefined templates. The nature of our approach is semi-automatic as the query templates are manually written by human assessors, who are the experts of the knowledge bases, whereas the classification and query processing steps are completely automatic. Our experiments on the large-scale CSQA dataset for question-answering corroborate the effectiveness of our approach.

We design algorithms that, given a collection of documents and a distribution over user queries, return a small subset of the document collection in such a way that we can efficiently provide high-quality answers to user queries using only the selected subset. This approach has applications when space is a constraint or when the query-processing time increases significantly with the size of the collection. We study our algorithms through the lens of stochastic analysis and prove that even though they use only a small fraction of the entire collection, they can provide answers to most user queries, achieving a performance close to the optimal. To complement our theoretical findings, we experimentally show the versatility of our approach by considering two important cases in the context of Web search. In the first case, we favor the retrieval of documents that are relevant to the query, whereas in the second case we aim for document diversification. Both the theoretical and the experimental analysis provide strong evidence of the potential value of query covering in diverse application scenarios.