Mihaela Colhon

The rapid technological advancement has led to an increased amount of content and information which needs to be reliably analyzed and correlated in order to make accurate inferences. Data quality is a stringent issue when considering gaps or missing information and various imputation methods, algorithms or statistical techniques have been created to correct missing data. To this purpose we propose an imputation method using the Sliding Window Technique to help infer missing data from datasets, a method that is efficient in terms of accuracy and complexity. In order to illustrate the method, we present a case study that simulates a scenario in which a half-yearly dataset must be analyzed in relation to annual datasets. The obtained results are passed to a statistical descriptive analysis and the conclusions derived from the performed analysis suggest that the estimations are homogeneous enough.

The rapid proliferation of social media platforms has led to a remarkable increase of fake news dissemination, creating a significant challenge for the integrity of online information. To analyze this issue, this study aims to investigate the relationship between user characteristics and behaviors on social media platforms as a prequel for the development of effective fake news detection strategies. For that purpose, we examined open access datasets available for Romanian internet users and selected several internet usage activities which we analyzed in relation to key demographic characteristics that lead to establishing user profiles prone to manipulations. By understanding the underlying factors that contribute to the spread of fake news, we can design targeted interventions to mitigate its impact and promote a more informed and responsible online environment.

Summary: The issue of whether or not word sense disambiguation (WSD) can improve information retrieval (IR) results has been intensely debated over the years, with many inconclusive or contradictory results and a majority of skeptical opinions. All three classes of WSD methods (supervised, unsupervised, and knowledge‐based) have been considered by the literature with respect to IR. We hereby survey the unsupervised approach which, although relatively rarely used, has provided positive results at a large scale. Unsupervised WSD has already made proof of its utility in IR and it is our belief that it still holds a promise for this field. The two main existing types of unsupervised methods for IR, which are of completely different natures, are presented, within the scientific context in which they were born, and are compared. Regardless of the gap in time between these central approaches, we are of the opinion that the unsupervised solution to the discussed problem remains the most significant for IR applications. By surveying what we consider the most promising existing approach to usage of WSD in IR, and by discussing its possible extensions, we hope to stimulate continuation of this line of research, possibly at an even more successful level.

In this paper, a special type of polysemantic words, that is, words with multiple meanings for the same part of speech, are analyzed under the name of neutrosophic words. These words represent the most difficult cases for the disambiguation algorithms as they represent the most ambiguous natural language utterances. For approximate their meanings, we developed a semantic representation framework made by means of concepts from neutrosophic theory and entropy measure in which we incorporate sense related data. We show the advantages of the proposed framework in a sentiment classification task.