A matter of words: NLP for quality evaluation of Wikipedia medical articles

Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles. In particular, we evaluate the articles adopting an "actionable" model, whose features are related to the content of the articles, so that the model can also directly suggest strategies for improving a given article quality. We rely on Natural Language Processing (NLP) and dictionaries-based techniques in order to extract the bio-medical concepts in a text. We prove the effectiveness of our approach by classifying the medical articles of the Wikipedia Medicine Portal, which have been previously manually labeled by the Wiki Project team. The results of our experiments confirm that, by considering domain-oriented features, it is possible to obtain sensible improvements with respect to existing solutions, mainly for those articles that other approaches have less correctly classified. Other than being interesting by their own, the results call for further research in the area of domain specific features suitable for Web data quality assessment

Words worth attention: predicting words of the week on the Russian Wiktionary

Such collaborative lexicography projects as Wiktionary are becoming strong competitors for traditional semantic resources just as Wikipedia has already become for expert-built knowledge bases. Keeping the data obtained from the general public crowd in good quality is a very challenging problem because of the fuzzy nature of the crowdsourcing phenomena. The presented study focuses on predicting the word of the week articles on the Russian Wiktionary by treating this problem as a binary classification task. The best proposed model is based on the Naïve Bayes classifier and has weighted average precision, recall, and F1-measure values of 87% by evaluating on the provided dataset

Hidden populations: discovering the differences between the known and the unknown drug using populations in the Republic of Georgia

Abstract Background The HIV epidemic in Georgia is increasing. Data shows that compared to previous years, Georgia has increasingly more HIV-infected individuals than previous assessments. Select client groups remain hard to reach by harm reduction programs. The need for innovative strategies to involve these individuals is imperative. Methods The following study examines demographics and risk factors of participants, previously known and not known to harm reduction services, for HIV and other infectious disease in towns across Georgia in 2015 and compares risk among different groups, while also assessing the rationale for implementing Peer-Driven Interventions in Georgian Harm Reduction activities. Important differences in demographics and risk profile are thought to exist between those exposed, and those unexposed, to harm reduction activity. Results Important and striking differences between previously known and unknown participants, including demographic background and risk profile and behaviours exist in the drug using community. These differences can potentially explain some of the rise of HIV prevalence in Georgia. Conclusion Significant differences exist between known and unknown drug users in Georgia, the differences between which are crucial for planning future and holistic harm reduction activities in Georgia, regionally and globally. The research advocates for smarter harm reduction activity, adds to the global evidence for the utility of Peer-Driven Intervention, and encourages sustained global effort for reduction of blood-borne disease burden globally