ESR projects

Crowdsourcing is a model used for assigning micro-tasks to an open group of users through the internet. My research aims to understand the motivation and incentives of users participating in several forms of crowdsourcing in the field of multilingual information science. The main objective of my research is to explore behavioral characteristics to find any engagement patterns as well as potential challenges discouraging users’ participation.

Through this study I aim to analyse how to attract more users and how to encourage them to participate. In addition, one more aspect of this study is the use of multilingual knowledge bases to understand whether there are different incentives in cross-lingual information and how these connect with users’ contribution.

Fact validation is composed of the sub-tasks of parsing inputs, retrieving evidence, and inferring relations between the two. In related applications, approaches that compose a design – after breaking problems down into steps – are better suited to tasks with composite structure. Learning on multiple modalities introduces an additional level of complexity for monolithic architectures. This research aims to implement and evaluate systems optimised to process diverse data and adapt to the multiple sub-tasks comprising the fact validation pipeline.

The point of this project is to contribute to a larger understanding of the role of national media in the European Public Sphere as well as measuring the influence of the media in shaping the public’s image of the European Parliament.

Ideally, this project would also provide some pointers as to how the European Institutions should communicate with the electorate thus bridging the gap between these institutions and public opinion.

The main focus of this research is to develop methodology, techniques and tools for modeling the characteristics of information which is spreading from different sources across different barriers. As a part of the Cleopatra project, it will expand the existing research results and provide insight towards new areas of information spreading in the contemporary world.

In general, news bias is determined by studying linguistic attributes such as keywords and other syntactic and semantic features. But with the advent of machine learning in recent years, advanced algorithmic methods have been developed to determine bias in written news content. The majority of online written news content is available in English and even more importantly most of the tools and resources for text analysis are designed for English language. Consequently, most research work seeks to strengthen techniques for monolingual (mostly English) news bias analysis and there is less consideration of cross-lingual and cross-cultural bias in news reporting. The purpose of our research is therefore to develop methods, techniques and tools for analyzing bias in cross-lingual news reporting. It will rely on and expand the language processing pipelines that have been created as part of the Cleopatra project.

In my PhD, I research the creation and development of current event pages on Wikipedia and perform large-scale temporal analysis of events across different European Wikipedia language versions. I specifically research events that have a social or cultural significance for the European Union or European cultures such as the European Refugee Crisis, the Arab Spring, and Brexit.

I also work on cultural recommendation of page creations based on cultural and social relevance, preferences and importance, as well as the relationship between events, location and related entities.

While the sentiment analysis in highly resourced languages has received full attention, the same cannot be said for the low-resourced languages. It is this resource-scarce nature that makes them less effective in the analysis. This study aims to develop tools/methods to perform sentiment detection in cross-lingual fashion for low-resourced languages. This would imply the investigation of using resources from high-resourced languages and aid the sentiment analysis in scarce-resourced languages.

Fact validation is composed of the sub-tasks of parsing inputs, retrieving evidence, and inferring relations between the two. In related applications, approaches that compose a design – after breaking problems down into steps – are better suited to tasks with composite structure. Learning on multiple modalities introduces an additional level of complexity for monolithic architectures. This research aims to implement and evaluate systems optimized to process diverse data and adapt to the multiple sub-tasks comprising the fact validation pipeline.