Early Detection and Forecasting of Research Trends.

Abstract

Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others. Currently, this task is performed either by domain experts, with the assistance of tools for exploring research data, or by automatic approaches. The constant increase of research data makes the second solution more appropriate, howeverautomatic methods suffer from a number of limitations. For instance, they are unable to detect emerging but yet unlabelled research areas (e.g., Semantic Web before 2000). Furthermore, they usually quantify the popularity of a topic simply in terms of the number of related publications or authors for each year; hence they can provide good forecasts only on trends which have existed for at least 3-4 years. This doctoral work aims at solving these limitations by providing a novel approach for the early detection and forecasting of research trends that will take advantage of the rich variety of semantic relationships between research entities (e.g., authors, workshops, communities) and of social media data (e.g., tweets, blogs).

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