The advent of Web 2.0 empowered users to actively interact with the Web instead of passively consuming content. Today, Web users contribute content to discussion forums, microblogging sites, and review portals, while they organize themselves into online social networks where they form relationships post their thoughts and activities, and interact with each other. Individuals can now have a “presence” on the Web that goes well beyond creating a home page and some documents. Web users generate knowledge, either explicitly by contributing content, or implicitly through their choices and actions online. This kind of data is a goldmine for scientific research with an unlimited number of practical applications, ranging from marketing and recommendations to sociology and political science. For example, for the first time in history we are able to tap into the collective conscience of the planet’s population, and credibly answer the question “what do people think about X” where X can be a person, an object, an idea, or an event. We can perform large scale sociological studies to understand how users interact and affect each other.
In this project, we jointly mine different types of user-generated data, in order to enhance our understanding for the task at hand, and improve the knowledge extraction process. To this end, we considered the following sources of information about online users: textual information contributed in the form of reviews, micro-reviews, tweets and discussions; social network data in the form of friendship or following relationships between the users; structured data in the form of attribute-value pairs for users or items they interact with; user behavior data in the form of numerical ratings and opinions. Using this data we address the following general problems: summarization of reviews and micro-reviews; recommendations of content and links to users; understanding the evolution and nature of links in social networks; understanding of the way opinions are shaped, expressed and diffused online; interpreting textual information using structured data. Within the project, the fellow pursued research in all of these directions. His work introduced novel problems and methods that advance the state of the art in their respective fields, and also have several applications in practice.
The research project was developed to jumpstart the career of the principal researcher as a new faculty member. By the end of the project the Marie Curie fellow is successfully integrated in the local academic community. He is currently a tenured Associate professor with a research group consisting of several undergraduate and graduate students, and a network of collaborations with fellow faculty members in the host department and abroad.
· D. Dimitriadou. Discovery of associations between technical and lexicographical attributes extracted from internet reviews. B.Sc. Thesis, 2015
· V. Tsintzou. Election Analysis and Prediction of Election Results with Twitter. Diploma Thesis, 2016