Jointly Mining of User Generated Content Sources (JMUGCS)
Marie-Curie Reintegration Grant 322375
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