The world of media and communication is currently experiencing enormous disruptions: from one-way communication and word of mouth exchanges, we have moved to bi- or multi directional communication patterns. No longer can selected few (e.g. media organizations and controllers of communication channels) act as gatekeepers, deciding what is communicated to whom and what not. Individuals now have the opportunity to access information directly from primary sources, through a channel we label e'-word of mouth', or what we commonly call 'Social Media'. A key problem: it takes a lot of effort to distinguish useful information from the 'noise' (e.g. useless or misleading information). Finding relevant information is often tedious. This challenge has become the focus of various research efforts. Many concentrate on the automatic discovery of information by adapting semantic search and retrieval technologies to the particularities of Social Media content. REVEAL, however, aims to discover higher level concepts hidden within information. In Social Media we do not only have bare content; we also have interconnected sources. We have to deal with interactions between them, and we have many indicators about the context within which content is used, and interactions taking place. A core challenge is to decipher interactions of individuals in permanently changing constellations, and do so in real time.This is what we aim for! We will reveal much more than bare content. Further to discovering what is being said, we will determine how trustworthy that information is. We will predict contributor impact and how much or to what extent all this affects reputation or influence. This allows us to automatically judge the quality and accuracy of content, and bring us to predicting future trends with greater accuracy. We label all this Social Media modalities.
- Network theory
- Machine learning and data mining
Open thesis topic: