Today online social network services are challenging state-of-the-art social media mining algorithms due to its real-time nature, scale and amount of unstructured data generated. Given the amount and cadence of the data made available by online social network services like Twitter, classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions about computational complexity and required execution time per document analysis. If from the point of view of a natural language processing, text mining and unsupervised learning, the problem of detecting events in unbounded text streams is hard, dealing with dynamic networks with millions of nodes and edges is also not an easy task.
This work presents a research proposal towards a robust real-time social network text stream event detection system that combines text stream mining and network analysis methods. This proposal presents the current state-of-the-art systems, algorithms and methodologies to perform event detection in streaming environments. The present research proposal is based on the premise that the precision and accuracy of an event detection algorithm could be improved by considering network properties of social network when events happen. It is expected to be proven that events are better predicted by taking into account extra information about the network rather than just considering the data stream text or terms alone.