Research On Technology Of Topic Detection And Tracking In Microblog

Microblogging is bringing huge impact to communication and has become a valuable source of public opinions in recently years.It is a kind of social media with clear we-media features,in which information disseminates rapidly,and the public is widely involved.Public events which happened in real world and public opinions about them are gathered in microblogging,conflicting and influencing each other,which makes microblogging become a huge and complex opinion field.The spread of event information in microblogging gives rise to public opinions,and influences their evolution.The gathering of the latter,in turn,affects the development of events in the real world.Therefore,microblogging is an excellent platform for technologies researches on public events,public opinion,and their interaction patterns.It also makes the study has a strong practical significance and social value.In this thesis,messages in microblogging are divided into two categories according to the targets of their descriptions.One is the objective information,which describes the properties and developments of events.The other is subjective ideas,which describes interests,sentiments and opinions of the public.Furthermore,two special kinds of messages in microblogging,expandable posts and their reposts,are selected as samples corresponding to the two types of messages above.Base on this,some key technologies on public event and public opinion analysis,including new event detection,event evolution analysis,public concern evolution analysis,and the measurement of influence of the public concern on the event information evolution,are studied in this thesis.In order to detect new events from short and noisy messages in microblogging,we proposed a novel method for new event detection based on swarm intelligence.According to microblogging users and their relationships,message stream is split into multiple destinations.Then we constructed multiple individual models to simulate user's knowledge structures when judging new events.The final conclusion of new event detection is drawn from an election by integration of each vote.Since the granularity of events is hard to define in event evolution analysis,we introduced Information Extraction(IE)techniques and the concept of atomic event,and proposed an evolution analysis approach based on atomic event mining.Our method firstly extracts atomic events from each document,and then identifies their co-reference among the whole corpus,finally measures their relationships and builds the evolution graph.