Temporal Information Models for Real time Microblog Search
Real-time search in Twitter and other social media services is often biased towards the most recent results due to the in the moment nature of topic trends and their ephemeral relevance to users and media in general. However, in the moment, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where it was shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step.
07/11/2018 14:00
Multimodal Systems
Flávio Martins is a Ph.D. candidate working in the Web and Media Search lab (NOVA Search). His research interests include information retrieval, data mining, and machine learning with a focus on time-aware information retrieval and real-time social media search. As part of his dissertation work, he studies and builds techniques that leverage temporal signals from multiple sources on the Web to improve the ranking of search results.
He is co-advised by João Magalhães in the NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) at Universidade NOVA de Lisboa, and Jamie Callan in the Language Technologies Institute (LTI) at Carnegie Mellon University.
