Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.

The raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in both the IR and relevant scientific communities, this question is largely unanswered. We do not really understand how an information need emerges and how it is physically manifested. Information need refers to a complex concept: at the very initial state of the phenomenon (i.e. at a visceral level), even the searcher may not be aware of its existence. This renders the measuring of this concept (using traditional behaviour studies) nearly impossible. In this paper, we investigate the connection between an information need and brain activity. Using functional Magnetic Resonance Imaging (fMRI), we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A) Task, where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track. The results of this experiment revealed a distributed network of brain regions commonly associated with activities related to information need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search. We believe our study and conclusions constitute an important step in unravelling the nature of information need and therefore better satisfying it.

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Over the years, recommender systems have been systematically applied in both industry and academia to assist users in dealing with information overload. One of the factors that determine the performance of a recommender system is user feedback, which has been traditionally communicated through the application of explicit and implicit feedback techniques. In this paper, we propose a novel video search interface that predicts the topical relevance of a video by analysing affective aspects of user behaviour. We, furthermore, present a method for incorporating such affective features into user profiling, to facilitate the generation of meaningful recommendations, of unseen videos. Our experiment shows that multimodal interaction feature is a promising way to improve the performance of recommendation.

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