Paper at ECIR 2011 — Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts

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2. Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search.  

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3. Abstract

   In this paper we describe how Twitter is used in various languages. We observe notable differences between languages regarding the use of hashtags, links, mentions, and conversations. We propose two dimensions that can be used to classify languages, each of which is likely to require different ways of analysis.

   References

   [1] Wouter Weerkamp, Simon Carter, and Manos Tsagkias. 2011. How People use Twitter in Different Languages. In Web Science 2011. UVA Link; PDF.

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5. Abstract

   Offering access to information in microblog posts requires successful language identification. Language identification on sparse and noisy data can be challenging. In this paper we explore the performance of a state-of-the-art n-gram-based language identifier, and we introduce two semi-supervised priors to enhance performance at microblog post level: (i) blogger-basedprior, using previous posts by the same blogger, and (ii) link-based prior, using the pages linked to from the post. We test our models on five languages (Dutch, English, French, German, and Spanish), and a set of 1,000 tweets per language. Results show that our priors improve accuracy, but that there is still room for improvement.

   References

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