Paper at WebScience 2011 — How People use Twitter in Different Languages

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1. Abstract

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2. 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

   42.72% similar — Paper at DIR 2011 — Semi-Supervised Priors for Microblog Language Identification

3. Abstract

   We propose a retrieval model for searching microblog posts for a given topic of interest. We develop a language modeling approach tailored to microblogging characteristics, where redundancy-based IR methods cannot be used in a straightforward manner. We enhance this model with two groups of quality indicators: textual and microblog specific. Additionally, we propose a dynamic query expansion model for microblog post retrieval. Experimental results on Twitter data reveal the usefulness of boolean search, and demonstrate the utility of quality indicators and query expansion in microblog search.

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4. References

   [1] Manos Tsagkias, Maarten de Rijke, and Wouter Weerkamp. 2011. Linking online news and social media. In Proceedings of the fourth ACM international conference on Web search and data mining (WSDM ‘11). Association for Computing Machinery, New York, NY, USA, 565–574. ACM Link PDF

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5. The advent of social media has established a symbiotic relationship between social media and online news. This relationship can be leveraged for tracking news content, and predicting behavior with tangible real-world applications, e.g., online reputation management, ad pricing, news ranking, and media analysis. In this thesis we focus on tracking news content in social media, and predicting user behavior.

   In the first part, we develop methods for tracking content which build upon, and extend practices in Information Retrieval. We begin with discovering social media posts that discuss a news article yet they do not provide a hyperlink to it. Our methods model news articles using several channels of information, either endogenous or exogenous to the article. These models are then used to query an index of social media posts. During this process we found that the query models are close in size to the documents to be retrieved, violating a standard assumption of language

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6. I am very happy that our paper Language Intent Models for Inferring User Browsing Behavior by Manos Tsagkias, and Roi Blanco [1] has been accepted at SIGIR 2013, which will be held in Portland, Oregon, 12–16 August 2012. The paper was realized during my three-month internship at Yahoo! Research Barcelona during September–December 2011.

   References

   [1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35^th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF

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7. I moved to Barcelona in the first week of September for a three-month internship at Yahoo! Research Labs. I’m very excited about it, and I’m looking forward to getting to know the people here and the problems they are working on.

   Update: My work at Yahoo! Research Labs resulted in a publication at SIGIR 2012 [1]; see [post] for the abstract(20120623-sigir2012-language-intent-models.md).

   References

   [1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35^th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF

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