Paper at TREC 2009 — The University of Amsterdam at TREC 2009

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1.   For our experimental evaluation, we use data from Twitter, Digg, Delicious, the New York Times Community, Wikipedia, and the blogosphere to generate query models. We show that different query models, based on different data sources, provide complementary information and manage to retrieve different social media utterances from our target index. As a consequence, data fusion methods manage to significantly boost retrieval performance over individual approaches. Our graph-based term selection method is shown to help improve both effectiveness and efficiency.

   47.89% similar — Paper at WSDM 2011 — Linking online news and social media

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. 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

   41.03% similar — Ph.D. Thesis — Mining Social Media: Tracking Content and Predicting Behavior

4. 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

   39.02% similar — Paper at DIR 2011 — Semi-Supervised Priors for Microblog Language Identification

5. Abstract

   On-line news agents provide commenting facilities for readers to express their views with regard to news stories. The number of user supplied comments on a news article may be indicative of its importance or impact. We report on exploratory work that predicts the comment volume of news articles prior to publication using five feature sets. We address the prediction task as a two stage classification task: a binary classification identifies articles with the potential to receive comments, and a second binary classification receives the output from the first step to label articles “low” or “high” comment volume. The results show solid performance for the former task, while performance degrades for the latter.

   References

   38.76% similar — Paper at CIKM 2009 — Predicting the Volume of Comments on Online News Stories

6.   To make up for this discrepancy, we consider distributions that emerge from sampling without replacement: the central and non-central hypergeometric distributions. We present two retrieval models that build on top of these distributions: a log odds model and a bayesian model where document parameters are estimated using the Dirichlet compound multinomial distribution.   We analyse the behavior of our new models using a corpus of news articles and blog posts and find that for the task of republished article finding, where we deal with queries whose length approaches the length of the documents to be retrieved, models based on distributions associated with sampling without replacement outperform traditional models based on multinomial distributions.

   38.26% similar — Paper at SIGIR 2011 — Hypergeometric Language Models for Republished Article Finding

7. 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.

   References

   36.17% similar — Paper at ECIR 2011 — Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts