Paper at SIGIR 2013 — Pseudo Test Collections for Training and Tuning Microblog Rankers

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1. 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

   41.10% similar — Paper at ECIR 2011 — Incorporating Query Expansion and Quality Indicators in Searching Microblog Posts

2. Much of what is discussed in social media is inspired by events in the news and, vice versa, social media provide us with a handle on the impact of news events. We address the following linking task: given a news article, find social media utterances that implicitly reference it. We follow a three-step approach: we derive multiple query models from a given source news article, which are then used to retrieve utterances from a target social media index, resulting in multiple ranked lists that we then merge using data fusion techniques. Query models are created by exploiting the structure of the source article and by using explicitly linked social media utterances that discuss the source article. To combat query drift resulting from the large volume of text, either in the source news article itself or in social media utterances explicitly linked to it, we introduce a graph-based method for selecting discriminative terms.  

   40.52% similar — Paper at WSDM 2011 — Linking online news and social media

3. We describe the participation of the University of Amsterdam’s ILPS group in the web, blog, web, entity, and relevance feedback track at TREC 2009. Our main preliminary conclusions are as follows. For the Blog track we find that for top stories identification a blogs to news approach outperforms a simple news to blogs approach. This is interesting, as this approach starts with no input except for a date, whereas the news to blogs approach also has news headlines as input. For the web track, we find that spam is an important issue in the ad hoc task and that Wikipedia- based heuristic optimization approaches help to boost the retrieval performance, which is assumed to potentially reduce the spam in top ranked documents. As for the diversity task, we explored different methods. Initial results show that clustering and a topic model-based approach have similar performance, which are relatively better than a query log based approach. Our performance in the Entity track was downright

   39.69% similar — Paper at TREC 2009 — The University of Amsterdam at TREC 2009

4. The popularity of microblogging platforms, such as Twitter, renders them valuable real-time information resources for tracking various aspects of worldwide events, e.g., earthquakes, political elections, etc. Such events are usually characterized in microblog posts via the use of hashtags (#). As microbloggers come from different backgrounds, and express themselves in different languages, we witness different “translations” of hashtags which, however, are about the same event. Language-dependent variants of hashtags can possibly lead to issues in content-analysis. In this paper, we propose a method for translating hashtags, which builds on methods from information retrieval. The method introduced is source and target language independent. Our method is desirable, either instead of, or complimentary, to the direct translation of the hashtag for three reasons. First we return a list of hashtags on the same topic, which takes into account the plurality and variability of hashtags used by

   37.55% similar — Paper at WebScience 2011 — Twitter hashtags: Joint Translation and Clustering

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

   36.08% similar — Ph.D. Thesis — Mining Social Media: Tracking Content and Predicting Behavior

6. 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

   34.26% similar — Paper at DIR 2011 — Semi-Supervised Priors for Microblog Language Identification

7.   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.

   32.68% similar — Paper at SIGIR 2011 — Hypergeometric Language Models for Republished Article Finding