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

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1. 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.  

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2. 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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3. 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

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4. 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

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5. 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

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6. 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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7. Republished article finding is the task of identifying instances of articles that have been published in one source and republished more or less verbatim in another source, which is often a social media source. We address this task as an ad hoc retrieval problem, using the source article as a query. Our approach is based on language modeling. We revisit the assumptions underlying the unigram language model taking into account the fact that in our setup queries are as long as complete news articles. We argue that in this case, the underlying generative assumption of sampling words from a document with replacement, i.e., the multinomial modeling of documents, produces less accurate query likelihood estimates.  

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