Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 239–249, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Linking Tweets to News: A Framework to Enrich Short Text Data in Social Media Weiwei Guo∗ and Hao Li† and Heng Ji† and Mona Diab‡ ∗Department of Computer Science, Columbia University †Computer Science Department and Linguistic Department, Queens College and Graduate Center, City University of New York ‡Department of Computer Science, George Washington University weiwei@cs.columbia.edu, {haoli.qc,hengjicuny}@gmail.com, mtdiab@gwu.edu Abstract Many current Natural Language Process- ing [NLP] techniques work well assum- ing a large context of text as input data. However they become ineffective when applied to short texts such as Twitter feeds. To overcome the issue, we want to find a related newswire document to a given tweet to provide contextual support for NLP tasks. This requires robust model- ing and understanding of the semantics of short texts. The contribution of the paper is two-fold: 1. we introduce the Linking-Tweets-to- News task as well as a dataset of linked tweet-news pairs, which can benefit many NLP applications; 2. in contrast to previ- ous research which focuses on lexical fea- tures within the short texts (text-to-word information), we propose a graph based latent variable model that models the in- ter short text correlations (text-to-text in- formation). This is motivated by the ob- servation that a tweet usually only cov- ers one aspect of an event. We show that using tweet specific feature (hashtag) and news specific feature (named entities) as well as temporal constraints, we are able to extract text-to-text correlations, and thus completes the semantic picture of a short text. Our experiments show significant im- provement of our new model over base- lines with three evaluation metrics in the new task. 1 Introduction Recently there has been an increasing interest in language understanding of Twitter messages. Re- searchers (Speriosui et al., 2011; Brody and Di- akopoulos, 2011; Jiang et al., 2011) were in- terested in sentiment analysis on Twitter feeds, and opinion mining towards political issues or politicians (Tumasjan et al., 2010; Conover et al., 2011). Others (Ramage et al., 2010; Jin et al., 2011) summarized tweets using topic models. Al- though these NLP techniques are mature, their performance on tweets inevitably degrades, due to the inherent sparsity in short texts. In the case of sentiment analysis, while people are able to achieve 87.5% accuracy (Maas et al., 2011) on a movie review dataset (Pang and Lee, 2004), the performance drops to 75% (Li et al., 2012) on a sentence level movie review dataset (Pang and Lee, 2005). The problem worsens when some existing NLP systems cannot produce any results given the short texts. Considering the following tweet: Pray for Mali... A typical event extraction/discovery system (Ji and Grishman, 2008) fails to discover the war event due to the lack of context information (Ben- son et al., 2011), and thus fails to shed light on the users focus/interests. To enable the NLP tools to better understand Twitter …

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