Abstract
Controversial topics, especially the new emerging ones are widely discussed and searched in social medias like Twitter. When people are interested in topics and search on Twitter, high quality tweets are expected to appear at the top. Since it is only argumentation that truly reasons things out, we believe that high quality tweets are those with argumentation that consists of claim and evidence. Moreover, claim is the heart of argumentation, we concentrate on claim retrieval in Twitter. Based on a learning-to-rank framework, we integrate Twitter structural information and topic-independent claim-related lexicon to re-rank the relevant tweet list pre-retrieved by BM25 scores. We also automatically construct topic-dependent claim-oriented lexicons to further elevate the retrieval performance. Additionally, our model can be easily adapted to new topics without any manual process or external information, which guarantees the practicability of our model.
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This is to distinguish from “evidence” or “data” which is essential prerequisite for world knowledge [19].
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Claim-related information refers to words whose appearance can make information gain for detecting whether a tweet contains claim.
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The overall inter-annotator agreement was calculated by averaging the agreements on all tweets in the dataset. For each tweet, the inter-annotator agreement was calculated as the number of annotators who agree over the majority label divided by the total number of annotators for that tweet.
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Acknowledgments
We appreciate the comments from anonymous reviewers. This work is supported by National Key Research and Development Program of China (Grant No. 2017YFB1402400) and National Natural Science Foundation of China (No. 61602490).
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Ma, W., Chao, W., Luo, Z., Jiang, X. (2018). Claim Retrieval in Twitter. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_20
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DOI: https://doi.org/10.1007/978-3-030-02922-7_20
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