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"Breaking" Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters.

, , , and . CoRR, (2017)

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Data Sets: Word Embeddings Learned from Tweets and General Data., , , and . CoRR, (2017)SPread: Automated Financial Metric Extraction and Spreading Tool from Earnings Reports., , and . WSDM, page 853-856. ACM, (2020)funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter., , , , and . SemEval@ACL, page 852-856. Association for Computational Linguistics, (2017)Using counterfactual contrast to improve compositional generalization for multi-step quantitative reasoning., , and . ACL (1), page 14930-14943. Association for Computational Linguistics, (2023)Tweet Topic Classification Using Distributed Language Representations., , , , and . WI, page 81-88. IEEE Computer Society, (2016)Tweet Sentiment Analysis by Incorporating Sentiment-Specific Word Embedding and Weighted Text Features., , , , and . WI, page 568-571. IEEE Computer Society, (2016)TipMaster: A Knowledge Base of Authoritative Local News Sources on Social Media., , , , and . AAAI, page 7825-7830. AAAI Press, (2018)Using paraphrases to improve tweet classification: Comparing WordNet and word embedding approaches., , , , , and . IEEE BigData, page 4014-4016. IEEE Computer Society, (2016)funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts., , , , and . SemEval@ACL, page 741-746. Association for Computational Linguistics, (2017)Reuters tracer: Toward automated news production using large scale social media data., , , , , and . IEEE BigData, page 1483-1493. IEEE Computer Society, (2017)