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Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection., , , , , , and . SemEval@NAACL-HLT, page 1041-1046. Association for Computational Linguistics, (2019)SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums., , , , , and . SemEval@NAACL-HLT, page 860-869. Association for Computational Linguistics, (2019)What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context., , , , , , , and . ACL, page 3364-3374. Association for Computational Linguistics, (2020)AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language., , , , , and . ACM Trans. Asian Low Resour. Lang. Inf. Process., 16 (4): 25:1-25:20 (2017)OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model., , , , , , , , , and 1 other author(s). SemEval@ACL, page 603-610. Association for Computational Linguistics, (2017)A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis., , , , , and . ACM Trans. Inf. Syst., 35 (1): 7:1-7:21 (2016)Tanbih: Get To Know What You Are Reading., , , , , , , , , and 3 other author(s). EMNLP/IJCNLP (3), page 223-228. Association for Computational Linguistics, (2019)A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models., , , , , , , , and . WANLP@EACL, page 110-118. Association for Computational Linguistics, (2017)Deep Learning Models for Sentiment Analysis in Arabic., , , , , and . ANLP@ACL, page 9-17. Association for Computational Linguistics, (2015)Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects., , , , , , and . ACLING, volume 117 of Procedia Computer Science, page 266-273. Elsevier, (2017)