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Identifying Linguistic Areas for Geolocation., and . W-NUT@EMNLP, page 231-236. Association for Computational Linguistics, (2019)Challenges of studying and processing dialects in social media., , and . NUT@IJCNLP, page 9-18. Association for Computational Linguistics, (2015)BERTective: Language Models and Contextual Information for Deception Detection., , , and . EACL, page 2699-2708. Association for Computational Linguistics, (2021)When Did that Happen? - Linking Events and Relations to Timestamps., , , , and . EACL, page 185-193. The Association for Computer Linguistics, (2012)Universal Joy A Data Set and Results for Classifying Emotions Across Languages., , , and . WASSA@EACL, page 62-75. Association for Computational Linguistics, (2021)Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks., , , and . NAACL-HLT, page 175-190. Association for Computational Linguistics, (2022)The Importance of Modeling Social Factors of Language: Theory and Practice., and . NAACL-HLT, page 588-602. Association for Computational Linguistics, (2021)"It's Not Just Hate": A Multi-Dimensional Perspective on Detecting Harmful Speech Online., , , , , and . EMNLP, page 8093-8099. Association for Computational Linguistics, (2022)The Rating Game: Sentiment Rating Reproducibility from Text., , and . EMNLP, page 2527-2532. The Association for Computational Linguistics, (2015)The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews - An Adversarial Study.. ACL (2), The Association for Computer Linguistics, (2016)