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Manual annotations are a prerequisite for many applications of machine learning.
Learning from crowds
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010 · 2010
Earlier work this paper cites.
Discriminating Gender on Twitter
John D. Burger, John Henderson, George Kim, and Guido Zarrella. 2011 · 2011
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Bayesian bias mitigation for crowdsourcing
Fabian L. Wauthier and Michael I. Jordan. 2011 · 2011
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Learning from crowds in the presence of schools of thought
Yuandong Tian and Jun Zhu. 2012 · 2012
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Classifying political orientation on Twitter: It’s not easy!
Raviv Cohen and Derek Ruths. 2013 · 2013
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POMDP-based control of workflows for crowdsourcing
Peng Dai, Christopher H. Lin, Mausam, and Daniel S. Weld. 2013 · 2013
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“How Old Do You Think I Am?”: A Study of Language and Age in Twitter
Dong Nguyen, Rilana Gravel, Dolf Trieschnigg, and Theo Meder. 2013 · 2013
Earlier work this paper cites.
Vader: A parsimonious rule-based model for sentiment analysis of social media text
C. J. Hutto and Eric Gilbert. 2014 · 2014
Earlier work this paper cites.
Why gender and age prediction from tweets is hard: Lessons from a crowdsourcing experiment
Dong-Phuong Nguyen, R. B. Trieschnigg, A. Seza Doğruöz, Rilana Gravel, Mariët Theune, Theo Meder, and F. M. G. de Jong. 2014 · 2014
Earlier work this paper cites.
The benefits of a model of annotation
Rebecca J. Passonneau and Bob Carpenter. 2014 · 2014
Cited alongside, same era.
A Comparative Study of Demographic Attribute Inference in Twitter
Xin Chen, Yu Wang, Eugene Agichtein, and Fusheng Wang. 2015 · 2015
Cited alongside, same era.
Adapting computational text analysis to social science (and vice versa)
Paul DiMaggio. 2015 · 2015
Cited alongside, same era.
What I’ve learned about annotating informal text (and why you shouldn’t take my word for it)
Nathan Schneider. 2015 · 2015
Cited alongside, same era.
Towards Domain-Specific Semantic Relatedness: A Case Study from Geography
Shilad Sen, Isaac L. Johnson, Rebecca Harper, Huy Mai, Samuel Horlbeck Olsen, Benjamin Mathers, Laura Souza Vonessen, Matthew Wright, and Brent J. Hecht. 2015 · 2015
Cited alongside, same era.
Less is more? How demographic sample weights can improve public opinion estimates based on Twitter data
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
Javid Ebrahimi, Dejing Dou, and Daniel Lowd. 2016 · 2016
Later among the works it cites.
Contextualized Sentiment Analysis
Will Frankenstein, Kenneth Joseph, and K. M. Carley. 2016 · 2016
Later among the works it cites.
Semeval-2016 task 6: Detecting stance in tweets
Saif M. Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016a · 2016
Later among the works it cites.
Replacing mechanical turkers? challenges in the evaluation of models with semantic properties
Fred Morstatter and Huan Liu. 2016 · 2016
Later among the works it cites.
Probabilistic modeling for crowdsourcing partially-subjective ratings
An Thanh Nguyen, Matthew Halpern, Byron C. Wallace, and Matthew Lease. 2016 · 2016
Later among the works it cites.
Who Said What: Modeling Individual Labelers Improves Classification
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Pablo Barberá. 2016 · 2016
Cited alongside, same era.
Anchoring and Agreement in Syntactic Annotations
Yevgeni Berzak, Yan Huang, Andrei Barbu, Anna Korhonen, and Boris Katz. 2016 · 2016
Cited alongside, same era.
Demographic dialectal variation in social media: A case study of African-American English
Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016 · 2016
Cited alongside, same era.
Real Men Don’t Say “Cute”: Using Automatic Language Analysis to Isolate Inaccurate Aspects of Stereotypes
Jordan Carpenter, Daniel Preoţiuc-Pietro, Lucie Flekova, Salvatore Giorgi, Courtney Hagan, Margaret Kern, Anneke E. K. Buffone, Lyle Ungar, and Martin E. P. Seligman. 2016 · 2016
Cited alongside, same era.
Stance and sentiment in tweets
Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2016b
Cited in the paper.
Melody Y. Guan, Varun Gulshan, Andrew M. Dai, and Geoffrey E. Hinton. 2017 · 2017
Closest in time.
“Voters of the Year”: 19 Voters Who Were Unintentional Election Poll Sensors on Twitter
William Hobbs, Lisa Friedland, Kenneth Joseph, Oren Tsur, Stefan Wojcik, and David Lazer. 2017 · 2017
Closest in time.
Girls rule, boys drool: Extracting semantic and affective stereotypes from Twitter
Kenneth Joseph, Wei Wei, and Kathleen M. Carley. 2017 · 2017
Closest in time.
A framework for (under) specifying dependency syntax without overloading annotators
Nathan Schneider, Brendan O’Connor, Naomi Saphra, David Bamman, Manaal Faruqui, Noah A. Smith, Chris Dyer, and Jason Baldridge. 2013 · 2091
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