Separating Fact from Fear: Tracking Flu Infections on Twitter

By Alex Lamb, Michael J. Paul, Mark Dredze. North American Chapter of the Association for Computational Linguistics, 2013

322 citations recorded by the source corpus.

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“Participant” Perceptions of Twitter Research Ethics (2018, background): “…in turn has proven itself a highly successful platform for research, resulting in studies and knowledge gained on topics such as disease tracking (Lamb, Paul, & Dredze, 2013; Signorini, Segre, & Polgreen, 2011), disaster events (Kogan, Palen, & Anderson, 2016; Sakaki, Okazaki, & Matsuo, 2010;…”
Comparing and combining sentiment analysis methods (2013, methodology): “Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods.”
Incorporating Dialectal Variability for Socially Equitable Language Identification (2017, methodology): “Setup A list of health-related terms was compiled from lexicons for inﬂuenza (Lamb et al., 2013); psychological well-being (Smith et al., 2016; Preot¸iuc-Pietro et al., 2015); and temporal orientation lexica correlated with age, gender and personality traits (Park et al., 2016).”
National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic (2013, methodology): “These linguistic features are described in detail in our recent paper [9].”