Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

By Eiji Aramaki, S. Maskawa, Mizuki Morita. Conference on Empirical Methods in Natural Language Processing, 2011

681 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/51853d0e2b9ca6962bd8fa1272c77811e5ad18ab

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Citations in the corpus

You Tweet What You Eat: Studying Food Consumption Through Twitter (2014, background): “Recently, Twitter has been used as a source of data for public health monitoring, such as for tracking flu-like symptoms [3, 9, 21], adverse side-effects of drugs [4], tobacco use [19], and county-level health statistics [10] .”
Processing Social Media Messages in Mass Emergency (2014, background): “, Aramaki et al. [2011]); for a survey, see Weber and Mejova [2013].”
Sentiment analysis of Twitter data for predicting stock market movements (2016, background): “Eiji et al. [11] have studied the twitter data for catching the ﬂu outbreaks.”
Quantifying Mental Health Signals in Twitter (2014, background): “…media is plentiful and has enabled diverse research on a wide range of topics, including political science (Boydstun et al., 2013), social science (Al Zamal et al., 2012), and health at an individual and population level (Paul and Dredze, 2011; Dredze, 2012; Aramaki et al., 2011; Hawn, 2009).”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “Furthermore, these could be used for general population tracking to understand compliance with social distancing.”
Social big data: Recent achievements and new challenges (2015, background): “[111] Classify tweets related to influenza Classification”
Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll (2015, methodology): “[15] Eiji Aramaki, Sachiko Maskawa, and Mizuki Morita.”
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses (2015, background): “While many users discuss physical health conditions such as cancer or the flu (Paul and Dredze, 2011; Dredze, 2012; Aramaki et al., 2011; Hawn, 2009), some also discuss mental illness.”
What to Expect When the Unexpected Happens: Social Media Communications Across Crises (2015, methodology): “This practice follows customary procedures used for using crowdsourced annotation of text messages for both information type [4, 10, 24, 34] and information source [14, 34].”
Challenges of Big Data Analysis (2013, unknown): “They are implementing specialized data analytics programs to collect, store, man- ageandanalyzelargedatasetsfromarangeofsourcestoidentifykeybusinessinsightsthatcanbeex-ploitedtosupportbetterdecisionmaking.”
Vulnerability Disclosure in the Age of Social Media: Exploiting Twitter for Predicting Real-World Exploits (2015, background): “In prior analysis of Twitter data, success has been found in a wide variety of applications including earthquake detection [53], epidemiology [26, 37], and the stock market [34, 58].”
Incorporating Dialectal Variability for Socially Equitable Language Identification (2017, background): “We conclude with a real-world case study on us-ing Twitter posts as a real-time source of information for tracking health and well-being trends (Paul and Dredze, 2011; Achrekar et al., 2011; Aramaki et al., 2011).”