You Are What You Tweet: Analyzing Twitter for Public Health

By Michael J. Paul, Mark Dredze. International Conference on Web and Social Media, 2011

1,145 citations recorded by the source corpus.

Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.

Subfields: Epidemiology, Health, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e4e3d5552a0071f4f677d06c672c31b402b1266c

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Citations in the corpus

Big Data for Development: A Review of Promises and Challenges (2016, neutral): “Or to say it in more modern terms: “You Are What You Tweet” (Paul and Dredze, 2011).”
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).”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “More generally, Twitter has been widely used in a range of other fields to measure population level trends, such as sociolinguistics [17], political science [37], and public health [40].”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, background): “…become important resources for various applications such as breaking news aggregation (Ritter et al. 2012; Cui et al. 2012), the identification of cyber-attacks (Ritter et al. 2015) or natural disasters (Neubig et al. 2011; Bruns and Liang 2012), and mining disease outbreaks (Paul and Dredze 2011).”
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).”
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity (2014, background): “Turning to research on social media, a growing body of work has demonstrated that social media is an increasingly adopted platform allowing users to communicate around a variety of health concerns (Paul & Dredze, 2011; Fox, 2013; De Choudhury et al. 2014).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “…propositional content or not, both degree adverbs and intensifiers impact the sentiment of the predicate, and there is some work in exploring this interaction (Zhang, Zeng, Xu, Xin, Mao, & Wang, 2008; Wang & Wang, 2012; Xu, Wong, Lu, Xia, & Li, 2008; Lu & Tsou, 2010; Taboada, Voll, & Brooke, 2008).”
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media (2016, neutral): “In recent years, social media has been recognized to be a powerful “lens” that can provide insights into psychological states, health and well-being of individuals and populations [50, 19].”
OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates (2012, background): “For example, Paul and Dredze (2011) analyze Twitter to better understand trends in health, such as what drugs are used to treat common ailments.”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “For instance, links among disease carriers and new infections based on covisited locations were found by Sadilek et al. [85], while Paul and Dredze [76] identified links between mentioned ailments and the geographies where they occur.”
Recognizing Depression from Twitter Activity (2015, background): “Twitter data are also suggested to be effective for estimating health-related statistics [10, 29].”
Using matched samples to estimate the effects of exercise on mental health from twitter (2015, background): “Of the many recent studies inferring health from social media (Lampos and Cristianini 2010; Culotta 2010; Paul and Dredze 2011; Signorini, Segre, and Polgreen 2011; Heaivilin et al. 2011; Dredze 2012; Jamison-Powell et al. 2012; Sadilek, Kautz, and Silenzio 2012; Stewart and Diaz 2012; Hanson et al.”
Separating Fact from Fear: Tracking Flu Infections on Twitter (2013, methodology): “ATAM: We obtained 1.6 million tweets that were automatically labeled as inﬂuenza/other by ATAM (Paul and Dredze, 2011).”
Semi-supervised User Geolocation via Graph Convolutional Networks (2018, background): “User geolocation, the task of identifying the “home” location of a user, is an integral component of many applications ranging from public health monitoring (Paul and Dredze, 2011; Chon et al., 2015; Yepes et al., 2015) and regional studies of sentiment, to real-time emergency awareness systems (De ”
Ethical Research Protocols for Social Media Health Research (2017, background): “, 2011), public health (Paul and Dredze, 2011), economics (Bollen et al.”