Towards detecting influenza epidemics by analyzing Twitter messages

By Aron Culotta. SOMA '10, 2010

710 citations recorded by the source corpus.

Subfields: Epidemiology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ca4b6c4d3e1797c89ad9a338923f4845b423af3a

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In Epidemiology

In Sociology and Political Science

Citations in the corpus

Statistical physics of vaccination (2016, background): “An increasing fraction of tweets contains geographical information that can be adopted to create models at different granularity going from cities [725] to countries [723, 724, 726, 736].”
Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis (2017, background): “…extracted from the content of online reviews by sentiment analysis techniques can be used to forecast many social economic phenomena, including product market shares, box office attendance, transmissions of information or diseases (Culotta, 2010) and the results of political elections (Lee, 2009).”
A real-time architecture for detection of diseases using social networks: design, implementation and evaluation (2012, background): “Previous work such as Culotta [2], Lampos and Cristianini [3] or Paul and Dredze [4] studied the possibility of detecting disease outbreaks by Twitter.”
You Are What You Tweet: Analyzing Twitter for Public Health (2011, background): “For comparison, these numbers are in the range of the 0.95 of Culotta (2010a) and the 0.97 of Lampos and Cristianini (2010) 5 .”
Predicting Responses to Microblog Posts (2012, neutral): “Mining Twitter Social media has been used to detect events (Sakaki et al., 2010; Popescu and Pennac-chiotti, 2010; Popescu et al., 2011), and even predict their outcomes (Asur and Huberman, 2010; Culotta, 2010).”
Predicting crime using Twitter and kernel density estimation (2014, background): “As shown, researchers have attempted to use social media to predict or detect disease outbreaks [11], election results [12], macroeconomic processes (including crime) [13], box office performance of movies [14], natural phenomena such as earthquakes [15], product sales [16], and financial markets…”
Harvesting ambient geospatial information from social media feeds (2011, neutral): “6 With the advent of micro-blogging the focus has also moved to using twitter messages to forecast inﬂuenza rates (Culotta 2010) or swine ﬂu pandemics (Ritterman et al. 2009).”
Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review (2020, methodology): “This approach can provide a rapid and effective monitoring mechanism for public health on a large scale at a low cost (Al-garadi et al., 2016; Choi et al., 2017; Culotta, 2010; Jain & Kumar, 2015; Pastor, 2020).”
Spatio-Temporal Data Mining (2017, background): “As another example, the outbreak of influenza at a given location and time can be predicted based on web searches [Ginsberg et al. 2009] and twitter messages [Culotta 2010] at neighboring locations and times.”
Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products (2013, background): “High correlations are reported between Twitter statistics and Centers for Disease Control and Prevention (CDC) statistics with regard to influenza informedness, affirming the value of Twitter as a rapid, cost-effective health status surveillance methodology [10].”
Using matched samples to estimate the effects of exercise on mental health from twitter (2015, background): “Social media are increasingly used for tracking health concerns such as influenza (Lampos and Cristianini 2010; Culotta 2010; Paul and Dredze 2011; Signorini, Segre, and Polgreen 2011; Sadilek, Kautz, and Silenzio 2012), E.”
#Earthquake: Twitter as a Distributed Sensor System (2013, background): “…work on data mining approaches that addressed disease outbreak detection by harvesting information from news articles (Brownstein et al. 2009), Internet search engines (e.g. Polgreen et al. 2008), blogs at large (Corley et al. 2010), and Twitter specifically (Ritterman et al. 2009, Culotta 2010).”
Separating Fact from Fear: Tracking Flu Infections on Twitter (2013, background): “Strategies for Twitter influenza surveillance include supervised classification (Culotta, 2010b; Culotta, 2010a; Eiji Aramaki and Morita, 2011), unsupervised models for disease discovery (Paul and Dredze, 2011), keyword counting1, tracking geographic illness propagation (Sadilek et al., 2012b), and…”
Social big data: Recent achievements and new challenges (2015, methodology): “Culotta [110] Track and predict outbreak detection using Twitter Classification (regression models)”
Sentiment analysis on evolving social streams: how self-report imbalances can help (2014, background): “Social media data has been successfully used to detect real-world events such as disease outbreaks [9], earthquakes [41] and recurring events such as goals and touchdowns in sports matches [29].”