Predicting Flu Trends using Twitter data

By Harshavardhan Achrekar, Avinash Gandhe, Ross Lazarus, Ssu-Hsin Yu, Benyuan Liu. Conference on Computer Communications Workshops, 2011

506 citations recorded by the source corpus.

Subfields: Epidemiology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f531e75597371f082b17afecd84c91fdd53bb015

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In Sociology and Political Science

Citations in the corpus

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).”
On the Interpretation of Digital Trace Data in Communication and Social Computing Research (2014, unknown): “While two offer no additional interpretation (Achrekar, Gandhe, Lazarus, Yu, & Liu, 2011; Wu, Hofman, Mason, & Watts, 2011), the rest go further.”
Effects of Sampling on Twitter Trend Detection (2016, methodology): “(Achrekar et al., 2011; Paul et al., 2014; Chew and Eysen-bach, 2010; Gesualdo et al., 2013; Aramaki et al., 2011; Ji et al., 2012; Culotta, 2010; Achrekar et al., 2012; Sadilek et al., 2012; Lamb et al., 2013; Yates et al., 2014; Parker et al., 2015; Nagel et al., 2013; de Quincey and Kostkova,…”
Predicting Social Unrest Events with Hidden Markov Models Using GDELT (2017, methodology): “For example, Achrekar et al. [34] used autoregressive modeling to predict flu trends using twitter data.”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, methodology): “This approach has been applied to prediction of economic, financial and other signals [4, 7, 15, 2, 1].”
Measuring Social Jetlag in Twitter Data (2016, background): “Some of this research exploits the temporal nature of Twitter data, such as flu trend prediction (Achrekar et al. 2011), earthquake observations (Sakaki, Okazaki, and Matsuo 2010), event extraction (Ritter et al.”
Surpassing the Limit: Keyword Clustering to Improve Twitter Sample Coverage (2015, background): “The predictive power of social media services, such as Twitter, has been used to effectively track and predict the spread of disease [1, 6, 8].”
Vulnerability Disclosure in the Age of Social Media: Exploiting Twitter for Predicting Real-World Exploits (2015, background): “Prior research efforts on Twitter analytics have been able to extract information from millions of tweets, by focusing on popular topics like movies [27], flu outbreaks [20, 26], or large-scale threats like spam [56].”
Multi-Task Learning for Spatio-Temporal Event Forecasting (2015, methodology): “Clustering-based methods search for novel spatial clusters of documents or terms using predefined similarity metrics, such as cosine similarity and social similarity for documents [3], or auto-correlations [2] and co-occurrences [32] for terms.”