Tracking the flu pandemic by monitoring the social web

By Vasileios Lampos, Nello Cristianini. International Workshop on Cognitive Information Processing, 2010

358 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/22d6ba3a04d6814c112861f67d94884177e2fa05

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In Epidemiology

Citations in the corpus

Measuring objective and subjective well-being: dimensions and data sources (2020, background): “[127] measure the prevalence of flu-like symptoms in the general UK population, based on the contents of Twitter, searching for symptom-related statements, turning this information into a flu-score and they obtain on average a statistically significant linear correlation which is higher than 95%.”
The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic (2011, result): “If future results are consistent with our findings, Twitter-based surveillance efforts like ours and similar efforts underway in two European research groups [31,32] may provide an important and”
User Classification with Multiple Textual Perspectives (2016, background): “Recently, the growth of online social networks provides the opportunity to perform user classification in a broader context (Bollen et al., 2011; Sadilek et al., 2012; Lampos and Cristianini, 2010; Zamal et al., 2012).”
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 .”
Effects of Sampling on Twitter Trend Detection (2016, background): “…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, 2010; Lampos and Cristianini, 2010; Szomszor et al., 2012; Nagar et al., 2014; Chakraborty et al., 2014) Yates et al. (2016a) model relationships between…”
Confronting the data-divide in a time of spatial turns and volunteered geographic information (2013, methodology): “As an example in the health domain, Chen and Sui (2010), Boulos et al. (2010), and Lampos and Cristianini (2010) describe VGI data collection approaches for the purposes of disease surveillance.”
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.”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “…it is still challenging to quickly extract spatiotemporal patterns from big social data to, for example, help predict criminal activity (Heffner 2014), observe emerging public health threats and provide more effective intervention (Lampos and Cristianini 2010; Jalali, Olabode, and Bell 2012).”
The Geography and Importance of Localness in Geotagged Social Media (2016, background): “, [38,44,49]) now regularly use social media VGI to better understand phenomena of interest ranging from social unrest and emergencies [6,24,43] to disease tracking [22,27,42].”
A real-time architecture for detection of diseases using social networks: design, implementation and evaluation (2012, background): “[3] Vasileios Lampos, Nello Cristianini.”
The power of prediction with social media (2013, methodology): “As representative of this line of research and commonly applied methods we will just refer to the work by Lampos and Cristianini (2010) and Signorini et al. (2011).”