Mapping spatiotemporal patterns of events using social media: a case study of influenza trends

ABSTRACT

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.

KEYWORDS:

• CyberGIS
• event detection
• influenza surveillance
• social media
• spatiotemporal analysis

Acknowledgments

This material is based in part upon work supported by the US National Science Foundation under grant numbers: 1047916, 1354329, 1429699, and 1443080. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://about.twitter.com/company visited on 10 January 2016.

2. Data source: http://www.cdc.gov/flu/weekly/fluviewinteractive.htm.

3. Data Source: Google Flu Trends (http://www.google.org/flutrends).

Additional information

Funding

This material is based in part upon work supported by the US National Science Foundation under grant numbers: [1047916], [1354329], [1429699], and [1443080].