Riskr: A Low-Technological Web2.0 Disaster Service to Monitor and Share Information

Farber, Jess; Myers, Trina; Trevathan, Jarrod; Atkinson, Ian; Andersen, Trevor

doi:10.1109/nbis.2012.12

Cited by 3 publications

(1 citation statement)

References 7 publications

(14 reference statements)

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“…For example, from some reports, the time when Chinese web users know the September 11 attacks is only 5 minutes later than the president of the United States [12,13]. Recently, with the rapid development of social media such as Twitter and Facebook, web becomes an important events' information provider more than ever [14].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Crowd Sensing Based Burst Computing of Events Using Social Media

Liu

Mei

et al. 2015

International Journal of Distributed Sensor NetworksRetracted 2018-2-16

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With the popularity of web, the internet is becoming a major information provider and poster of an event due to its real-time, open, and dynamic features. In this paper, crowd sensing based burst computation algorithm of a web event is developed in order to let the people know a web event clearly and help the social group or government process the events effectively. Different temporal features of web events are developed to provide the basics for the proposed computation algorithm. The burst power is presented to integrate the above temporal features of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that the number of web pages and the average clustering coefficient can be used to detect events. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly.

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Section: Introductionmentioning

confidence: 99%