2018
DOI: 10.1007/s10796-018-9832-0
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Information Needs and Communication Gaps between Citizens and Local Governments Online during Natural Disasters

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Cited by 41 publications
(29 citation statements)
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“…The results also show that, in times of public health crisis, citizens care more about the textual content of a post, rather than whether it contains a picture or video. Meanwhile, matching between crisis-related information demand and supply directly determines the effect of online communication (Hong et al, 2018;Wukich & Mergel, 2015). This empirical study also reveals that posts with pictures and videos attached relating to the latest information on the pandemic still brings the most citizen engagement; this could attribute to the Elaboration Likelihood Model (Petty & Cacioppo, 1986, pp.…”
Section: Summary Of Findingsmentioning
confidence: 84%
“…The results also show that, in times of public health crisis, citizens care more about the textual content of a post, rather than whether it contains a picture or video. Meanwhile, matching between crisis-related information demand and supply directly determines the effect of online communication (Hong et al, 2018;Wukich & Mergel, 2015). This empirical study also reveals that posts with pictures and videos attached relating to the latest information on the pandemic still brings the most citizen engagement; this could attribute to the Elaboration Likelihood Model (Petty & Cacioppo, 1986, pp.…”
Section: Summary Of Findingsmentioning
confidence: 84%
“…Design of information systems Two of the papers, viz. Troudi et al (2018) and Avvenuti et al (2018), use Bbig data^framework to develop useful information systems. Troudi et al (2018) report a new mashup based method for event detection from social media using the Hadoop framework.…”
Section: Application Of Machine Learningmentioning
confidence: 99%
“…Their proposed setup offers a multidimensional visualization by combining different multimedia components. On the other hand, Avvenuti et al (2018) present a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. They apply a classification based technique using word embeddings and geo-tagging to identify actionable information from tweets collected during two natural disasters in Italy.…”
Section: Application Of Machine Learningmentioning
confidence: 99%
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