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Articles

VGI and crowdsourced data credibility analysis using spam email detection techniques

ORCID Icon, ORCID Icon & ORCID Icon
Pages 520-532 | Received 12 Jan 2017, Accepted 08 Jun 2017, Published online: 21 Jun 2017

Figures & data

Figure 1. Main steps involved in filter-based email classification (Guzella and Caminhas Citation2009).

Figure 1. Main steps involved in filter-based email classification (Guzella and Caminhas Citation2009).

Figure 2. CSD credibility detection workflow.

Figure 2. CSD credibility detection workflow.

Table 1. Example of the combination results of the Incident title and Description of the Ushahidi CrowdMap message fields.

Table 2. Example of the combination result of the Incident title, Description and Location of the Ushahidi CrowdMap message fields.

Table 3. Examples of correctly and incorrectly classified messages.

Table 4. Test 1 – unforced training results using the small training sample (35 messages) and 33 test messages.

Table 5. Test 2 – forced training results using small training sample (35 messages) and 33 test messages.

Table 6. Test 3 – unforced training results using the larger training sample (77 messages) and 33 test messages.

Table 7. Test 4 – forced training results using the larger training sample (77 messages) and 33 test messages.

Table 8. Quality of the CSD classification.

Figure 3. Assessed credibility of 2011 Australian flood’s Ushahidi CrowdMap data.

Figure 3. Assessed credibility of 2011 Australian flood’s Ushahidi CrowdMap data.

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