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Damage Identification in Social Media30.html
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<h3><b>Damage Identification in Social Media Posts using Multimodal Deep Learning</b></h3> <!-- Title -->
<h5> Multimodal (Image, Captioned Image, and Text) | General | Response </h5> <!-- Type | Topic | Disaster phase -->
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<p> This is a general-disaster dataset for damage assessment. It contains 10,875 images, 5,879 captioned images, and 19,031 textual data. It is introduced for multiclass classification.</p>
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<li class="list-group-item"><strong>ML task type:</strong> Multiclass classification </li>
<li class="list-group-item"><strong>Data Source:</strong> Social Media (Instagram and Twitter); Publicly available datasets; Google Images</li>
<li class="list-group-item"><strong>Size:</strong> 10,875 Images; 5,879 Captioned Images, 19,031 Text </li>
<li class="list-group-item"><strong>Timespan:</strong> N/A </li>
<li class="list-group-item"> <strong>Geographical Coverage:</strong> Global </li>
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<li class="list-group-item"> <strong>Baseline Information</strong></li>
<li class="list-group-item"> <strong>Evaluated on:</strong> Maximum Decision Rule, Weighted Maximum Decision Rule, DFMC with ANN, DFMC with KNN, DFMC with SVM</li>
<li class="list-group-item"> <strong>Metrics used:</strong> Accuracy (Test)</li>
<li class="list-group-item"> <strong>Results as reported in original paper: </strong> 92.62 (DFMC with SVM)</li>
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<li class="list-group-item"> <strong>Find out more by:</strong></li>
<li class="list-group-item"> <a href="http://idl.iscram.org/files/husseinmouzannar/2018/2129_HusseinMouzannar_etal2018.pdf" target="_blank">Reading the research paper</a></li>
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