language:
- en
Source: CrisisMMD (Alam et al., 2017)
Data Type: Multimodal — each sample includes:
tweet_text (social media text)
tweet_image (corresponding image from the tweet)
Total Samples Used: ~18,802(from the dataset)
Class Labels:
0 → Non-informative
1 → Informative
Collect only values where tweet_text and tweet_image are equal. (thus collected 12,743 tweets and convert it into test and train .pt files)
✅ Preprocessing Done Text:
Tokenized using BERT tokenizer (bert-base-uncased)
Extracted input_ids and attention_mask
Image:
Processed using ResNet-50
Extracted 2048-dimensional feature vectors
Label:
Encoded to 0 or 1 as per class
The final preprocessed dataset was saved as .pt files:
train_info.pt
test_info.pt
Each contains: input_ids, attention_mask, image_vector, and label tensors.