  In this paper, we present our methods for the MediaEval 2020 Flood Related
Multimedia task, which aims to analyze and combine textual and visual content
from social media for the detection of real-world flooding events. The task
mainly focuses on identifying floods related tweets relevant to a specific
area. We propose several schemes to address the challenge. For text-based flood
events detection, we use three different methods, relying on Bog of Words (BOW)
and an Italian Version of Bert individually and in combination, achieving an
F1-score of 0.77%, 0.68%, and 0.70% on the development set, respectively. For
the visual analysis, we rely on features extracted via multiple
state-of-the-art deep models pre-trained on ImageNet. The extracted features
are then used to train multiple individual classifiers whose scores are then
combined in a late fusion manner achieving an F1-score of 0.75%. For our
mandatory multi-modal run, we combine the classification scores obtained with
the best textual and visual schemes in a late fusion manner. Overall, better
results are obtained with the multimodal scheme achieving an F1-score of 0.80%
on the development set.
