2 research outputs found
Social Media-based Substance Use Prediction
In this paper, we demonstrate how the state-of-the-art machine learning and
text mining techniques can be used to build effective social media-based
substance use detection systems. Since a substance use ground truth is
difficult to obtain on a large scale, to maximize system performance, we
explore different feature learning methods to take advantage of a large amount
of unsupervised social media data. We also demonstrate the benefit of using
multi-view unsupervised feature learning to combine heterogeneous user
information such as Facebook `"likes" and "status updates" to enhance system
performance. Based on our evaluation, our best models achieved 86% AUC for
predicting tobacco use, 81% for alcohol use and 84% for drug use, all of which
significantly outperformed existing methods. Our investigation has also
uncovered interesting relations between a user's social media behavior (e.g.,
word usage) and substance use
MSAF: Multimodal Split Attention Fusion
Multimodal learning mimics the reasoning process of the human multi-sensory
system, which is used to perceive the surrounding world. While making a
prediction, the human brain tends to relate crucial cues from multiple sources
of information. In this work, we propose a novel multimodal fusion module that
learns to emphasize more contributive features across all modalities.
Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module
splits each modality into channel-wise equal feature blocks and creates a joint
representation that is used to generate soft attention for each channel across
the feature blocks. Further, the MSAF module is designed to be compatible with
features of various spatial dimensions and sequence lengths, suitable for both
CNNs and RNNs. Thus, MSAF can be easily added to fuse features of any unimodal
networks and utilize existing pretrained unimodal model weights. To demonstrate
the effectiveness of our fusion module, we design three multimodal networks
with MSAF for emotion recognition, sentiment analysis, and action recognition
tasks. Our approach achieves competitive results in each task and outperforms
other application-specific networks and multimodal fusion benchmarks