2 research outputs found
Tensor Factorization with Label Information for Fake News Detection
The buzz over the so-called "fake news" has created concerns about a
degenerated media environment and led to the need for technological solutions.
As the detection of fake news is increasingly considered a technological
problem, it has attracted considerable research. Most of these studies
primarily focus on utilizing information extracted from textual news content.
In contrast, we focus on detecting fake news solely based on structural
information of social networks. We suggest that the underlying network
connections of users that share fake news are discriminative enough to support
the detection of fake news. Thereupon, we model each post as a network of
friendship interactions and represent a collection of posts as a
multidimensional tensor. Taking into account the available labeled data, we
propose a tensor factorization method which associates the class labels of data
samples with their latent representations. Specifically, we combine a
classification error term with the standard factorization in a unified
optimization process. Results on real-world datasets demonstrate that our
proposed method is competitive against state-of-the-art methods by implementing
an arguably simpler approach.Comment: Presented at the Workshop on Reducing Online Misinformation Exposure
ROME 201
Broad Learning for Healthcare
A broad spectrum of data from different modalities are generated in the
healthcare domain every day, including scalar data (e.g., clinical measures
collected at hospitals), tensor data (e.g., neuroimages analyzed by research
institutes), graph data (e.g., brain connectivity networks), and sequence data
(e.g., digital footprints recorded on smart sensors). Capability for modeling
information from these heterogeneous data sources is potentially transformative
for investigating disease mechanisms and for informing therapeutic
interventions.
Our works in this thesis attempt to facilitate healthcare applications in the
setting of broad learning which focuses on fusing heterogeneous data sources
for a variety of synergistic knowledge discovery and machine learning tasks. We
are generally interested in computer-aided diagnosis, precision medicine, and
mobile health by creating accurate user profiles which include important
biomarkers, brain connectivity patterns, and latent representations. In
particular, our works involve four different data mining problems with
application to the healthcare domain: multi-view feature selection, subgraph
pattern mining, brain network embedding, and multi-view sequence prediction.Comment: PhD Thesis, University of Illinois at Chicago, March 201