20 research outputs found
CSI: A Hybrid Deep Model for Fake News Detection
The topic of fake news has drawn attention both from the public and the
academic communities. Such misinformation has the potential of affecting public
opinion, providing an opportunity for malicious parties to manipulate the
outcomes of public events such as elections. Because such high stakes are at
play, automatically detecting fake news is an important, yet challenging
problem that is not yet well understood. Nevertheless, there are three
generally agreed upon characteristics of fake news: the text of an article, the
user response it receives, and the source users promoting it. Existing work has
largely focused on tailoring solutions to one particular characteristic which
has limited their success and generality. In this work, we propose a model that
combines all three characteristics for a more accurate and automated
prediction. Specifically, we incorporate the behavior of both parties, users
and articles, and the group behavior of users who propagate fake news.
Motivated by the three characteristics, we propose a model called CSI which is
composed of three modules: Capture, Score, and Integrate. The first module is
based on the response and text; it uses a Recurrent Neural Network to capture
the temporal pattern of user activity on a given article. The second module
learns the source characteristic based on the behavior of users, and the two
are integrated with the third module to classify an article as fake or not.
Experimental analysis on real-world data demonstrates that CSI achieves higher
accuracy than existing models, and extracts meaningful latent representations
of both users and articles.Comment: In Proceedings of the 26th ACM International Conference on
Information and Knowledge Management (CIKM) 201
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
TransNets: Learning to Transform for Recommendation
Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017
Strong Resonance Effect in a Lossy MediumâBased Optical Cavity for Angle Robust Spectrum Filters
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108680/1/adma201402117.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108680/2/adma201402117-sup-0001-S1.pd