31,406 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
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
During the 2016 US elections Twitter experienced unprecedented levels of
propaganda and fake news through the collaboration of bots and hired persons,
the ramifications of which are still being debated. This work proposes an
approach to identify the presence of organized behavior in tweets. The Random
Forest, Support Vector Machine, and Logistic Regression algorithms are each
used to train a model with a data set of 850 records consisting of 299 features
extracted from tweets gathered during the 2016 US presidential election. The
features represent user and temporal synchronization characteristics to capture
coordinated behavior. These models are trained to classify tweet sets among the
categories: organic vs organized, political vs non-political, and pro-Trump vs
pro-Hillary vs neither. The random forest algorithm performs better with
greater than 95% average accuracy and f-measure scores for each category. The
most valuable features for classification are identified as user based
features, with media use and marking tweets as favorite to be the most
dominant.Comment: 51 pages, 5 figure
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Document Filtering for Long-tail Entities
Filtering relevant documents with respect to entities is an essential task in
the context of knowledge base construction and maintenance. It entails
processing a time-ordered stream of documents that might be relevant to an
entity in order to select only those that contain vital information.
State-of-the-art approaches to document filtering for popular entities are
entity-dependent: they rely on and are also trained on the specifics of
differentiating features for each specific entity. Moreover, these approaches
tend to use so-called extrinsic information such as Wikipedia page views and
related entities which is typically only available only for popular head
entities. Entity-dependent approaches based on such signals are therefore
ill-suited as filtering methods for long-tail entities. In this paper we
propose a document filtering method for long-tail entities that is
entity-independent and thus also generalizes to unseen or rarely seen entities.
It is based on intrinsic features, i.e., features that are derived from the
documents in which the entities are mentioned. We propose a set of features
that capture informativeness, entity-saliency, and timeliness. In particular,
we introduce features based on entity aspect similarities, relation patterns,
and temporal expressions and combine these with standard features for document
filtering. Experiments following the TREC KBA 2014 setup on a publicly
available dataset show that our model is able to improve the filtering
performance for long-tail entities over several baselines. Results of applying
the model to unseen entities are promising, indicating that the model is able
to learn the general characteristics of a vital document. The overall
performance across all entities---i.e., not just long-tail entities---improves
upon the state-of-the-art without depending on any entity-specific training
data.Comment: CIKM2016, Proceedings of the 25th ACM International Conference on
Information and Knowledge Management. 201
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