60,584 research outputs found
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
Hy-DeFake: Hypergraph Neural Networks for Detecting Fake News in Online Social Networks
Nowadays social media is the primary platform for people to obtain news and
share information. Combating online fake news has become an urgent task to
reduce the damage it causes to society. Existing methods typically improve
their fake news detection performances by utilizing textual auxiliary
information (such as relevant retweets and comments) or simple structural
information (i.e., graph construction). However, these methods face two
challenges. First, an increasing number of users tend to directly forward the
source news without adding comments, resulting in a lack of textual auxiliary
information. Second, simple graphs are unable to extract complex relations
beyond pairwise association in a social context. Given that real-world social
networks are intricate and involve high-order relations, we argue that
exploring beyond pairwise relations between news and users is crucial for fake
news detection. Therefore, we propose constructing an attributed hypergraph to
represent non-textual and high-order relations for user participation in news
spreading. We also introduce a hypergraph neural network-based method called
Hy-DeFake to overcome the challenges. Our proposed method captures semantic
information from news content, credibility information from involved users, and
high-order correlations between news and users to learn distinctive embeddings
for fake news detection. The superiority of Hy-DeFake is demonstrated through
experiments conducted on four widely-used datasets, and it is compared against
six baselines using four evaluation metrics
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