Interpretable Fake News Detection with Topic and Deep Variational Models
- URL: http://arxiv.org/abs/2209.01536v1
- Date: Sun, 4 Sep 2022 05:31:00 GMT
- Title: Interpretable Fake News Detection with Topic and Deep Variational Models
- Authors: Marjan Hosseini, Alireza Javadian Sabet, Suining He, and Derek Aguiar
- Abstract summary: We focus on fake news detection using interpretable features and methods.
We have developed a deep probabilistic model that integrates a dense representation of textual news.
Our model achieves comparable performance to state-of-the-art competing models.
- Score: 2.15242029196761
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growing societal dependence on social media and user generated content
for news and information has increased the influence of unreliable sources and
fake content, which muddles public discourse and lessens trust in the media.
Validating the credibility of such information is a difficult task that is
susceptible to confirmation bias, leading to the development of algorithmic
techniques to distinguish between fake and real news. However, most existing
methods are challenging to interpret, making it difficult to establish trust in
predictions, and make assumptions that are unrealistic in many real-world
scenarios, e.g., the availability of audiovisual features or provenance. In
this work, we focus on fake news detection of textual content using
interpretable features and methods. In particular, we have developed a deep
probabilistic model that integrates a dense representation of textual news
using a variational autoencoder and bi-directional Long Short-Term Memory
(LSTM) networks with semantic topic-related features inferred from a Bayesian
admixture model. Extensive experimental studies with 3 real-world datasets
demonstrate that our model achieves comparable performance to state-of-the-art
competing models while facilitating model interpretability from the learned
topics. Finally, we have conducted model ablation studies to justify the
effectiveness and accuracy of integrating neural embeddings and topic features
both quantitatively by evaluating performance and qualitatively through
separability in lower dimensional embeddings.
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