1,359 research outputs found
A Topic-Agnostic Approach for Identifying Fake News Pages
Fake news and misinformation have been increasingly used to manipulate
popular opinion and influence political processes. To better understand fake
news, how they are propagated, and how to counter their effect, it is necessary
to first identify them. Recently, approaches have been proposed to
automatically classify articles as fake based on their content. An important
challenge for these approaches comes from the dynamic nature of news: as new
political events are covered, topics and discourse constantly change and thus,
a classifier trained using content from articles published at a given time is
likely to become ineffective in the future. To address this challenge, we
propose a topic-agnostic (TAG) classification strategy that uses linguistic and
web-markup features to identify fake news pages. We report experimental results
using multiple data sets which show that our approach attains high accuracy in
the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019
World Wide Web Conference (WWW'19 Companion). Presented in the 2019
International Workshop on Misinformation, Computational Fact-Checking and
Credible Web (MisinfoWorkshop2019). 6 page
Rumor Detection on Social Media: Datasets, Methods and Opportunities
Social media platforms have been used for information and news gathering, and
they are very valuable in many applications. However, they also lead to the
spreading of rumors and fake news. Many efforts have been taken to detect and
debunk rumors on social media by analyzing their content and social context
using machine learning techniques. This paper gives an overview of the recent
studies in the rumor detection field. It provides a comprehensive list of
datasets used for rumor detection, and reviews the important studies based on
what types of information they exploit and the approaches they take. And more
importantly, we also present several new directions for future research.Comment: 10 page
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
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long
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