184 research outputs found
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
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
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
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