TL;DR
This paper introduces a framework that searches for fact-checking articles related to social media posts containing misinformation, aiming to reduce the spread of fake news by informing and discouraging users.
Contribution
It presents a novel multi-modal search framework combining text and image analysis to find relevant fact-checking articles for social media content.
Findings
Achieves promising results on real-world datasets
Effectively links misinformation to fact-checking content
Potential to scale up verified information on social media
Abstract
Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking…
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Taxonomy
MethodsLinear Layer · Tanh Activation · Sigmoid Activation · *Communicated@Fast*How Do I Communicate to Expedia? · 1x1 Convolution · Batch Normalization · Long Short-Term Memory · Residual Connection · Weight Decay · Attention Dropout
