LibGuides: Four Moves to Avoid Misinformation: 4.Trace Back to the Source
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Learn how to use the SIFT method to evaluate sources online and steer clear of misinformation, disinformation, and mal-information!Keywords:
Misinformation
TRACE (psycholinguistics)
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Find resources for evaluating news sources and identifying disinformation and misinformation.
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The threat posed by misinformation and disinformation is one of the defining challenges of the 21st century. Provenance is designed to help combat this threat by warning users when the content they are looking at may be misinformation or disinformation. It is also designed to improve media literacy among its users and ultimately reduce susceptibility to the threat among vulnerable groups within society. The Provenance browser plugin checks the content that users see on the Internet and social media and provides warnings in their browser or social media feed. Unlike similar plugins, which require human experts to provide evaluations and can only provide simple binary warnings, Provenance's state of the art technology does not require human input and it analyses seven aspects of the content users see and provides warnings where necessary.
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The rise in online misinformation in recent years threatens democracies by distorting authentic public discourse and causing confusion, fear, and even, in extreme cases, violence. There is a need to understand the spread of false content through online networks for developing interventions that disrupt misinformation before it achieves virality. Using a Deep Bidirectional Transformer for Language Understanding (BERT) and propagation graphs, this study classifies and visualizes the spread of misinformation on a social media network using publicly available Twitter data. The results confirm prior research around user clusters and the virality of false content while improving the precision of deep learning models for misinformation detection. The study further demonstrates the suitability of BERT for providing a scalable model for false information detection, which can contribute to the development of more timely and accurate interventions to slow the spread of misinformation in online environments.
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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 articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.
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Learn how to use the SIFT method to evaluate sources online and steer clear of misinformation, disinformation, and mal-information!
Misinformation
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This guide provides tips, fact checking websites, and resources to help you discern whether the news and other information you see, read, and hear about is real or fake.
Misinformation
Fake News
2019-20 coronavirus outbreak
Coronavirus
Pandemic
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Fake news is false information about current events, intentionally created to mislead readers. The spread of such fake news has the potential to create a negative impact on individuals and society. With today's straightforward creation of social media posts, there has been an increasing amount of fake news, compared to traditional media in the past. We present one of the most serious societal issue of misinformation, specifically using Presidential Election and COVID-19 health related fake news. We present multi-dimensional approaches that organizations and individuals could utilize for detecting fake news, ranging from human/social approaches, to technical approaches to organizational trust/policy approaches. The Machine Learning approach as a technical solution is presented for automating the detection of fake news and misleading contents. A fake news detection web application is presented to make it easy for end users to determine whether an article is legitimate or fake.
Misinformation
Fake News
Disinformation
Presidential election
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Learn how to use the SIFT method to evaluate sources online and steer clear of misinformation, disinformation, and mal-information!
Misinformation
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