Fake Drivers Abstract

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Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

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The Commercial Driver Abstract (CDA) provides industry with accurate information on new and current commercial drivers. Previously, the only information available was found in the current driver abstract and information the drivers were willing to share about their driving history. The DPPA prohibits the release of personal driving record abstract information without DPPA permissible use. The Arizona Motor Vehicle Record Request (Form 46-4416) lists all accepted uses. If an employer or other organization requests your AZ driving record, you can give a one-time consent OR you may provide ongoing (general) consent to every. Hey Laowinners!I came across a pretty alarming news story coming from Canada's public news network, the CBC which has literally been caught parroting the Com.

Nguyen Vo,Kyumin Lee

DriversAbstract
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 articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.
Anthology ID:
2020.emnlp-main.621
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7717–7731
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.621
DOI:
10.18653/v1/2020.emnlp-main.621
PDF:
https://www.aclweb.org/anthology/2020.emnlp-main.621.pdf

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Nguyen Vo,Kyumin Lee

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 articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.

Fake Drivers Abstract Example

Abstract

Fake Drivers Abstract Template

Anthology ID:
2020.emnlp-main.621
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7717–7731
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.621
DOI:
10.18653/v1/2020.emnlp-main.621
PDF:
https://www.aclweb.org/anthology/2020.emnlp-main.621.pdf