@inproceedings{iftene-etal-2020-real,
title = "A Real-Time System for Credibility on {T}witter",
author = "Iftene, Adrian and
Gifu, Daniela and
Miron, Andrei-Remus and
Dudu, Mihai-Stefan",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.757",
pages = "6166--6173",
abstract = "Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.</abstract>
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%0 Conference Proceedings
%T A Real-Time System for Credibility on Twitter
%A Iftene, Adrian
%A Gifu, Daniela
%A Miron, Andrei-Remus
%A Dudu, Mihai-Stefan
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F iftene-etal-2020-real
%X Nowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity of social networks, more and more users began posting pictures, information, and news about personal life. At the same time, they started to use all this information to get informed about what their friends do or what is happening in the world, many of them arousing much suspicion. The problem we are currently experiencing is that we do not currently have an automatic method of figuring out in real-time which news or which users are credible and which are not, what is false or what is true on the Internet. The goal of this is to analyze Twitter in real-time using neural networks in order to provide us key elements about both the credibility of tweets and users who posted them. Thus, we make a real-time heatmap using information gathered from users to create overall images of the areas from which this fake news comes.
%U https://aclanthology.org/2020.lrec-1.757
%P 6166-6173
Markdown (Informal)
[A Real-Time System for Credibility on Twitter](https://aclanthology.org/2020.lrec-1.757) (Iftene et al., LREC 2020)
ACL
- Adrian Iftene, Daniela Gifu, Andrei-Remus Miron, and Mihai-Stefan Dudu. 2020. A Real-Time System for Credibility on Twitter. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6166–6173, Marseille, France. European Language Resources Association.