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Proceedings ArticleDOI

Information credibility on twitter

TLDR
There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.
Abstract
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.

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Book ChapterDOI

Multimedia Social Big Data: Mining

TL;DR: The work presented is an extensive and organized overview of the multimedia social big data mining and applications, including a comprehensive coverage of the taxonomy, types and techniques of Multimedia Social Big Data mining.
Journal ArticleDOI

EchoFakeD: improving fake news detection in social media with an efficient deep neural network

TL;DR: In this paper, a deep learning model with tensor factorization approach is proposed to detect fake news. But, the authors focus on both the content of the news article and the existence of echo chambers in the social network for fake news detection.
Posted ContentDOI

Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning

TL;DR: This article proposed a preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern-and fact-based models for joint detection, and then uses these maps to guide the joint learning of pattern and fact based models for final prediction.
Journal ArticleDOI

Hoax news-inspector: a real-time prediction of fake news using content resemblance over web search results for authenticating the credibility of news articles

TL;DR: An automated system Hoax News-Inspector that can automatically collect fabricated news data and classify it into binary classes Fake or Real, which later benefits further research for predicting and understanding Fake news.
References
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Proceedings ArticleDOI

What is Twitter, a social network or a news media?

TL;DR: In this paper, the authors have crawled the entire Twittersphere and found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
Proceedings ArticleDOI

Earthquake shakes Twitter users: real-time event detection by social sensors

TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Proceedings ArticleDOI

Why we twitter: understanding microblogging usage and communities

TL;DR: It is found that people use microblogging to talk about their daily activities and to seek or share information and the user intentions associated at a community level are analyzed to show how users with similar intentions connect with each other.
Proceedings ArticleDOI

Microblogging during two natural hazards events: what twitter may contribute to situational awareness

TL;DR: Analysis of microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service, aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.
Proceedings ArticleDOI

Finding high-quality content in social media

TL;DR: This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.
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