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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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Citations
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The source of the story: Evaluating the credibility of crisis information sources

TL;DR: Analysis and findings indicate that for crisis information about natural disasters, people tend to trust traditional media channels, such as printed news, and televised news, while the type of social tie also influences the perceived credibility of the crisis information.
Proceedings ArticleDOI

Tweet credibility analysis evaluation by improving sentiment dictionary

TL;DR: A method for extracting sentiment orientations of words and phrases is proposed to improve the evaluation for analyzing the credibility of tweet information and evolutionally learns from a large amount of social data on Twitter.
Journal ArticleDOI

What Motivates People to Correct Misinformation? Examining the Effects of Third-person Perceptions and Perceived Norms

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Image Credibility Analysis with Effective Domain Transferred Deep Networks

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Detecting malicious short URLs on Twitter

TL;DR: A mechanism to develop a machine learning classifier to detect malicious short URLs with visible content features, tweet context, and social features from one popular Online Social Network Twitter is proposed.
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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