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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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Journal ArticleDOI

Using geosocial search for urban air pollution monitoring

TL;DR: The feasibility to use a geographical search on social networks, that is, a geosocial search, about air pollution related posts, as effective air impureness measurements is explored and the time series of posts returned by the geossocial search is proposed to predict next pollution values.
Posted Content

Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection

TL;DR: This paper designs a sifted multi-task learning method with a selected sharing layer for fake news detection that achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%.
Proceedings ArticleDOI

LogUCB: an explore-exploit algorithm for comments recommendation

TL;DR: A novel upper confidence bound (UCB) algorithm called LOGUCB that balances exploration with exploitation when the average rating of a comment is modeled using logistic regression on its features, which outperforms state-of-the-art explore-exploit algorithms.
Journal ArticleDOI

Geographic variability of Twitter usage characteristics during disaster events

TL;DR: This research examines tweeting activity during two earthquakes in Italy and Myanmar, and compares the granularity of geographic references used, user profile characteristics that are related to credibility, and the performance of Naïve Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.
Proceedings ArticleDOI

Towards automatic real time identification of malicious posts on Facebook

TL;DR: This paper characterized a dataset of 4.4 million public posts generated on Facebook during 17 news-making events and identified 11,217 malicious posts containing URLs and proposed an extensive feature set based on entity profile, textual content, metadata, and URL features to automatically identify malicious content on Facebook in real time.
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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