TweetCred: Real-Time Credibility Assessment of Content on Twitter

  title={TweetCred: Real-Time Credibility Assessment of Content on Twitter},
  author={Aditi Gupta and Ponnurangam Kumaraguru and Carlos Castillo and Patrick Meier},
  booktitle={Social Informatics},
During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets. [] Key Method This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us…

CredFinder: A real-time tweets credibility assessing system

CredFinder is a real-time content credibility assessment system capable of measuring the trustworthiness of information through user analysis and content analysis and is capable of providing a credibility score for each user's tweets.

A Text Mining Approach for Evaluating Event Credibility on Twitter

  • D. Hassan
  • Computer Science
    2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)
  • 2018
A text mining approach for automatic evaluation of events on social networks using events extracted from the CREDBANK dataset and a corpus of tweets annotated with human credibility judgements shows that the approach is promising.

CbI: Improving Credibility of User-Generated Content on Facebook

This paper proposes a system which calculates the Accuracy, Clarity, and Timeliness (A-C-T) of a Facebook post which in turn are used to rank the post for its credibility.

Credibility Analysis on Twitter Considering Topic Detection

An extension of the previous credibility model is proposed by integrating the detection of the topic in the tweet and calculating the topic credibility measure by considering hashtags, which demonstrates an improvement in the extended credibility model with respect to the original one.

Extreme User and Political Rumor Detection on Twitter

This paper proposes a rule-based method for detecting political rumors on Twitter based on identifying extreme users, and employs clustering methods to identify news tweets and unsupervised classification methods for the detection of extreme users.

Information Credibility on Twitter Using Machine Learning Techniques

A machine learning model has been developed to detect the credibility of tweets over four distinct credibility classes and build a credibility analysis model which can filter out all such uncredible and questionable contents from social media.

CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations

CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements designed to bridge the gap between machine and human computation in online information credibility in fields such as social science, data mining and health.

Investigating Rumor Propagation with TwitterTrails

An interactive, web-based tool that allows users to investigate the origin and propagation characteristics of a rumor and its refutation, if any, on Twitter and its expanding collection of investigated rumors can be used to answer questions regarding the amount and success of misinformation on Twitter.

A Credibility Analysis System for Assessing Information on Twitter

A new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information is proposed and reveals that a significant balance between recall and precision was achieved for the tested dataset.

Seeing and Believing: Evaluating the Trustworthiness of Twitter Users

This work created a model which, it hope, will ultimately facilitate and support the increase of trust in the social network communities and collected data and analysed the behaviour of 50,000 politicians on Twitter.



Information credibility on twitter

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%.

Credibility in Context: An Analysis of Feature Distributions in Twitter

An analysis that highlights the utility of the individual features in Twitter such as hash tags, retweets and mentions for predicting credibility shows that the best indicators of credibility include URLs, mentions, retwets and tweet length and that features occur more prominently in data describing emergency and unrest situations.

Information Credibility on Twitter in Emergency Situation

A novel Twitter monitor model to monitoring Twitter online and an unsupervised learning algorithm is proposed to detect the emergency situation and a supervised method using learning Bayesian Network is used to predict the tweets credibility in emergency situation.

Credibility ranking of tweets during high impact events

Results show that extraction of credible information from Twitter can be automated with high confidence and the performance of the ranking algorithm significantly enhanced when it applied re-ranking strategy.

$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter

The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral on Twitter, and found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content.

Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy

The role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster was highlighted, and automated techniques can be used in identifying real images from fake images posted on Twitter.

Tweeting is believing?: understanding microblog credibility perceptions

It is shown that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments.

PhishAri: Automatic realtime phishing detection on twitter

This research shows that PhishAri is able to detect phishing tweets at zero hour with high accuracy which is much faster than public blacklists and as well as Twitter's own defense mechanism to detect malicious content.

Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

An extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events is introduced.

Twitter under crisis: can we trust what we RT?

The behavior of Twitter users under an emergency situation is explored and it is shown that it is posible to detect rumors by using aggregate analysis on tweets, and that the propagation of tweets that correspond to rumors differs from tweets that spread news.