Detecting fake news for reducing misinformation risks using analytics approaches

  title={Detecting fake news for reducing misinformation risks using analytics approaches},
  author={Chaowei Zhang and Ashish Gupta and Christian Kauten and A. Deokar and Xiao Qin},
  journal={Eur. J. Oper. Res.},

Natural Language Processing based Online Fake News Detection Challenges – A Detailed Review

  • V. HirlekarArun Kumar
  • Computer Science
    2020 5th International Conference on Communication and Electronics Systems (ICCES)
  • 2020
The general approach of fake news detection as well as taxonomy of feature extraction which plays an important role to achieve maximum accuracy with the help of different Machine Learning and Natural Language Processing algorithms are discussed.

A Literature Review of NLP Approaches to Fake News Detection and Their Applicability to Romanian-Language News Analysis

This study reference existing datasets and related work in the analysis of English fake news, discuss potential detection techniques for Romanianfake news, as well as establish future work plans for this research initiative.

Fake News Detection: Covid-19 Perspective

This work has collected a new dataset for detecting fake news from traditional media on Covid-19, and merged 170 fake news with four scales of true news and analyzed the outcome.

A Systematic Review on the Detection of Fake News Articles

The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective and it is proposed that future research should focus on developing approaches that address generalisability issues, explainability and bias.

Evaluation of Tools and Extension for Fake News Detection

The general public will know the basic techniques for fake news identification and several Fact-checking websites are discussed here to help social media users verify the information present in Social-media.

A Review on Fake News Detection with Machine Learning

  • Swati R. Khokale
  • Computer Science
    International Journal for Research in Applied Science and Engineering Technology
  • 2021
This paper reviews various Machine learning approaches in detection of fake and real news.

Fake News Detection Approach Using Parallel Predictive Models and Spark to Avoid Misinformation Related to Covid-19 Epidemic

A classification model based on machine learning and deep learning algorithms to classify COVID-19 tweets into two classes using Apache Spark and the Python API Tweepy is proposed, which uses the features of tweets to detect fake news.

An Unsupervised Misinformation Detection Framework to Analyze the Users using COVID-19 Twitter Data

This research work leverages the popularity of the tweets in form of re-posts to identify the influential content on the social media and proposes an unsupervised framework to detect misinformed content and the users who may be the sources of or susceptible to spreading misinformedcontent.

Detecting Fake News on Social Media

  • Esra Bozkanat
  • Business, Computer Science
    Advances in Social Networking and Online Communities
  • 2021
The current study shows fake news to be detectable based on four features: Propagation, User Type, Social Media Type, and Formatting, and demonstrates that Facebook increases the likelihood of news being fake compared to Twitter or Instagram.

Exploring Roles of Emotion in Fake News Detection

This paper demonstrates effective representations of emotions within both news content and users’ comments and proposes an emotion-aware fake news detection framework that seamlessly incorporates emotion features to enhance the accuracy of identifying fake news.



Fake News vs Satire: A Dataset and Analysis

This work presents a dataset of fake news and satire stories that are hand coded, verifiable, and, in the case offake news, include rebutting stories, and includes a thematic content analysis of the articles, identifying major themes that include hyperbolic support or con- demnation of a gure, conspiracy theories, racist themes, and dis- crediting of reliable sources.

News Credibility Evaluation on Microblog with a Hierarchical Propagation Model

This work proposes a hierarchical propagation model for evaluating news credibility on Micro blog by formulating this propagation process as a graph optimization problem, and provides a globally optimal solution with an iterative algorithm.

Detecting Fraudulent Behavior on Crowdfunding Platforms: The Role of Linguistic and Content-Based Cues in Static and Dynamic Contexts

This study analyzes a sample of fraudulent and nonfraudulent projects published at a leading crowdfunding platform and investigates whether content-based cues and linguistic cues are valuable for fraud detection.

Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 U.S. presidential campaign

Though some warnings about online “echo chambers” have been hyperbolic, tendencies toward selective exposure to politically congenial content are likely to extend to misinformation and to be

Rumor has it: Identifying Misinformation in Microblogs

This paper addresses the problem of rumor detection in microblogs and explores the effectiveness of 3 categories of features: content- based, network-based, and microblog-specific memes for correctly identifying rumors, and believes that its dataset is the first large-scale dataset on rumor detection.

Fake News: A Legal Perspective

The concept of “fake news” has garnered substantial attention in recent years, evolving from its satirical literary origins into a passionately criticized Internet phenomenon. Whether described as

Automatic deception detection: Methods for finding fake news

This research surveys the current state‐of‐the‐art technologies that are instrumental in the adoption and development of fake news detection, as well as various formats and genres.

Social Media and Fake News in the 2016 Election

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake

Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking

Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.