Fake News Detection Tools and Methods -- A Review
- URL: http://arxiv.org/abs/2112.11185v1
- Date: Sun, 21 Nov 2021 13:19:23 GMT
- Title: Fake News Detection Tools and Methods -- A Review
- Authors: Sakshini Hangloo and Bhavna Arora
- Abstract summary: We discuss the recent literature about different approaches to detect fake news over the Internet.
We highlight the various publicly available datasets and various online tools that are available and can debunk Fake News in real-time.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the past decade, the social networks platforms and micro-blogging sites
such as Facebook, Twitter, Instagram, and Weibo have become an integral part of
our day-to-day activities and is widely used all over the world by billions of
users to share their views and circulate information in the form of messages,
pictures, and videos. These are even used by government agencies to spread
important information through their verified Facebook accounts and official
Twitter handles, as they can reach a huge population within a limited time
window. However, many deceptive activities like propaganda and rumor can
mislead users on a daily basis. In these COVID times, fake news and rumors are
very prevalent and are shared in a huge number which has created chaos in this
tough time. And hence, the need for Fake News Detection in the present scenario
is inevitable. In this paper, we survey the recent literature about different
approaches to detect fake news over the Internet. In particular, we firstly
discuss fake news and the various terms related to it that have been considered
in the literature. Secondly, we highlight the various publicly available
datasets and various online tools that are available and can debunk Fake News
in real-time. Thirdly, we describe fake news detection methods based on two
broader areas i.e., its content and the social context. Finally, we provide a
comparison of various techniques that are used to debunk fake news.
Related papers
- Adapting Fake News Detection to the Era of Large Language Models [48.5847914481222]
We study the interplay between machine-(paraphrased) real news, machine-generated fake news, human-written fake news, and human-written real news.
Our experiments reveal an interesting pattern that detectors trained exclusively on human-written articles can indeed perform well at detecting machine-generated fake news, but not vice versa.
arXiv Detail & Related papers (2023-11-02T08:39:45Z) - Fake News Detection and Behavioral Analysis: Case of COVID-19 [0.22940141855172028]
"Infodemic" due to spread of fake news regarding the pandemic has been a global issue.
Readers could mistake fake news for real news, and consequently have less access to authentic information.
It is challenging to accurately identify fake news data in social media posts.
arXiv Detail & Related papers (2023-05-25T13:42:08Z) - Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
Multiverse is a new feature based on multilingual evidence that can be used for fake news detection.
The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
arXiv Detail & Related papers (2022-11-25T18:24:17Z) - FALSE: Fake News Automatic and Lightweight Solution [0.20999222360659603]
In this paper, R code have been used to study and visualize a modern fake news dataset.
We use clustering, classification, correlation and various plots to analyze and present the data.
arXiv Detail & Related papers (2022-08-16T11:53:30Z) - Faking Fake News for Real Fake News Detection: Propaganda-loaded
Training Data Generation [105.20743048379387]
We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda.
Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles.
Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
arXiv Detail & Related papers (2022-03-10T14:24:19Z) - Is it Fake? News Disinformation Detection on South African News Websites [0.015863809575305417]
Natural Language Processing is widely used in detecting fake news.
It is especially a problem in more localised contexts such as in South Africa.
In this work we investigate fake news detection on South African websites.
arXiv Detail & Related papers (2021-08-06T04:54:03Z) - User Preference-aware Fake News Detection [61.86175081368782]
Existing fake news detection algorithms focus on mining news content for deceptive signals.
We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling.
arXiv Detail & Related papers (2021-04-25T21:19:24Z) - The Rise and Fall of Fake News sites: A Traffic Analysis [62.51737815926007]
We investigate the online presence of fake news websites and characterize their behavior in comparison to real news websites.
Based on our findings, we build a content-agnostic ML for automatic detection of fake news websites.
arXiv Detail & Related papers (2021-03-16T18:10:22Z) - How does Truth Evolve into Fake News? An Empirical Study of Fake News
Evolution [55.27685924751459]
We present the Fake News Evolution dataset: a new dataset tracking the fake news evolution process.
Our dataset is composed of 950 paired data, each of which consists of articles representing the truth, the fake news, and the evolved fake news.
We observe the features during the evolution and they are the disinformation techniques, text similarity, top 10 keywords, classification accuracy, parts of speech, and sentiment properties.
arXiv Detail & Related papers (2021-03-10T09:01:34Z) - Where Are the Facts? Searching for Fact-checked Information to Alleviate
the Spread of Fake News [9.68145635795782]
We propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users.
The search can directly warn fake news posters and online users about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets.
arXiv Detail & Related papers (2020-10-07T04:55:34Z) - Fake News Spreader Detection on Twitter using Character N-Grams.
Notebook for PAN at CLEF 2020 [0.0]
This notebook describes our profiling system for the fake news detection task on Twitter.
We conduct different feature extraction techniques and learning experiments from a multilingual perspective.
Our models achieve an overall accuracy of 73% and 79% on the English and Spanish official test set.
arXiv Detail & Related papers (2020-09-29T08:32:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.