Fake News Detection with Semantic Features and Text Mining

@article{Bharadwaj2019FakeND,
  title={Fake News Detection with Semantic Features and Text Mining},
  author={Pranav Bharadwaj and Zongru Shao},
  journal={International Journal on Natural Language Computing},
  year={2019}
}
Nearly 70% of people are concerned about the propagation of fake news. This paper aims to detect fake news in online articles through the use of semantic features and various machine learning techniques. In this research, we investigated recurrent neural networks vs. the naive bayes classifier and random forest classifiers using five groups of linguistic features. Evaluated with real or fake dataset from kaggle.com, the best performing model achieved an accuracy of 95.66% using bigram features… 

Figures and Tables from this paper

Content Based Fake News Detection Using N-Gram Models
TLDR
This paper proposes the fake news detection system that considers the content of the online news articles and investigates two machine learning algorithms with the use of word n-grams and character n- grams analysis.
Fake News Detection Using Machine Learning and Deep Learning Algorithms
TLDR
It is shown that fake news with textual content can indeed be classified, especially using a convolutional neural network, as well as four traditional methods applied to extract features from texts.
An Ensemble Technique to Detect Fabricated News Article Using Machine Learning and Natural Language Processing Techniques
TLDR
The paper focuses on sources of articles to widen misclassification tolerance and make more accurate predictions, and presents various ensemble techniques to perform the binary classification of news articles.
Mining Text Patterns over Fake and Real Tweets
With the exponential growth of users and user-generated content present on online social networks, fake news and its detection have become a major problem. Through these, smear campaigns can be
Using a Rule-based Model to Detect Arabic Fake News Propagation during Covid-19
TLDR
The result demonstrates that as more information and knowledge about Covid-19 become available over time, people's awareness increase, while the number of fake news tweets decreases, and the categorization of false news indicates that the social category was highest in all Arab countries except Palestine, Qatar, Yemen, and Algeria.
Fake News Detection Using BERT Model with Joint Learning
TLDR
This work purpose a novel BERT approach with joint learning framework that combines relational features classification (RFC) and named entity recognition (NER) that provides a meaningful weight to attributes, which leads to better performance compared to other baselines.
Fake News Detection on Social Media: A Systematic Survey
TLDR
A systematic survey on the process of fake news detection on social media is introduced and the types of data and the categories of features used in the detection model, as well as benchmark datasets are discussed.
Defining News Authenticity on Social Media Using Machine Learning Approach
TLDR
This paper proposes an approach to detect fake news on social media that covers both news content and social context and uses synonym-based feature extraction method and three different classifiers based on multidimensional dataset.
...
1
2
...

References

SHOWING 1-10 OF 15 REFERENCES
Deception detection for news: Three types of fakes
TLDR
Three types of fake news are discussed, each in contrast to genuine serious reporting, and their pros and cons as a corpus for text analytics and predictive modeling are weighed.
Fake News Detection on Social Media: A Data Mining Perspective
TLDR
This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
TLDR
This paper presents liar: a new, publicly available dataset for fake news detection, and designs a novel, hybrid convolutional neural network to integrate meta-data with text to improve a text-only deep learning model.
GloVe: Global Vectors for Word Representation
TLDR
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Recurrent neural network based language model
TLDR
Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model.
The Fake News Machine: Inside a Town Gearing up for 2020.” CNNMoney, Cable News Network, money.cnn.com/interactive/media/the-macedonia- story
  • 2020
Bitcoin Bulls Spreading Fake News About Golden Asteroid: Peter Schiff.
  • NewsBTC, NewsBTC,
  • 2019
Many Americans Say Made-Up News Is a Critical Problem That Needs To Be Fixed.
  • Pew Research Center's Journalism Project, Pew Research Center,
  • 2019
Mark zuckerberg lays out facebooks 3-pronged approach to fake news
  • 2018
...
1
2
...