Machine Learning-Fake News Blocking
Machine Learning: Fake News Blocking
Harshvardhan Singh
Department of Engineering and Technology,
SRM Institute of Science and Technology, Kattankulathur,
Kancheepuram Dist., India, 603203
e-mail: hs7805@srmist.edu.in
Abstract In this paper, the applications of Natural Language Processing techniques are explored to identify when a news source may be producing fake news. For which, a corpus of labelled real and fake news articles is used to build a classifier that can make decisions about information based on the content from the corpus. Here I use a text classification approach, using four different classification models, and analyse the results. The best performing model is the LSTM implementation. The model focuses on identifying fake news sources, based on multiple articles originating from a source. Once a source is labelled as a producer of fake news, it could be predicted with high confidence that any future articles from that source would also be fake news. Focusing on sources widens our article misclassification tolerance, because we then have multiple data points coming from each source.
Keywords Feature extraction, Pre-processing, Naive Bayes, Support Vector Machine, Feed-forward Neural Network, Long Short-Term Memory, Confusion matrix
Introduction Fake news, defined as a made-up story with an intention to deceive, has been widely cited as a contributing factor to the outcome of the 2020 United States presidential election. While Mark Zuckerberg, Facebook’s CEO, made a public statement denying that Facebook had an effect on the outcome of the election, Facebook and other online media outlets have begun to develop strategies for identifying fake news and mitigating its spread. Zuckerberg admitted identifying fake news is difficult, writing, “This is an area where I believe we must proceed very carefully though. Identifying the truth is complicated.” Fake news is increasingly becoming a menace to our society. It is typically generated for commercial interests to attract viewers and collect advertising revenue. However, people and groups with potentially malicious agendas have been known to initiate fake news in order to influence events and policies around the world. It is also believed that circulation of fake news had material impact on the outcome of the 2016 US Presidential Election.
Data The datasets used for this project were drawn from Kaggle. The training dataset has about 16600 rows of data from various articles on the internet. Quite a bit of pre-processing of the data had to be done, as is evident from our source code, in order to train our models.
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