Fake News Detection – Students Research in ML and DL at Durham College
The term "fake news" was pretty much unknown and unpopular a few decades ago, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days, and people share it without confirming it. Often, it is to promote or enforce specific views, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms.
In this digital age, it is not easy to tackle the spread of fake news, where thousands of information-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances, bringing with it artificial bots that may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spam or harmful emails and communications.
Fake news has grown in popularity and spread as a result of recent political events. Humans are inconsistent, if not outright terrible detectors of fake news, as evidenced by the pervasive effects of the widespread onset of fake news. As a result, efforts have been made to automate detecting fake news. The most prominent of these attempts are "blacklists" of unreliable sources and authors. While these technologies are useful we need to account for more complex instances when trusted sources and authors leak fake news in order to provide a complete end-to-end solution. As a result, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classification decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it.
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- Date: 26 Apr 2022
- Time: 06:00 PM to 07:00 PM
- All times are (GMT-05:00) Canada/Eastern
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- Toronto, Ontario
- Canada
Speakers
Roshna Babu
Fake News Detection – Students Research in ML and DL at Durham College
Speakers: Roshna Babu, Abraham Mathew, Neha Joseph
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