Abstract

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. Besides other use cases, news outlets benefitted from the widespread use of social media platforms by providing updated news in near real time to its subscribers. The news media evolved from newspapers, tabloids, and magazines to a digital form such as online news platforms, blogs, social media feeds, and other digital media formats. It became easier for consumers to acquire the latest news at their fingertips. Facebook referrals account for 70% of traffic to news websites. These social media platforms in their current state are extremely powerful and useful for their ability to allow users to discuss and share ideas and debate over issues such as democracy, education, and health. However, such platforms are also used with a negative perspective by certain entities commonly for monetary gain and in other cases for creating biased opinions, manipulating mindsets, and spreading satire or absurdity. The phenomenon is commonly known as fake news. There has been a rapid increase in the spread of fake news in the last decade, most prominently observed in the 2016 US elections. Such proliferation of sharing articles online that do not conform to facts has led to many problems not just limited to politics but covering various other domains such as sports, health, and science. One such area affected by fake news is the financial markets, where a rumour can have disastrous consequences and may bring the market to a halt. Therefore, there is a need for an automated way to classify fake and real news accurately. Some studies have been conducted but still there is a need for further attention and exploration. The proposed work attempts to eliminate the spread of rumours and fake news and helps people to identify the news source as trustworthy or not by automatically classifying the news. Initially, N-gram Feature Selection is used to extract the optimal features from the dataset. Then, long short-term memory (LSTM) is used to perform the classification operation.

Description