The landscape in which society interacts with news has evolved due to the advent of the internet and modern communication platforms. Although this evolution has led to greater diversity and accessibility of news media, it has also created challenges regarding selective news coverage, bias, and fake news. This work proposes a novel news platform called Liquid News that aims to enhance people’s understanding of news by leveraging machine-learning-based analysis and semantic navigational aids.
Over decades, news and how we interact with information have significantly evolved. This evolution has led to greater accessibility and connectivity with the rise of the internet. Still, it also has strengthened or introduced negative factors such as bias and fake news. This has culminated in a world where the ability to access and share information has become readily available; however, truly understanding the news and global events has become far more obscured due to the rampant rise of bias and fake news.
The intended end product of Liquid Movies News is an interface that allows users to parse news via a semantic-relational model that leverages the latent connection between news segments to garner a better understanding of the news at hand. For example, Queen Elizabeth II's death was heavily covered in the news/media. The information was focused on her death and related topics such as royal success, British history, the monarchy's wealth, etc. These topics relate to the Queen's death on a latent semantic-relational level and are essential to understanding her death's significance. However, these topics were covered across multiple news mediums and at varying depths, making it hard to identify and understand these latent connections. Liquid Movies aims to build an interface that uses machine learning to identify the key topics and parse, group, and relate news segments from many news sources, hopefully uncovering these latent relationships and promoting a greater understanding of the news and media around us.