Stance-o-meter: A Data-driven Solution for Digital News Media and Journalism

In the contemporary age of digital media, journalists face challenges such as:

• » Lack of data-driven solutions to aid better, data-informed editorial decisions
• » Lack of access to basic information collection tools to reliably source news events
• » Editorial accountability since journalists often rely on their peers to evaluate the information objectively

We aim to build a stance-o-meter for editorial desks of media houses and journalists. Our ML, NLP, and AI-based solution can be an integral journalism tool in the digital era.

The stance-o-meter provides editorial desks with a tool to make data-informed editorial decisions rather than relying on traditional methods like manually keeping an eye on competitors. Some parameters that the stance-o-meter provides include polarity, subjectivity, the volume of news articles produced per topic, etc. It will help media houses clearly identify trends in the digital cycle, decide which topics to 'go big' on, insight into the political slant of their competitors, etc. It could also provide a score indicating an individual journalist's stance on a topic. This could improve the traditional method of peer review by being an empirical offshoot and can also help in journalist hiring decisions.

The stance-o-meter reveals the current general mood and leaning of journalism. With this data-driven solution, we move from unachievable objectivity to moral clarity and transparency.

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