Thanks to the internet, we can now gather information from around the world in real time. Many researchers use online news articles to analyse how people perceive wildlife. However, using keyword searches could lead to mistakes — articles like Toronto Blue Jays Score Season High would be put in the wildlife category, while clearly referring to sports. We will be comparing human responses and machine learning models to develop a tool that can quickly identify content about wildlife. This will help conservation scientists more accurately use media to understand what motivates people to care about wildlife and act to protect it.

Help us create a data set to evaluate various language models' accuracy. By sorting through article titles and choosing those related to wildlife you will provide us with a crucial piece of information — the ground truth data! This means that your answers would be used as a reference point for machine learning model evaluation. By comparing human classifications to those from machine learning models, we can make conclusions about the effectiveness of these AI tools. The more accurate the tool, the easier it will be to see how people engage with wildlife, and how those sentiments change over time.

Understanding how people feel about wildlife can make a big difference in conservation efforts. When people care about animals, they’re more likely to take steps to protect them. But figuring out how people feel isn’t always easy. Online media is a great source of this information, and accurate ML tools can help tap into it.