Semester 1, academic year 2023/2024
By Bruno Nadalic Sotic
This thesis explores how visual elements within news headlines influence audience engagement. It specifically investigates the presence of news values in images and their impact on user interactions with news articles, a topic previously understudied in communication science. The research utilizes a comprehensive dataset of user behavior logs from the Microsoft News platform, encompassing both click and non-click activities, to shed light on the dynamics of news consumption.
The study employs machine vision techniques via commercially available APIs. These are used to systematically convert visual elements of news headline imagery into textual representations. This approach allows for a detailed analysis of ‘image features’—such as the presence of notable figures, emotional expressions, or unusual scenes—and their alignment with traditional news values like prominence, novelty, and emotional impact. By doing so, this study attempts to validate to what extent we can measure news values using automated visual content analysis.
The study applies a combination of supervised and unsupervised machine learning methods to correlate said image features with news value factors, and subsequently quantify their influence on user engagement metrics. This approach provides a measurable framework for understanding how visuals affect news engagement but also extends news value theory to the visual domain.