Extending Deep Learning to Image Content Analysis
Chris J. Vargo · 2024
This chapter explores the application of deep learning in image content analysis within mass media research, emphasizing the critical role of images in shaping public perception and discourse. It delves into the mechanics of neural networks, the importance of diverse datasets like GDELT and the Internet Archive, and the utility of high-level Python packages for image analysis. Ethical considerations, particularly algorithmic bias, are scrutinized, highlighting the necessity of human oversight in automated systems. The chapter concludes by discussing the potential challenges of automated-image analysis technologies, advocating for a balanced approach that integrates human expertise with computational efficiency.