Unveiling Media Narratives: A Deep Dive into Content Analysis Leveraging Technology and Machine Learning

Abstract Information: Media content analysis looks across different media sources (i.e. newspapers, magazines, journals) to identify what topics are being amplified and which voices are being promoted, which can play a crucial role in understanding how different voices and topics are, or are not, being represented and amplified in media narratives. These media narratives can in turn influence public opinion and discourse on diverse topics such as public health, politics, and education. This session will consist of a demonstration of a step-by-step guide process on how to perform a media content analysis, using two evaluation projects as examples. These projects include an evaluation of participants in a health leadership program and the extent to which they were featured in the media, how they were featured, and which topics were most frequently amplified; and an evaluation of journalists who participated in a fellowship program and what they wrote about, where the content was featured, and how that changed over time. This session will demonstrate the methods used in these analyses such as Latent Dirichlet Allocation (LDA) topic modeling, web scraping, sentiment analysis, and qualitative diagramming, and will also include an explanation of tools that can be used such as R and Nexis. After this session, participants will have a better understanding of how to utilize media content analysis in their projects, will understand the limitations and possibilities, and have new ideas on how to measure the extent to which different voices and topics are being represented and amplified in the media. No prior knowledge of media content analysis or any specific tools or methods will be necessary for attendees!