My initial data analysis utilized basic and interpretive content analysis of sixty news articles discussing the homeless and homelessness in some sense, twenty articles from CNN, my left-leaning news source, twenty from Fox News, my right-leaning news source, and twenty from Reuters, my central news source. I took a snapshot of the data, using articles that were all published between 2020-2022. These articles were chosen utilizing a purposive sampling method by Google advanced search and a cleared history to find articles from the three websites using the key terms “homeless” and “U.S.”. Once I had every article I could find within the correct time frame, I used a random number generator to pick which articles I examined, in hopes of avoiding any algorithmic bias. Within those articles I noted which terms and phrases were used most often within the article, especially taking note of what is written within the attention grabbing title and subheading. I also examined the first image used in the article, and took note of what is being represented. After a basic content analysis, I performed an interpretive content analysis to examine the meanings underlying the language and imagery, in order to see how the sources frame the idea of homelessness today. This provided me with an idea of the current discourse used to inform the public of homelessness, and I therefore was able to compare the data across the three news media outlets.