Today Arizona Governor Jan Brewer vetoed a bill that would have explicitly allowed business owners to refuse service to LGBT persons (or anyone, really) if the business owners invoked a religious freedom objection. The bill was obviously and intentionally anti-gay. What was interesting was the way different media outlets framed the issue. For example, the Washington Post, on their website’s front page, wrote the headline as “Brewer vetoes bill denying service to gays”:
Yesterday I wrote about the co-occurrence of claims about homosexuality in national newspapers since 1950. As part of that analysis, I argued that the more relationships that exist between claims, the denser the co-occurrence networks, the more discourse work is being done. If a claim is made without justification or contradiction, the claim is taken on its face – it is not considered controversial. A controversial, or unpopular, claim requires justification and will likely be accompanied by opposing claims, which leads to denser networks. Yesterday I focused on how these claim networks changed over time. Today, I want to briefly explore how these networks differ across types of speakers.
For my dissertation, I coded a sample of newspaper articles published since 1950 that mentioned homosexuality. My sample consisted of 720 articles. In the course of coding these articles, I copied any paragraph that mentioned homosexuality. 2382 total paragraphs were copied into my coding application. I then coded each paragraph for the presence of one of 12 claims about homosexuality and/or gay men and lesbians. Using this data, I constructed co-occurrence networks. I calculated how many times two claims appeared together in the same article. I then calculated how many times we might expect these two claims to appear together randomly given the number of articles and the total number of articles each claim appeared in. A tie is present if two claims appeared together more often than one standard deviation above this random expectation. Ties are colored blue if the two claims appeared together more often than two standard deviations above what we would expect by random chance. The nodes represent claims. Red nodes represent negative claims and green nodes represent positive claims. The size of the node represents how many times a claim appeared in total. The Bad claim is a “other-negative” category for negative claims that did not fit in any of the other claims. Similarly, the Good claim is a “other-positive” category for positive claims that did not fit elsewhere.
Who leads the LGBT movement? The answer to that question, predictably, says a lot about the movement overall. I calculated the top covered LGBT SMO for each year from 1960 (the first year an organization appears in the newspapers) through 2010. This includes coverage from the New York Times, Los Angeles Times, and Wall Street Journal. The results are in the table at the end of this post. Continue reading
My dissertation investigates how and why newspaper discourse of homosexuality has changed over time. As I’ve coded my data, I keep seeing the same year marking some turning point in various trends: 1990. Here’s a selection of those trends: Continue reading