Question: Where are the data used by their case study from?
Evidence: The data for our case study spans over thirty years of articles from the New York Times, from January 1986 to December 2015, and was originally collected by BIBREF68 BIBREF68. The articles come from all sections of the newspaper, such as “World", “New York & Region", “Opinion", “Style", and “Sports". Our distributional semantic methods rely on all of the available data in order to obtain the most fine-grained understanding of the relationships between words possible. For the other techniques, we extract paragraphs containing any word from a predetermined list of LGTBQ terms (shown in Table TABREF19).
Each acronym label was matched insensitive to case and punctuation. Some currently prominent LGBTQ terms, such as queer and trans were not included in this study, as other senses of these words were considerably more frequent in earlier years. We further filtered out paragraphs from sections that typically did not pertain to news, such as “Arts", “Theater", and “Movies". While these sections could provide valuable information, we decided to focus on representation of LGBTQ groups in more news-related contexts. Furthermore, due to the original newspaper layout and subsequent digital processing, Arts sections often contained extremely long paragraphs containing numerous listings and schedules. Removing these sections substantially simplified data processing.
A challenging question when analyzing mass media for subjective attitudes is deciding whose perspective we want to capture: an individual reporter, the institution, or society at large? In this case study, we aim to identify the institution's dehumanizing attitudes towards LGBTQ people. We represent the New York Times institution as a combination of the journalists' words in news articles, direct quotes, paraphrases from interviews, and published opinion articles. Therefore, despite our news focus, we include data from “Opinion" sections; while opinion articles are stylistically different from traditional journalistic reporting due to more overt biases and arguments, these articles are important in constructing the institution's perspective. In addition, we consider all text in each relevant paragraph, including quotes and paraphrases, because they are important to a newspaper's framing of an issue, as particular quotes representing specific stances are intentionally included or excluded from any given article BIBREF69. However, this approach is sometimes problematic when the newspaper quotes clearly extreme positions that they are unlikely to agree with, and we discuss this issue throughout our qualitative analysis.
We refer to the remaining subset of the New York Times data after filtering as the LGBTQ corpus. The LGBTQ corpus consists of 93,977 paragraphs and 7.36 million tokens. However, a large increase in reporting on LGBTQ-related issues has led to a highly skewed distribution in the amount of data over years, with 1986 containing the least data (1144 paragraphs and 73,549 tokens) and 2012 containing the most (5924 paragraphs and 465,254 tokens).
For all experiments, we also include results for the terms American and Americans. Our primary motivation for including American(s) is to contrast changes in LGBTQ labels' representation in the New York Times with another social group label that appears in sufficiently different contexts. This ensures that the changes we find in dehumanizing language towards LGBTQ groups do not apply uniformly to all social groups, and are thus not merely an artifact of the publication's overall language change. While a natural “control" variable would be labels such as straight or heterosexual, we found that these terms only occurred within discussions of LGBTQ communities, likely because they name socially unmarked categories. We also considered comparing LGBTQ labels to person/people, but we found that the word embedding-based experiments were particularly sensitive to syntactic forms, so we selected a word that behaves more similarly syntactically to gay and homosexual, particularly by having both nominal and adjectival uses. Nevertheless, it is important to note that American(s) is by no means a neutral control variable. Because of its in-group status for the New York Times (a U.S. institution), we expect our measurements to show that American(s) appears in more humanizing contexts than LGBTQ group labels; however, we do not expect to find substantial changes in the use of American(s) over time.
Figure FIGREF20 shows the counts of group labels for each year in the New York Times from 1986 to 2015. For visualization purposes, only words with a total count greater than 1000 are shown. Notably, the relative frequency of homosexual decreased substantially over time, while gay, lesbian, and bisexual are more frequent in later years. We also see the emergence of the terms lgbt and transgender after 2000. Aggregated counts over all years for each LGBTQ label can be found in the Appendix.
The data for our case study spans over thirty years of articles from the New York Times, from January 1986 to December 2015, and was originally collected by Fast and Horvitz (2016).
Answer: The New York Times, from January 1986 to December 2015.