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We compare the contents of Fox and MSNBC weekday evening telecasts using natural language analysis with Linguistic Inventory Word Count (LIWC) and sociopolitical dictionaries tapping into moral foundations, values, grievances, and personality. Across time, the two networks differed substantially across many constructs, particularly those from LIWC. The core of the difference between the networks was captured by a four‐component measure which we labeled Personalizing versus Formal speech. Scores on this measure were particularly volatile during 2019 and 2020, a period which included Trump's first impeachment, the beginning of the COVID pandemic, and the 2020 presidential campaign. In comparison with prior presidential election years, only MSNBC showed a drop in positive emotions in 2020, while both networks increased in the use of communal and analytic language. Contrary to our expectations, the language style of the two networks did not demonstrate increasing divergence over time. [ABSTRACT FROM AUTHOR]

Attributions for sexual orientation strongly predict opposition to gay rights policies; however, we propose that beliefs that gays and lesbians violate important values drive gay rights opposition and account for the relationship between attributions and anti-gay discrimination. In two studies, we found that beliefs that gays and lesbians violate values accounted for much of the relationship between attributions and anti-gay discrimination. In addition, these stereotypes were the most powerful predictors of opposition to gay rights when both value violations and attributions were included in the model. Results also demonstrated that violations of specific values predicted opposition to policies relevant to those values. This suggests that attributions of choice over sexual orientation are less relevant for predicting opposition to gay rights than beliefs about choice to uphold or violate values. [ABSTRACT FROM AUTHOR]

Americans' conificted attitudes toward immigrants and immigration has stymied immigration reform for decades. In this article, we explore the nuanced nature of stereotypes about immigrants and how they relate to ambivalent attitudes toward immigrant groups and the disparate array of inmgration policies that affect them. Using item response theory and multiple regression analysis, we identified and related stereotypes of different immigrant groups to group-based and policy attitudes. Results demonstrate that ambivalent stereotypes mapped onto ambivalent group-based and immigration policy attitudes. Specifically, stereotypes that portray groups in positive or sympathetic ways predicted positive attitudes toward the group and more supportive attitudes toward policies that facilitate their immigration to the United States. Conversely, negative qualities predicted negative attitudes toward the same group and support for policies that prevent the group from immigrating. Results are discussed in light of current theory related to stereotype content, complementarity of stereotypes, and broader implications for immigration attitudes and policy. [ABSTRACT FROM AUTHOR]