Identity Framing, Media Platforms, and Attitudes toward Immigration

• In Event: Poster Session: Public Policy Preferences and Attitudes toward Democracy & European Politics and Society
  In Poster Session: Poster Session: Public Policy Preferences and Attitudes toward Democracy

Abstract

Overview
The ethnicity of immigrants is a crucial determinant of immigration attitudes in the receiving country. Prior studies suggest that ethnic frames of news have a significant impact on people’s attitudes toward immigration. However, very few studies explore the effects of the same frames across media platforms. My research tries to fill this gap by testing the variation of ethnic framing effects in different media platforms under a survey experimental setting. In a 2×2 factorial design, the treatment group would read a news piece about immigration on a simulated Twitter platform, while the control group would read the plain texts. By plain text, I mean the news piece only contains words (no other media features such as logos and UI). In the online experimental setting, respondent would only see the texts of the news with a white background on their screen, nothing else. Respondents in each group would be further randomly assigned to two versions of news with different ethnic frames (i.e., Asian frames, European frames). This survey targets the ethnic White citizens in the US. Based on social identity theory, I expect that respondents who are White would demonstrate more positive attitudes toward European immigration than Asian immigration (Hypothesis 1). According to the media richness theory, identity-framing effects are stronger in Twitter than in plain texts (Hypothesis 2).

Experimental Design
I plan to conduct a survey experiment to test the two hypotheses above. As mentioned above, the survey experiment will compare the variation of framing effects between a Tweet and plain text. Selecting Twitter as a comparison for plain text is based on several considerations. First, Twitter is widely used all over the world, with 528.3 million monetizable monthly active users as of 2023 (Shewale 2023). Moreover, a Tweet is a text-based medium but with several rich media features (e.g., hashtag highlights keyword and convey concise information), making it an interesting case to compare with plain text. Even richer social media platforms such as Instagram or TikTok are either not text-based or have too high level of richness, which makes them almost certainly have significantly higher communication efficiency than plain text.

For the design of Tweet, I chose The Associated Press (AP) as the news source to eliminate partisan or ideological effects at a maximum level since AP is widely regarded as one of the most neutral news agencies in the US. The reason I specify the news source rather than make it anonymous is to maintain the external validity of my experiment (i.e., in real cases, when you read a real Tweet, you would know who posted it). Similarly, regarding other features, I attempt to make them as same as a real Tweet. Regarding plain text, I simply type the same words in the survey platform, and respondent will only see the text with a white background.

This survey experiment follows a 2×2 factorial design. Factor 1 is a media platform with two levels (level 1: Tweet, level 2: No platform or plain text). Factor 2 is framing, which has two levels (i.e., Asian, European). The treatment 1 is the Asian frame. In a made-up news piece about immigration, I specify that immigrants are ethnically Asian. Similarly, in the European frame, the news suggests that immigrants are from Europe.

Respondents would be randomly assigned to one of the media platforms (i.e., Tweet, plain text), and each of the platforms contains two different versions of frames that have an equal chance to be shown on each person’s screen (each individual would only see one version of framing). After that, there will be one post-treatment measure (i.e., attitudes toward immigration).

Variables and measurements
The dependent variable is the respondents’ attitudes toward immigration, measured by four survey questions with 5-point Likert scale (0-4). All questions will display after the treatment. The total score of immigration attitude is a sum of these four answers. Specifically, on a 16-point scale, a higher score means a more positive attitude toward immigration. The actual wording of the survey questions can be found from the Appendices. Using a 5-point scale allows to capture more nuanced variation of attitudes, whereas it is potentially too complicated to interpret.

The independent variable is the identity frames (Asian/European), measuring as a nominal variable with two different categories: 0 (European), 1 (Asian). Control variables include interests in politics, news and media consumption, political ideology and partisanship, employment status, and economic factors (e.g., household income).

Author

• Yiheng Mo