Abstract
Search engines play a gatekeeper role in current high-choice information environments. Considered a form of new media, users are still more likely to find and trust news found through search than social media sites. Indeed, search engines are one of the most utilised technologies to find political information, despite audits uncovering biases in their results, for example, towards national outlets over local ones. It is therefore important to keep in mind the potential of search results to affect public opinion. With this study, we investigate how Google search news headlines and snippets differ when varying migrant search terms (e.g., immigrant, refugee, expat). We employ computational text analysis methods as well as qualitative content analysis. Specifically, we employ an automated framework for detecting media frames, originally trained on Twitter data, and attempt to transfer it to news data; this framework allows for a categorization of data to frames of a generic-issue (economy, safety, health) and specific (hero:diversity, threat:jobs) nature. We evaluate its applicability for this novel data source and find that it performs well for frames related economy and security. Our next steps include analysing the results of other computational measures, namely, sentiment, agency and political outlet of the news item. We expect that sentiment and agency will complement the initial results we see based on media frames.
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