Search engine effects on news consumption: ranking and representativeness outweigh familiarity in news selection

TL;DR

This study analyzes how search engine algorithms influence news consumption, showing that ranking and representativeness are more influential than familiarity, and that Google Search promotes exposure to diverse and unfamiliar news sources.

Contribution

It provides empirical evidence on the dominance of algorithmic factors over psychological familiarity in shaping news selection on search engines.

Findings

Algorithmic ranking significantly influences news choices.

Google Search promotes exposure to unfamiliar and diverse news sources.

Familiarity has a lesser impact compared to ranking and representativeness.

Abstract

Online platforms have transformed the way in which individuals access and interact with news, with a high degree of trust particularly placed in search engine results. We use web tracked behavioral data across a 2-month period and analyze three competing factors, two algorithmic (ranking and representativeness) and one psychological (familiarity) that could influence the selection of news articles that appear in search results. Participants' (n=280) news engagement is our proxy for familiarity, and we investigate news articles presented on Google search pages (n=1221). Our results demonstrate the steering power of the algorithmic factors on news consumption as compared to familiarity. But despite the strong effect of ranking, we find that it plays a lesser role for news articles compared to non-news. We confirm that Google Search drives individuals to unfamiliar sources and find that it increases the diversity of the political audience to news sources. With our methodology, we take a step in tackling the challenges of testing social science theories in digital contexts shaped by algorithms.