IRAILGSep 15, 2023

Explaining Search Result Stances to Opinionated People

arXiv:2309.08460v16 citationsh-index: 30
Originality Incremental advance
AI Analysis

This work addresses a practical issue for search engine designers aiming to reduce bias in user information consumption, though it is incremental in nature.

The study tackled the problem of cognitive biases like confirmation bias in opinionated users during web searches by investigating whether stance labels and explanations on search results could increase the diversity of results clicked. They found that stance labels and explanations led to more diverse search result consumption, but did not observe systematic opinion change among users.

People use web search engines to find information before forming opinions, which can lead to practical decisions with different levels of impact. The cognitive effort of search can leave opinionated users vulnerable to cognitive biases, e.g., the confirmation bias. In this paper, we investigate whether stance labels and their explanations can help users consume more diverse search results. We automatically classify and label search results on three topics (i.e., intellectual property rights, school uniforms, and atheism) as against, neutral, and in favor, and generate explanations for these labels. In a user study (N =203), we then investigate whether search result stance bias (balanced vs biased) and the level of explanation (plain text, label only, label and explanation) influence the diversity of search results clicked. We find that stance labels and explanations lead to a more diverse search result consumption. However, we do not find evidence for systematic opinion change among users in this context. We believe these results can help designers of search engines to make more informed design decisions.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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