# Explaining Search Result Stances to Opinionated People

arXiv: https://arxiv.org/abs/2309.08460 · HTML: https://mlsanity.com/paper/2309.08460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 21 of 21)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Cross-Target Stance Classification with Self-Attention Networks](https://mlsanity.com/paper/1805.06593/) (2018)
- [Fairness in Ranking: A Survey](https://mlsanity.com/paper/2103.14000/) (2021)
- [Explainable AI: current status and future directions](https://mlsanity.com/paper/2107.07045/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA](https://mlsanity.com/paper/2009.08257/) (2020)
- [Evaluation Metrics for Measuring Bias in Search Engine Results](https://mlsanity.com/paper/2210.10517/) (2022)
- [Extractive Explanations for Interpretable Text Ranking](https://mlsanity.com/paper/2106.12460/) (2021)
- [Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study](https://mlsanity.com/paper/2110.07693/) (2021)
- [Adapting Security Warnings to Counter Online Disinformation](https://mlsanity.com/paper/2008.10772/) (2020)
- [Exploiting stance hierarchies for cost-sensitive stance detection of Web documents](https://mlsanity.com/paper/2007.15121/) (2020)
- [A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set](https://mlsanity.com/paper/2206.12027/) (2022)

## Built on by (most cited first, 1 of 1)

- [Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects](https://mlsanity.com/paper/2401.11201/) (2024)

## Abstract (arXiv)

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.