Diversifying search results of controversial queries

Kacimi, Mouna; Gamper, Johann

doi:10.1145/2063576.2063594

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…This could also be framed as a learning problem with the objective being to learn to alter the query such that bias in the results is minimized. In addition, rather than focusing only on result accuracy, a similar method could be applied to control for positive skew if, say, balanced perspectives and diversity were important in the results (as suggested in previous work [Kacimi and Gamper 2011;Yom-Tov et al 2014]). The effectiveness of this simplistic query-term substitution approach illustrates one way in which search engines could address content biases directly with low overhead.…”

Section: Query Termmentioning

confidence: 99%

Content Bias in Online Health Search

White

Hassan

2014

ACM Trans. Web

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Search engines help people answer consequential questions. Biases in retrieved and indexed content (e.g., skew toward erroneous outcomes that represent deviations from reality), coupled with searchers' biases in how they examine and interpret search results, can lead people to incorrect answers. In this article, we seek to better understand biases in search and retrieval, and in particular those affecting the accuracy of content in search results, including the search engine index, features used for ranking, and the formulation of search queries. Focusing on the important domain of online health search, this research broadens previous work on biases in search to examine the role of search systems in contributing to biases. To assess bias, we focus on questions about medical interventions and employ reliable ground truth data from authoritative medical sources. In the course of our study, we utilize large-scale log analysis using data from a popular Web search engine, deep probes of result lists on that search engine, and crowdsourced human judgments of search result captions and landing pages. Our findings reveal bias in results, amplifying searchers' existing biases that appear evident in their search activity. We also highlight significant bias in indexed content and show that specific ranking signals and specific query terms support bias. Both of these can degrade result accuracy and increase skewness in search results. Our analysis has implications for bias mitigation strategies in online search systems, and we offer recommendations for search providers based on our findings.

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Section: Query Termmentioning

confidence: 99%

Content Bias in Online Health Search

White

Hassan

2014

ACM Trans. Web

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“…Kacimi and Gamper propose a different opinion diversification framework for controversial queries [17,18]: three criteria are considered for diversification: topical relevance, semantic diversification, and sentiment diversification. Their model favors documents most different in sentiment direction and in the arguments they discuss.…”

Section: Related Workmentioning

confidence: 99%

“…How can opinionated content exhibiting sentiments be diversified? Initial approaches have been presented [10,17,18]; however these only consider equal diversification. In this paper, we view the problem from a high-level perspective to allow for sentiment diversification according to different biases, which will be vital for situations like a literature review on a controversial topic.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature [10,17,23] we identified a document to either have a single discrete sentiment from {−1, 0, 1}, or the sentiment is broken down into three scores positivity, negativity, and neutrality such that they sum to 1.0 for a single document. We refer to these latter ones as finer grained "fractional scores" in the rest of the paper.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment diversification with different biases

Aktolga

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic -the 'Topic Sentiment'. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the 'minority' or outlying sentiment(s) are boosted and those with the popular sentiment are demoted.Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48% is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.

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“…In the domain of IR many concepts are introduced to define, for example, opinion-like queries in social networks [24]. Efforts are also being made in order to diversify query results of ambiguous queries, for example concepts such as sentiment diversification to identify positive, negative and neutral sentiments about the search topic can be used [25].…”

Section: Discussionmentioning

confidence: 99%

Contextual Query Using Bell Tests

Barros

Toffano

Meguebli

et al. 2014

Quantum Interaction

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Tests are essential in Information Retrieval and Data Mining in order to evaluate the effectiveness of a query. An automatic measure tool intended to exhibit the meaning of words in context has been developed and linked with Quantum Theory, particularly entanglement. "Quantum like" experiments were undertaken on semantic space based on the Hyperspace Analogue Language (HAL) method. A quantum HAL model was implemented using state vectors issued from the HAL matrix and query observables, testing a wide range of window sizes. The Bell parameter S, associating measures on two words in a document, was derived showing peaks for specific window sizes. The peaks show maximum quantum violation of the Bell inequalities and are document dependent. This new correlation measure inspired by Quantum Theory could be promising for measuring query relevance.

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Diversifying search results of controversial queries

Cited by 13 publications

References 17 publications

Content Bias in Online Health Search

Content Bias in Online Health Search

Sentiment diversification with different biases

Contextual Query Using Bell Tests

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