Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection

Alam, Mehwish; Iana, Andreea; Grote, Alexander; Ludwig, Katharina; Müller, Philipp; Paulheim, Heiko

doi:10.1145/3487553.3524674

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Their approach leverages dormant ties, mentions of mentions, and community members within a user's network to offer diverse recommendations and facilitate new social connections. In a study by Alam et al (2022), biases in news recommender systems are examined using stance and sentiment analysis. By conducting an experiment on a German news corpus focused on migration, the study reveals that these recommender systems tend to recommend articles with negative sentiments and stances against refugees and migration.…”

Section: Introductionmentioning

confidence: 99%

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Areeb

Nadeem

Sohail

et al. 2023

WIREs Data Min & Knowl

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A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas > Internet Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Security and Privacy

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Section: Introductionmentioning

confidence: 99%

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Areeb

Nadeem

Sohail

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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Multi-source information contrastive learning collaborative augmented conversational recommender systems

Liu,

Cao,

Huang

et al. 2024

Complex Intell. Syst.

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Conversational Recommender Systems (CRS) aim to provide high-quality items to users in fewer conversation rounds using natural language. Despite various attempts that have been made, there are still some problems: Previous CRS only learned item representations in a single knowledge graph and ignored item tags; information gaps exist in the same items from different knowledge graphs and information popularity both affect user preferences; system generated responses lack descriptiveness and diversity. To address these problems and fully utilize external knowledge, we propose a Multi-source Information Contrastive Learning Collaborative Augmented method ($$\textbf{MCCA}$$ MCCA ), which aims to mine the potential tag preferences of users in dialogues as well as enhance the accuracy of item representation and user preference modeling. Specifically, we utilize the obtained items and their tags to construct a new knowledge graph that incorporates movie tags. We design a Multi-source Item Fusion mechanism ($$\textbf{MIF}$$ MIF ) to bridge the information gaps between items from different knowledge graphs and then utilize unsupervised contrastive learning to enhance the items’ representation capability after MIF. Additionally, a Multi-Tag Fusion mechanism ($$\textbf{MTF}$$ MTF ) is designed to combine user-perceived information (i.e., tag popularity) and keywords obtained from reviews to co-enhance user preference representations through items and tags, and to incorporate fused item and tag features into the conversation module. Extensive experiments on two datasets show that MCCA significantly outperforms state-of-the-art methods. The source code will be available at https://github.com/lhy-cqut/MCCA.

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AI alignment: Assessing the global impact of recommender systems

Bojic

2024

Futures

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Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection

Cited by 8 publications

References 19 publications

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Multi-source information contrastive learning collaborative augmented conversational recommender systems

AI alignment: Assessing the global impact of recommender systems

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