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Coping Responses to the Stress of Using News Platforms' recommendation algorithms

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第8期41卷 4759-4774页

作者： Yang, Jie Li, Hongxiu Zhang, Linzi Xu, Guohu Zhongnan Univ Econ & Law Sch Business Adm Wuhan Peoples R China Tampere Univ Dept Informat & Knowledge Management Tampere Finland

Drawing on the stressor-strain-outcome (SSO) framework, this study examined how information-related stressors (perceived information overload, information quality deterioration, and information narrowing) and privacy-related stressor (perceived privacy risk) in using news platforms' recommendation algorithms trigger users' psychological reactance, which leads to their active coping, expressive coping, or avoidance coping with their stress in using such recommendation algorithms. Using a sample of 352 valid research responses, this study found that users' psychological reactance is positively influenced by perceived information quality deterioration, followed by perceived information overload and perceived privacy risk. Meanwhile, perceived information narrowing has no significant impact on psychological reactance. In turn, psychological reactance exerts a stronger impact on expressive coping and avoidance coping responses than on active coping response. This study enriches the literature on the stress in algorithm usage at the individual level and provides practical insights for the providers of news platforms which have applied recommendation algorithms.

关键词： recommendation algorithms stressor-strain-outcome framework stress psychological reactance coping responses

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The impact of different recommendation algorithms on consumer search behavior and merchants competition

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INTERNATIONAL REVIEW OF ECONOMICS & FINANCE 2025年 98卷

作者： Zhang, Weiyi Wang, Yong China Univ Geosci Sch Econ & Management Beijing Peoples R China Tsinghua Univ Inst Econ Sch Social Sci Beijing Peoples R China

recommendation algorithms on platform markets can be categorized into neutral algorithms and non-neutral algorithms. We explore how these two algorithms affect consumer's search behaviors and merchant's competition behaviors based on a consumer search model. We found that as platform transitions from not providing recommendation algorithms to providing neutral algorithms and then to providing non-neutral algorithms, the price dispersion among merchants gradually increases, while the intensity of price competition decreases. When the difference in transaction utilities among merchants is small, providing neutral algorithms can enhance platform profits, consumer surplus, and social welfare. In the meantime, providing non-neutral algorithms always harms platform profits and social welfare, but still enhances consumer surplus. This study recommends that platforms should maintain a balance between neutral and non- neutral algorithms in the development of recommendation systems, where platforms can then guide merchants to focus their efforts and resources on product development and service improvement, rather than engaging in price wars and paid promotions.

关键词： Consumer search recommendation algorithms Platforms Price competition

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Reducing Symbiosis Bias through Better A/B Tests of recommendation algorithms 34

Reducing Symbiosis Bias through Better A/B Tests of Recommen...

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34th ACM Web Conference, WWW 2025

作者： Brennan, Jennifer Cong, Yahu Yu, Yiwei Lin, Lina Peng, Yajun Meng, Changping Han, Ningren Pouget-Abadie, Jean Holtz, David M. Google Research Kirkland WA United States University of California Berkeley Berkeley CA United States Google San Bruno CA United States Google Mountain View CA United States Google Research New York NY United States Haas School of Business University of California Berkeley Berkeley CA United States

ISBN: (纸本)9798400712746

It is increasingly common in digital environments to use A/B tests to compare the performance of recommendation algorithms. However, such experiments often violate the stable unit treatment value assumption (SUTVA), particularly SUTVA’s “no hidden treatments” assumption, due to the shared data between algorithms being compared. This results in a novel form of bias, which we term “symbiosis bias,” where the performance of each algorithm is influenced by the training data generated by its competitor. In this paper, we investigate three experimental designs–cluster-randomized, data-diverted, and user-corpus co-diverted experiments–aimed at mitigating symbiosis bias. We present a theoretical model of symbiosis bias and simulate the impact of each design in dynamic recommendation environments. Our results show that while each design reduces symbiosis bias to some extent, they also introduce new challenges, such as reduced training data in data-diverted experiments. We further validate the existence of symbiosis bias using data from a large-scale A/B test conducted on a global recommender system, demonstrating that symbiosis bias affects treatment effect estimates in the field. Our findings provide actionable insights for researchers and practitioners seeking to design experiments that accurately capture algorithmic performance without bias in treatment effect estimates introduced by shared data. © 2025 Copyright held by the owner/author(s).

关键词： A/B testing Experiment Design Interference Randomized Experiments recommendation algorithms Simulations SUTVA Symbiosis Bias

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The Dark Side of recommendation algorithms in Chinese Mass Short Video Apps: Effect of Perceived Over-recommendation on Users' Cognitive Dissonance and Discontinuance Intention

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2024年

作者： Lv, Xiaofeng Li, Jing Wang, Qingyang Nanjing Agr Univ Sch Informat Management Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Management Nanjing Peoples R China Nanjing Univ Sch Management & Engn Nanjing Peoples R China

In the digital age, human-algorithm interaction has become ubiquitous. The popularity of short-form video apps has led to greater access to and experience with algorithmic recommender systems, but it has also exposed their potential problems and dark sides. However, there is still limited knowledge about the dark side of over-recommendation from the perspective of user perception, especially regarding the negative impact on users' psychology and behavior. Drawing on social cognitive theory, this study constructed a theoretical model to investigate how information environment factors influence users' cognitive dissonance and discontinuance intention under the antecedent dimension of perceived over-recommendation. The model was tested using partial least squares structural equation modeling on 322 valid questionnaires from users of Chinse mass short video apps (eg, Douyin). The results showed that when users perceived over-recommendation, information narrowing, information redundancy, perceived overload, and privacy invasion significantly increased their cognitive dissonance, ultimately leading to discontinuance intention. Notably, cognitive dissonance fully mediated the relationship between the information environment and discontinuance intention, and self-efficacy did not play a significant moderating role. Additionally, the local path effects varied significantly across groups with different characteristics. To maintain the sustainability of short video platforms, it is crucial to explore moderate recommendation mechanisms. User-centered functionality improvements and differentiated behavioral interventions can help mitigate negative psychology and discontinuance intention among users.

关键词： recommendation algorithms short video perceived over-recommendation cognitive dissonance discontinuance intention

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Understanding the Contribution of recommendation algorithms on Misinformation recommendation and Misinformation Dissemination on Social Networks

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ACM TRANSACTIONS ON THE WEB 2023年第4期17卷 1-26页

作者： Pathak, Royal Spezzano, Francesca Pera, Maria Soledad Boise State Univ Boise ID 83725 USA Delft Univ Technol Delft Netherlands

Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter bubbles issues affecting recommender systems. While much has been studied about misinformation on social networks, the potential exacerbation that could result from recommender algorithms in this environment is in its infancy. In this manuscript, we present the result of an in-depth analysis conducted on two datasets (POLITIFACT FAKENEWSNET DATASET and HEALTHSTORY FAKEHEALTH DATASET) in order to deepen our understanding of the interconnection between recommender algorithms and misinformation spread on Twitter. In particular, we explore the degree to which well-known recommendation algorithms are prone to be impacted by misinformation. Via simulation, we also study misinformation diffusion on social networks, as triggered by suggestions produced by these recommendation algorithms. Outcomes fromthiswork evidence that misinformation does not equally affect all recommendation algorithms. Popularity-based and network-based recommender algorithms contribute the most to misinformation diffusion. Users who are known to be superspreaders are known to directly impact algorithmic performance and misinformation spread in specific scenarios. Findings emerging from our exploration result in a number of implications for researchers and practitioners to consider when designing and deploying recommender algorithms in social networks.

关键词： Misinformation recommendation algorithms social networks diffusion news Twitter

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An architecture and platform for developing distributed recommendation algorithms on large-scale social networks

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JOURNAL OF INFORMATION SCIENCE 2015年第5期41卷 686-704页

作者： Corbellini, Alejandro Mateos, Cristian Godoy, Daniela Zunino, Alejandro Schiaffino, Silvia UNICEN Univ ISISTAN Res Inst Buenos Aires DF Argentina

The creation of new and better recommendation algorithms for social networks is currently receiving much attention owing to the increasing need for new tools to assist users. The volume of available social data as well as experimental datasets force recommendation algorithms to scale to many computers. Given that social networks can be modelled as graphs, a distributed graph-oriented support able to exploit computer clusters arises as a necessity. In this work, we propose an architecture, called Lightweight-Massive Graph Processing Architecture, which simplifies the design of graph-based recommendation algorithms on clusters of computers, and a Java implementation for this architecture composed of two parts: Graphly, an API offering operations to access graphs;and jLiME, a framework that supports the distribution of algorithm code and graph data. The motivation behind the creation of this architecture is to allow users to define recommendation algorithms through the API and then customize their execution using job distribution strategies, without modifying the original algorithm. Thus, algorithms can be programmed and evaluated without the burden of thinking about distribution and parallel concerns, while still supporting environment-level tuning of the distributed execution. To validate the proposal, the current implementation of the architecture was tested using a followee recommendation algorithm for Twitter as case study. These experiments illustrate the graph API, quantitatively evaluate different job distribution strategies w.r.t. recommendation time and resource usage, and demonstrate the importance of providing non-invasive tuning for recommendation algorithms.

关键词： Graph databases graph-processing frameworks large-scale processing recommendation algorithms social networks work scheduling

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WooKong: A Ubiquitous Accelerator for recommendation algorithms With Custom Instruction Sets on FPGA

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IEEE TRANSACTIONS ON COMPUTERS 2020年第7期69卷 1071-1082页

作者： Wang, Chao Gong, Lei Ma, Xiang Li, Xi Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci Hefei 230027 Anhui Peoples R China

recommendation algorithms, such as Neighborhood-based Collaborative- Filtering (CF), have been widely applied in various emerging machine learning applications. However, under the circumstance of the explosive big data, it poses significant challenges to CF recommendation algorithms as it is becoming quite time and energy-consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. To solve these problems, in this article, we propose WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA. It is able to accommodate three types of CF recommendation algorithms, including User-based CF, Item-based CF, and SlopeOne recommendations algorithms, with five different similarity analysis metrics including Jaccard, Cosine, CosineIR, euclidean, and Pearson. To maintain flexibility for these different CF algorithms and metrics, we adopt custom instruction sets to manipulate the learning and prediction accelerators. We implement a hardware prototype on a real Xilinx Zynq FPGA development board. Experimental results show that the proposed learning and prediction accelerators can achieve 8.0X speedup and 1.7X speedup compared with an Intel i7 processor respectively. The accelerator has the energy benefits of up to 137.4X compared with an NVIDIA Tesla K40C GPU, with the affordable hardware cost.

关键词： Prediction algorithms Field programmable gate arrays Mathematical model Hardware Machine learning algorithms Acceleration Measurement Accelerator recommendation algorithms machine learning domain-specific architecture FPGA

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Auditing Entertainment Traps on YouTube: How Do recommendation algorithms Pull Users Away from News

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POLITICAL COMMUNICATION 2024年第6期41卷 903-920页

作者： Huang, Shengchun Yang, Tian Univ Penn Annenberg Sch Commun 3620 Walnut St Philadelphia PA 19104 USA Chinese Univ Hong Kong Sch Journalism & Commun Hong Kong Peoples R China

recommendation algorithms that customize information feeds for individuals have raised concerns about exacerbating inequalities in news exposure among citizens. In response to these concerns, we conducted an audit study on YouTube to analyze the algorithmic impact on curating news versus other content topics. We examined over 1.7 million YouTube video recommendations audited in 2019 and developed novel analysis approaches including network analysis and Markov chains. Results show that recommendation algorithms may potentially redirect users away from news content through two influence pathways: (1) the "topical filter bubbles," wherein entertainment content has a higher probability of being recommended over news content in a self-reinforcing manner;and (2) "algorithmic redirection," wherein the probability of entertainment videos being recommended after a news video is much higher than that for the opposite. Overall, YouTube recommendation algorithms have a higher probability of recommending entertainment videos than news. The findings imply essential biases in algorithmic recommendations on digital platforms beyond amplifying users' preferences.

关键词： Filter bubbles Markov chains news exposure recommendation algorithms YouTube

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Understanding users' negative responses to recommendation algorithms in short-video platforms: a perspective based on the Stressor-Strain-Outcome (SSO) framework

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ELECTRONIC MARKETS 2022年第1期32卷 41-58页

作者： Ma, Xiumei Sun, Yongqiang Guo, Xitong Lai, Kee-hung Vogel, Doug Harbin Inst Technol Sch Management 92 West Da Zhi St Harbin 150001 Heilongjiang Peoples R China Hong Kong Polytech Univ Fac Business Hung Hom Kowloon Li Ka Shing Tower Hong Kong Peoples R China Wuhan Univ Sch Informat Management 299 Bayi Rd Wuhan 430072 Hubei Peoples R China

AI-based recommendation algorithms have received extensive attention from both academia and industry due to their rapid development and broad application. However, not much is known regarding the dark side, especially users' negative responses. From the perspective of recommendation features and information characteristics, this study aims to uncover users' negative responses to such AI-based recommendation algorithms in the algorithm-driven context of short-video platforms. Drawing on the stressor-strain-outcome (SSO) framework, this study identifies information-related stressors and examines their influence on users' negative responses to a recommendation algorithm. The results show that such algorithms' greedy recommendation feature induces information narrowing, information redundancy, and information overload. These information factors predict users' exhaustion, which in turn promotes users' psychological reactance and discontinuance intention. This study adds knowledge on the dark side of recommendation algorithms.

关键词： recommendation algorithms SSO framework Information characteristics Negative responses Dark side of AI

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The Relationship Between Social Media Information Sharing Characteristics and Problem Behaviors Among Chinese College Students Under recommendation algorithms

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PSYCHOLOGY RESEARCH AND BEHAVIOR MANAGEMENT 2024年 17卷 2783-2794页

作者： Sun, Yadong Shan, Yanjie Xie, Jiaqiong Chen, Ke Hu, Jia Chongqing Univ Fac Social Sci Chongqing Peoples R China Chongqing Univ Inst Adv Studies Humanities & Social Sci Chongqing Peoples R China

Purpose: With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings. Methods: An online questionnaire survey was conducted among 34,752 college students in China. A latent profile analysis was conducted to explore the various behavioral patterns of Chinese college students' information sharing across the three social media platforms identified for this study. The Bolck-Croon-Hagenaars (BCH) method Regression Mixture Modeling was then used to analyze the differences in internalizing and externalizing problem behaviors among the different subgroups of Chinese college students. Results: The level of information sharing by college students across different social media platforms could be divided into "WeChat Moments low-frequency information sharing", "middle-frequency comprehensive information sharing", "TikTok high-frequency information sharing", and "Sina Weibo high-frequency information sharing". Significant differences were observed regarding internalizing and externalizing problem behaviors among college students in different information-sharing subgroups. Conclusion: This study identified four subgroups with different information-sharing characteristics using latent profile analysis. Among them, college students who are in subgroup of social media information sharing influenced by recommendation algorithms exhibit higher frequency of information sharing and higher level of internalizing and externalizing problematic behaviors. These results expand our understanding of col

关键词： user-generated content recommendation algorithms latent profile analysis internalizing problem behaviors externalizing problem behaviors

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