Computers in Human Behavior最新文献_第4页

Talking past each other in the cultural war area: Testing the efficacy of the moral attitude dynamic model to achieve accurate disagreement 文化战场上的各说各话：检验道德态度动态模型实现准确分歧的有效性

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-20 DOI: 10.1016/j.chb.2025.108873

Mengyao Xu , Fritz Cropp , Glen T. Cameron

This study evaluates the Moral Attitude Dynamic Model (MADM) as a tool to defuse conflicts tensions in two contemporary cases. It conducts a moral diagnosis on genetically modified (GM) food and abortion related tweets. Supporters' and opponents' tweets were identified using supervised machine learning classifiers for each issue separately. Distributed Dictionary Representation (DDR), a Natural Language Processing tool was adopted to quantify supporters' and opponents' moral stances along the enhanced contingency continuum (the key construct of MADM). The results reveal that serval types of misconceptions have been overlooked by both opponents and supporters of each issue. Multilevel linear modeling was then employed to investigate the quantified moral stances and to develop coping strategies for each group. Moreover, this study adopts computational methods for implementing MADM, enabling real-time tracking and analysis of target publics’ attitudes towards specific issues for more proactive and effective conflict management in polarized contexts. This is the first study to apply MADM in the context of polarization management. It expands the application of both Contingency Theory and Moral Foundation Theory (MFT) to polarization management as MADM bridges Contingency Theory and MFT. Furthermore, this study advances MFT methodologically by being the first to examine the moral loadings across all six innate moral foundations using computational methods.

本研究评估道德态度动态模型（MADM）在两个当代案例中作为化解冲突紧张关系的工具。它对转基因（GM）食品和堕胎相关的推文进行道德诊断。支持者和反对者的推文分别使用监督机器学习分类器进行识别。采用自然语言处理工具分布式字典表示（DDR），沿着增强的偶变连续体（MADM的关键结构）量化支持者和反对者的道德立场。结果显示，每个问题的反对者和支持者都忽视了几种类型的误解。然后采用多水平线性模型来研究量化的道德立场，并制定每个群体的应对策略。此外，本研究采用计算方法实施MADM，可以实时跟踪和分析目标公众对特定问题的态度，以便在两极分化的背景下更积极有效地管理冲突。这是首次将MADM应用于极化管理的研究。作为权变理论和道德基础理论的桥梁，MADM将权变理论和道德基础理论在两极分化管理中的应用拓展开来。

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引用次数: 0

Can professional or AI fact-checking protect trust in journalism from political attacks? The complex roles of source, transparency, ideology, and the machine heuristic 专业的或人工智能的事实核查能保护人们对新闻业的信任免受政治攻击吗？来源、透明度、意识形态和机器启发式的复杂角色

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-20 DOI: 10.1016/j.chb.2025.108872

Rui Wang , Yotam Ophir

Trust in mainstream news media in the United States has declined to a historic low, partly due to political influencers attacking journalism on social media as a form of identity performance. This study examines whether fact-checking can defend trust in journalism against such attacks by exploring the roles of fact-checker source (AI vs. human), transparency, political ideology, and belief in the machine heuristic. An online experiment (N = 526) found that identity congruence with the political influencer significantly shaped perceptions of post accuracy, fact-checker credibility, trust in the targeted news outlet, and sharing intentions. Attributing the fact-check to AI did not enhance its effectiveness than human, even among strong machine heuristic believers. However, providing transparency about the fact-checking process reduced partisan gaps in perceived fact-checker credibility. Theoretical and practical implications are discussed.

美国人对主流新闻媒体的信任已降至历史低点，部分原因是政治影响者在社交媒体上攻击新闻业，将其视为一种身份表现。本研究通过探索事实核查来源（人工智能与人类）、透明度、政治意识形态和对机器启发式的信仰的作用，考察事实核查是否可以捍卫对新闻业的信任，免受此类攻击。一项在线实验（N = 526）发现，与政治影响者的身份一致性显著影响了人们对帖子准确性、事实核查者可信度、对目标新闻媒体的信任和分享意图的看法。将事实核查归因于人工智能并没有提高其有效性，即使在机器启发式的坚定信徒中也是如此。然而，提供事实核查过程的透明度减少了两党在事实核查人员可信度方面的差距。讨论了理论和实践意义。

引用次数: 0

Depressed individuals’ support seeking in online communities: The role of anonymity, self-disclosure, and privacy skills 抑郁个体在网络社区寻求支持：匿名、自我表露和隐私技巧的作用

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108863

Jesse King, Amy L. Gonzales, Laurent H. Wang

Drawing from Luo and Hancock’s (2020) framework of self-disclosure in social media and psychological well-being and Büchi and Hargittai’s (2022) model for considering digital inequality when studying social media use and well-being, this study investigated how anonymity (i.e., affordance anonymity and self-anonymity) is related to emotional support seeking among people experiencing depression in online communities through increased self-disclosure, and tested how inequalities in privacy skills moderate such support-seeking processes. Results from an online survey with a sample of depressed individuals revealed that self-disclosure fully mediated the relationships between self-anonymity and online emotional support, but only when privacy skills were high. Additionally, privacy skills moderated the relationship between self-anonymity and depression disclosures such that those who had high privacy skills disclosed the most when self-anonymity was high, particularly on Reddit. Results further revealed platform differences (i.e., Reddit and Facebook) in the support-seeking processes. Qualitative analyses of the depression-related disclosures and their received comments elaborate on such platform differences. Together, the findings provide an empirical validation and extension of Luo and Hancock's framework by testing two types of anonymity and the mediating role of self-disclosure, and elaborate Büchi and Hargittai's model by testing privacy skills as a moderator of support seeking.

借鉴Luo和Hancock（2020）的社交媒体自我披露与心理健康的框架，以及b chi和Hargittai（2022）在研究社交媒体使用和幸福感时考虑数字不平等的模型，本研究调查了匿名性（即功能匿名性和自我匿名性）如何通过增加自我披露与在线社区抑郁症患者寻求情感支持相关。并测试了隐私技能的不平等如何缓和这种寻求支持的过程。一项针对抑郁个体样本的在线调查结果显示，自我表露完全中介了自我匿名和在线情感支持之间的关系，但只有在隐私技能较高的情况下才如此。此外，隐私技能调节了自我匿名和抑郁信息披露之间的关系，因此，那些隐私技能高的人在自我匿名程度高时披露的信息最多，尤其是在Reddit上。结果进一步揭示了平台（即Reddit和Facebook）在寻求支持过程中的差异。对抑郁症相关信息披露及其收到的评论的定性分析详细说明了这种平台差异。研究结果通过测试两种类型的匿名性和自我披露的中介作用，对Luo和Hancock的框架进行了实证验证和扩展，并通过测试隐私技能对支持寻求的调节作用，对b chi和Hargittai的模型进行了完善。

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引用次数: 0

The impact of message framing on end-user security:An experimental approach to prevent identity theft via smishing 消息帧对终端用户安全的影响：一种防止身份盗用的实验性方法

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108864

C. Jordan Howell , Caitlyn N. Muniz , George Burruss , Taylor Fisher , Kaylee Eckelman , David Maimon

Identity theft, exacerbated by smishing (SMS-based phishing), is inadequately addressed by countermeasures that focus primarily on technical solutions, overlooking the role of human behavior. This study proposes a theoretically grounded messaging system using fear appeals via text messages to enhance user security and prevent repeat victimization among identity theft victims. Stolen identities were sourced from illicit online platforms and divided into control and treatment groups. The treatment groups received either gain- or loss-framed fear appeals. The control group received no such messages. Simulated smishing attacks were conducted at two intervals: 1 h (wave 1) and one day (wave 2) after message receipt. The randomized controlled trial found that both gain- and loss-framed appeals reduced smishing victimization at wave 1, but only the gain-framed message remained effective at wave 2. These findings demonstrate that behavioral interventions grounded in established psychological theory can meaningfully reduce susceptibility to cyberattacks in real-world settings. By showing that gain-framed messages sustain protective behaviors longer than loss-framed ones, this study contributes to both theory and practice by identifying an effective, low-cost complement to traditional technical defenses. This study advances theoretical understanding, informs proactive policy, and highlights the need for improved data collection to enhance crime data infrastructure.

由于诈骗（基于短信的网络钓鱼）而加剧的身份盗窃，主要侧重于技术解决方案的对策无法充分解决，而忽略了人类行为的作用。本研究提出了一种基于理论的短信系统，通过短信使用恐惧呼吁来提高用户的安全性，并防止身份盗窃受害者再次受害。被盗的身份信息来自非法网络平台，并被分为对照组和实验组。治疗组分别收到了“获得”或“失去”的恐惧申诉。而对照组则没有收到这样的信息。在收到消息后的1小时（第1波）和1天（第2波）两个间隔进行模拟钓鱼攻击。随机对照试验发现，收益框架和损失框架的呼吁在第一波都减少了欺骗的受害，但只有收益框架的信息在第二波仍然有效。这些发现表明，基于既定心理学理论的行为干预可以有效地降低现实环境中对网络攻击的易感性。通过表明增益框架信息比损失框架信息维持保护行为的时间更长，本研究通过确定传统技术防御的有效、低成本补充，为理论和实践做出了贡献。这项研究推进了理论认识，为前瞻性政策提供了信息，并强调了改进数据收集以增强犯罪数据基础设施的必要性。

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引用次数: 0

Countering division with friendliness: How feeling understood by a friendly AI triggers both openness and resistance 以友善对抗分裂：友善的AI如何理解你的感受，从而引发开放和反抗

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108870

Raphael Emanuel Huber

Fostering understanding across divides could counter societal polarization but also risks reinforcing existing views. Using AI chatbots to experimentally induce interpersonal experiences, it was tested whether feeling understood enhances openness to opposing information. Across four studies (N = 1839), participants engaged in 6-min chatbot conversations before reading articles that challenged their vaccination or climate change views. These conversations were experimentally varied to be neutral, deflective, friendly, corrective, or understanding-focused. Both understanding-focused and friendly conversations increased feelings of understanding, enhancing perceived credibility of opposing information and predicting counter-attitudinal behavioural intentions—mediated paths that persisted at a 60-day follow-up. While experimental mediation effects were small but consistent (β = 0.02–0.06), correlational relationships were robust (β = 0.12–0.41). The effects were strongest among anti-vaccination and climate-sceptic participants. However, despite producing the strongest initial effects on credibility and intentions, random topic friendly conversations also generated suppressed negative direct effects that became apparent at follow-up, suggesting dual systems: one that automatically responds to social cues, and another that simultaneously detects inauthenticity. Factual corrections, initially without impact, showed positive effects at follow-up. These findings illuminate feeling understood as fundamental to bridging divides—powerful enough that even artificial displays activate openness, yet suggesting potential resilience through simultaneous authenticity detection, with implications for defending against cognitive warfare.

促进跨越分歧的理解可以对抗社会两极分化，但也有强化现有观点的风险。利用人工智能聊天机器人实验性地诱导人际体验，研究人员测试了感觉被理解是否会增强对相反信息的开放性。在四项研究中（N = 1839），参与者在阅读挑战他们的疫苗接种或气候变化观点的文章之前进行了6分钟的聊天机器人对话。在实验中，这些对话分为中性的、偏转的、友好的、纠正的或以理解为中心的。以理解为中心的对话和友好的对话都增加了理解的感觉，增强了对立信息的感知可信度，并预测了在60天的随访中持续存在的反态度行为意图介导的路径。虽然实验中介效应较小但一致（β = 0.02-0.06），但相关关系很强（β = 0.12-0.41）。这种影响在反对接种疫苗和气候怀疑论者中最为明显。然而，尽管对可信度和意图产生了最强烈的初始影响，随机的话题友好的谈话也产生了被抑制的负面直接影响，在后续变得明显，这表明了双重系统：一个自动响应社会线索，另一个同时检测不真实性。事实更正，最初没有影响，在随访中显示出积极的效果。这些发现阐明了被理解为弥合分歧的基础的感觉——强大到即使是人为的展示也能激活开放性，但同时通过真实性检测表明潜在的弹性，这意味着防御认知战。

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引用次数: 0

Personalizing explanations in AI-based decisions: The effects of personalization and (mis)aligning with individual preferences 基于人工智能的决策中的个性化解释：个性化和（错误地）与个人偏好一致的影响

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108854

Richard Uth , Nelli Niemitz , Isabel Valera , Markus Langer

The increasing reliance on AI-based decision-making in high-stakes contexts underscores the need for transparency and justice. Here, negative outcomes drive individuals affected by AI-based decisions to seek actionable explanations that enable them to realize what they can do to achieve a better future outcome. However, actionability is subjective, varying across individuals and contexts. Personalization of explanations has been proposed to address this variability, but insights on personalized explanation processes, their potential, and challenges are scarce. This paper investigates the impact of personalization and (mis)alignment with individual needs and preferences in explanations for AI-based decisions through an experimental online study simulating denied loan applications. In a within-participants design (N

=

255), participants ranked the actionability of decision-relevant features and experienced five explanation conditions: personalized directive explanations based on the most, second most, or least actionable feature (as ranked by participants); a non-personalized directive explanation highlighting a random feature; and no explanation. In line with justice theory, our results show that any explanation was better than none, and that personalized explanations led to more favorable reactions than non-personalized explanations, enhancing perceptions of justice and attractiveness of the bank. Closer alignment with preferences had only small positive effects, mainly for attractiveness. These findings highlight that even simple ranking-based approaches can make explanations more effective and accessible without requiring technical expertise while cautioning against offering superficial control. This study provides insights into the effects of ranking-based personalization, informing the design of explainability tailored to diverse user needs and addressing ethical and practical considerations in personalization.

在高风险环境中越来越依赖基于人工智能的决策，凸显了透明度和正义的必要性。在这里，负面结果会促使受人工智能决策影响的个人寻求可行的解释，使他们意识到自己可以做些什么来实现更好的未来结果。然而，可操作性是主观的，因个人和环境而异。解释的个性化已经被提出来解决这种可变性，但是对个性化解释过程、它们的潜力和挑战的见解很少。本文通过一项模拟贷款申请被拒的实验性在线研究，研究了个性化和（错误地）与个人需求和偏好在解释基于人工智能的决策时的影响。在参与者内部设计（N = 255）中，参与者对决策相关特征的可操作性进行排名，并经历五种解释条件：基于最具可操作性、第二具可操作性或最不具可操作性特征的个性化指令解释（根据参与者的排名）；强调随机特征的非个性化指令解释；也没有解释。与公平理论一致，我们的研究结果表明，任何解释都比没有解释好，个性化解释比非个性化解释导致更有利的反应，增强了对银行的公平和吸引力的感知。与偏好更紧密的联系只有很小的积极影响，主要是对吸引力的影响。这些发现强调，即使是简单的基于排名的方法也可以使解释更有效，更容易理解，而不需要技术专长，同时警告不要提供肤浅的控制。本研究提供了基于排名的个性化效果的见解，为针对不同用户需求量身定制的可解释性设计提供了信息，并解决了个性化中的道德和实际考虑因素。

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引用次数: 0

Push or Pull? Understanding switching intentions from human services to GAI agents through the PPM framework 推还是拉？通过PPM框架理解从人类服务到GAI代理的转换意图

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108874

Chunni Song , Shuhua Zhou

With the rapid advancement of artificial intelligence, generative AI agents such as ChatGPT are transforming knowledge acquisition and production, raising critical questions about why and how users switch from traditional human services to AI-driven systems. This study applies the Push–Pull–Mooring (PPM) framework to examine the determinants of users' switching intentions from traditional human services to generative AI agents. Data were collected from 451 valid survey responses across five countries (United States, United Kingdom, Singapore, China, and Pakistan). Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study assessed both first- and second-order constructs. The results showed that functional, emotional, and social dissatisfaction significantly increased users’ switching intentions, whereas perceived inefficiency did not exert a significant effect. On the pull side, attractiveness—driven by perceived intelligence, ease of use, and emotional capability—emerged as the strongest predictor of switching. Notably, emotional capability exerted a stronger influence than functional factors, underscoring the importance of affective design in GAI agents. Among mooring factors, inertia shaped by switching costs and habitual dependence negatively affected switching, while subjective norms positively moderated adoption by amplifying social influence. Theoretically, this study extended the PPM framework to AI-mediated knowledge services by identifying emotional capability as a novel pull factor in technology adoption. Practically, the findings highlighted the need to improve empathetic responsiveness, reduce switching costs through user-friendly onboarding, and tailor strategies to regional disparities in digital literacy. Together, these insights provided a cross-cultural perspective on user migration from human services to GAI agents, advancing understanding of human–machine interaction in the intelligent era.

随着人工智能的快速发展，像ChatGPT这样的生成式人工智能代理正在改变知识的获取和生产，提出了用户为什么以及如何从传统的人工服务转向人工智能驱动系统的关键问题。本研究应用推拉系泊（PPM）框架来检查用户从传统人类服务转向生成式人工智能代理的意图的决定因素。数据收集自5个国家（美国、英国、新加坡、中国和巴基斯坦）的451份有效调查回复。使用偏最小二乘结构方程模型（PLS-SEM），研究评估了一阶和二阶结构。结果表明，功能、情感和社交方面的不满意显著增加了用户的转换意图，而感知效率低下对用户的转换意图没有显著影响。在吸引力方面，由感知到的智力、易用性和情感能力驱动的吸引力成为了最强有力的转换预测因素。值得注意的是，情绪能力比功能因素的影响更大，这强调了情感设计在GAI代理中的重要性。在系泊因素中，由转换成本和习惯依赖形成的惯性负向影响转换，而主观规范通过放大社会影响正向调节采用。从理论上讲，本研究通过识别情感能力作为技术采用的新拉动因素，将PPM框架扩展到人工智能介导的知识服务。实际上，调查结果强调需要提高移情反应能力，通过用户友好的入职方式降低转换成本，并根据数字素养的地区差异量身定制战略。总之，这些见解提供了从人工服务到GAI代理的用户迁移的跨文化视角，促进了对智能时代人机交互的理解。

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引用次数: 0

Social media observation of ex-partners is associated with greater breakup distress, negative affect, and jealousy 社交媒体上对前伴侣的关注与更大的分手痛苦、负面影响和嫉妒有关

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-19 DOI: 10.1016/j.chb.2025.108869

Tara C. Marshall

After a romantic breakup, many people observe ex-partners on social media. However, it remains unclear whether observation has downstream consequences for breakup recovery, whether it matters if the observation is active (intentional) or passive (unintentional), and whether attachment anxiety plays a moderating role. The present studies (N = 762) used longitudinal, experimental, and daily diary methods to clarify our understanding. In Study 1, active observation on Facebook predicted heightened breakup distress within three months of a breakup and six months later, especially for people higher in anxious attachment. Study 2 found that experimentally enhancing the salience of observation increased negative affect and jealousy. In Studies 3 and 4, active observation on sites like Instagram and Snapchat was associated with greater same-day and next-day breakup distress, whereas passive observation was associated with greater same-day negative affect. Overall, these results suggest that reducing social media observation may assist breakup recovery.

失恋后，很多人会在社交媒体上关注前任。然而，目前尚不清楚观察是否对分手恢复有下游影响，观察是主动的（有意的）还是被动的（无意的），以及依恋焦虑是否起调节作用。目前的研究（N = 762）采用纵向、实验和日常日记的方法来澄清我们的理解。在研究1中，在Facebook上的积极观察预测，分手后三个月内和六个月后，尤其是对焦虑依恋程度较高的人来说，分手痛苦会加剧。研究2发现，通过实验增强观察的显著性会增加消极情绪和嫉妒心。在研究3和4中，在Instagram和Snapchat等网站上的主动观察与更大的当天和第二天分手痛苦有关，而被动观察与更大的当天负面影响有关。总的来说，这些结果表明减少社交媒体观察可能有助于分手后的恢复。

引用次数: 0

Association between smartphone use and mental health outcomes among community-dwelling older adults: A 2-year follow-up study 在社区居住的老年人中，智能手机使用与心理健康结果之间的关系：一项为期2年的随访研究

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-18 DOI: 10.1016/j.chb.2025.108868

Fernanda Martins Bertocchi , Giancarlo Lucchetti , Aparecida Carmem De Oliveira , Luiza Cunha Martins , Maria Eduarda Duarte de Oliveira , Vivian Maria Gomes de Oliveira , Alessandra Lamas Granero Lucchetti

Although evidence points to worse outcomes in young people and adults who more frequently use smartphones, cross-sectional studies involving older adults indicate possible benefits. However, longitudinal studies on this association remain scarce. This study aims to evaluate the longitudinal association of smartphone use with mental health (i.e., depression, anxiety, and stress) in community-dwelling older adults. A 2-year longitudinal, population-based follow-up study was conducted in a low-income city in Brazil. The use of technology was investigated through the use or not of smartphones, duration of use, and importance given to these devices. Mental health was assessed as an outcome using the Depression Anxiety Stress Scale-21 scale. Linear regression models adjusted for baseline measurements were employed. In total, 507 older adults were followed for 2 years. An increase in the use, importance given to, and number of hours of smartphone use was observed after the 2-year follow-up. We also found significant differences in the reduction of depression (Beta = −2.268; CI95 %: 4.137 to −0.398; Std. Beta = −0.141; p = 0.018) and anxiety (Beta = −1.641; CI95 %: 3.263 to −0.020; Std. Beta = −0.118; p = 0.047) while comparing users and non-users of smartphones. Similarly, among smartphone users (n = 117), more hours of use were associated with a greater reduction in anxiety (Beta = −0.710; CI95 %: 1.188 to −0.233; Std. Beta = −0.270; p = 0.004) and stress (Beta = −0.778; CI95 %: 1.430 to −0.125; Std. Beta = −0.219; p = 0.020). The findings of this study support the paradox of technology use among older individuals, affirming, through a longitudinal design, that smartphone use is associated with better outcomes. Encouraging smartphone use and promoting the digital inclusion of older adults in the digital world is important.

尽管有证据表明，更频繁使用智能手机的年轻人和成年人的结果更糟，但涉及老年人的横断面研究表明，智能手机可能会带来好处。然而，关于这种关联的纵向研究仍然很少。本研究旨在评估社区老年人智能手机使用与心理健康（即抑郁、焦虑和压力）的纵向关联。在巴西的一个低收入城市进行了一项为期2年的纵向、基于人群的随访研究。通过使用或不使用智能手机、使用时间和对这些设备的重要性来调查技术的使用情况。使用抑郁焦虑压力量表-21量表评估心理健康状况。采用基线测量调整后的线性回归模型。总共有507名老年人被跟踪了2年。在2年的随访后观察到，智能手机的使用、重要性和使用时间都有所增加。我们还发现，在比较智能手机用户和非用户时，抑郁（Beta = - 2.268; CI95 %: 4.137至- 0.398;Std. Beta = - 0.141; p = 0.018）和焦虑（Beta = - 1.641; CI95 %: 3.263至- 0.020;Std. Beta = - 0.118; p = 0.047）的减少有显著差异。同样，在智能手机用户（n = 117）中，使用时间越长，焦虑（Beta = - 0.710; CI95 %: 1.188至- 0.233；标准Beta = - 0.270; p = 0.004）和压力（Beta = - 0.778; CI95 %: 1.430至- 0.125；标准Beta = - 0.219; p = 0.020）的减少程度越高。这项研究的发现支持了老年人使用科技产品的悖论，通过纵向设计证实，智能手机的使用与更好的结果有关。鼓励使用智能手机，促进老年人融入数字世界，这一点非常重要。

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引用次数: 0

Human-bot symbiosis and misinformation propagation: Exploring the mechanisms of social bot participation from the perspective of emotional contagion 人-机器人共生与错误信息传播：从情绪传染的角度探讨社会机器人参与机制

IF 8.9 1区心理学 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in Human Behavior

Pub Date : 2025-11-17 DOI: 10.1016/j.chb.2025.108865

Shiying Zhang , Qing Ke , Linyi Zhang , Xuhong Zhang , Aijie Li

The rise of human-bot symbiosis has exacerbated the viral spread of misinformation, yet understanding how human-bot interactions shape its propagation remains underexplored. Building on emotional contagion theory, this study introduced a novel four-dimensional evaluation framework to examine emotional contagion in misinformation propagation. For the first time, we uncovered three distinct patterns: balanced contagion, echo chamber, and explosive contagion, revealing unique mechanisms of emotional diffusion in the spread of online misinformation. By comparing different machine learning algorithms, the study employed the best-performing CatBoost to effectively detect social bot accounts in the Chinese context. Leveraging a large-scale misinformation dataset, we analyzed the interaction patterns of different dissemination agents It was found that social bots tend to repost content from high-influence users. We also investigate the strategic roles of dissemination agents in the misinformation propagation network, and found that social bots primarily act as “endpoint disseminators”. This study not only extends the application scenarios of emotional contagion theory but also provides empirical evidence for social bot detection and misinformation governance.

人机共生的兴起加剧了错误信息的病毒式传播，但对人机互动如何影响错误信息传播的理解仍未得到充分探索。本研究以情绪传染理论为基础，引入一个新的四维评估框架来考察错误信息传播中的情绪传染。我们首次发现了三种不同的模式：平衡传染、回音室和爆炸性传染，揭示了网络错误信息传播中情感扩散的独特机制。通过比较不同的机器学习算法，该研究采用了性能最好的CatBoost来有效地检测中国背景下的社交机器人账户。利用大规模的错误信息数据集，我们分析了不同传播代理的交互模式，发现社交机器人倾向于转发来自高影响力用户的内容。我们还研究了传播代理在错误信息传播网络中的战略角色，发现社交机器人主要充当“端点传播者”。本研究不仅拓展了情绪传染理论的应用场景，也为社交机器人检测和错误信息治理提供了经验依据。

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引用次数: 0