In the realm of social media, information dissemination is pivotal, yet it is tainted by the proliferation of misinformation propagated by both bots and humans, bearing consequential impacts on individuals and society. To address this issue, social media platforms have implemented removal, reduction, and informing interventions, acting as deterrence mechanisms to dissuade users from engaging in the spread of misinformation. Nonetheless, the sustained effectiveness of these interventions on bots and humans remains unclear. Drawing on deterrence theory, this study examines the efficacy of social media interventions on bots and humans sharing health misinformation. Our results show most interventions have sustained effects on bots and their activities for years after implementation. However, the interventions may not have significant deterrence effects on humans and their activities. Our findings offer important theoretical and practical implications by highlighting the importance of studying both bots and humans and developing creative strategies to tackle health misinformation dissemination.

Smart cities are promising communities that leverage intelligent technologies to connect citizens through internet devices, thereby improving their quality of life. This is especially crucial for citizens with disabilities, who face significant challenges in urban living. This paper reviews, summarises, and synthesises the current literature on smart cities for people with disabilities. The analysis is grounded in a sociotechnical framework and the Quadruple Helix Model, with a focus on effective collaborations among various stakeholders to provide sustainable and inclusive smart cities. In examining 83 peer-reviewed articles, our literature analysis reveals that, despite the growing number of studies on smart cities, very few have explored the challenges and opportunities for people with disabilities from a socio-technical and collaborative perspective. Accordingly, we call for interdisciplinary research to understand how smart technologies should be developed, implemented, and used to address the special needs of people with disabilities and to build inclusive and technologically advanced smart cities. This study contributes to both research and practice by highlighting the underexamined area of inclusive smart cities. It provides a conceptual framework that can serve as a guideline to address and enhance the understanding of the critical role of smart cities in fostering social inclusion.

Unjust algorithmic recommendations can lead decision makers to discriminatory choices, risking harm to individuals or groups. This study addresses this concerning phenomenon and examines its implications. In an experimental study involving 122 managers, we found that algorithmic injustice causes discriminatory decisions without heightened guilt perception. Additionally, trust in data analytics outcomes moderates the impact of algorithmic injustice on discrimination and marginally influences the impact of discriminatory decision making on guilt perception. However, displacement of responsibility has no moderating effect on either relationship. These findings highlight the potential negative consequences of algorithmic decision making, showing a need for caution and awareness.

As firms are moving towards data-driven decision making, they are facing an emerging problem, namely, algorithmic bias. Accordingly, algorithmic systems can yield socially-biased outcomes, thereby compounding inequalities in the workplace and in society. This paper reviews, summarises, and synthesises the current literature related to algorithmic bias and makes recommendations for future information systems research. Our literature analysis shows that most studies have conceptually discussed the ethical, legal, and design implications of algorithmic bias, whereas only a limited number have empirically examined them. Moreover, the mechanisms through which technology-driven biases translate into decisions and behaviours have been largely overlooked. Based on the reviewed papers and drawing on theories such as the stimulus-organism-response theory and organisational justice theory, we identify and explicate eight important theoretical concepts and develop a research model depicting the relations between those concepts. The model proposes that algorithmic bias can affect fairness perceptions and technology-related behaviours such as machine-generated recommendation acceptance, algorithm appreciation, and system adoption. The model also proposes that contextual dimensions (i.e., individual, task, technology, organisational, and environmental) can influence the perceptual and behavioural manifestations of algorithmic bias. These propositions highlight the significant gap in the literature and provide a roadmap for future studies.

Social media has become a strategic tool for businesses and nonprofit organizations to connect with audiences. However, no comprehensive framework exists to support the continued improvement of social media outcomes. This work draws on prior studies related to social media analytics and user engagement to develop an overarching, analytics-driven process for social content strategy development and improvement. The process provides firms with a set of procedures to regularly assess competitors’ and possibly their own content topic posting activities. It then outlines steps to measure the influence of content topics and post characteristics on engagement outcomes and use the garnered insights to drive future posting activities. A proof-of-concept case in the healthcare context is presented to demonstrate the feasibility of the proposed process.

Recently, the massive and diverse fake news from politics to entertainment and health has amplified the social distrust problem and has become a big challenge for the society and research community. The existing fake news detection methods are mostly designed for either a specific domain or require huge labeled data from various domains. If there is not enough labeled data in a certain domain, existing models may not work well for detecting fake news from that domain. To overcome these limitations we propose a novel framework based on multisource domain adaptation and weak supervision for early fake news detection. The framework transfers sufficient labeled source domains’ knowledge into a target/new domain with limited or even no labeled data by the multi-source domain adaptation, and applies researchers’ prior knowledge about fake news to the target domain by the weak supervision. The weak supervision assigns the weak labels to the unlabeled samples in the target domain through known heuristic rules. Our experimental results show that our approach outperforms 7 state-of-the-art methods in three real-world datasets. In particular, our model achieves, on average, 5.2% higher accuracy than the best baseline. Our model with a more advanced encoder can further boost the performance by 3.7%. The code is available at this clickable link.