User Contributions Research Articles

Social media attention and retail investor behavior: Evidence from r/wallstreetbets

This paper studies the influence of Reddit social media attention on investor risk-taking and welfare using individual trading data from a large trading platform. We show that attention generated on the subreddit r/wallstreetbets (WSB) spurs uninformed trading, also in short positions. WSB attention increases overall risk levels as investors trade riskier stocks and allocate larger portfolio shares to new positions. WSB attention significantly reduces holding period returns (HPRs). Positions created when WSB attention is at its highest realize -8.5% HPRs, while the average HPR across all investments is positive. Investment decisions of retail investors are more likely to be based on emotional responses to angry and disgusted user contributions on Reddit rather than analyst investment advice.

Exploring user-led spatial practice and user design tools in urban low and middle-income residences: a study based in Kitulwatte, Colombo

In many developing Asian countries, informal user-led architectural production is prevalent, with dwellings designed and built largely without professional input. Unlike traditional architectural practices, where design, construction, and inhabitation are distinct phases, users perceive these aspects as interconnected, influencing each other continuously and simultaneously, resulting in unexpected and rich spatial experiences. This study focuses on informal user-led architectural production in Sri Lanka, examining the design methods users employ through Jonathan Hill’s framework of user creativity types. By analysing the architectural features of user-designed homes, identifying manifestations of user creativity, and assessing the influence of user-led production on urban adaptability and vibrancy, this research aims to underscore the creative and significant architectural contributions of users. Through an analysis of four residences in Kitulwatte, Colombo, the research reveals the conscious and subconscious processes inherent in user-led design, highlighting their substantial contribution to the urban fabric. This holistic approach transforms architecture from a static product into a dynamic process that continually evolves to meet individual needs. By elucidating the unique design tools and approaches employed by users, this paper enriches the discourse on architectural agency and challenges conventional notions of authorship, showcasing the profound impact of user influence on shaping the built environment.

Fighting Misinformation on Social Media: An Empirical Investigation of the Impact of Prominence Reduction Policies

Misinformation has dire implications for both public welfare and the operational aims of user-generated content platforms. As a result, platforms have adopted various content moderation policies aimed at decreasing the volume and impact of misinformation. However, implementing new platform policies runs the risk of decreasing user contribution and alienating core users, and results regarding the efficacy of such policies are mixed. Herein, we empirically assess a prominence reduction policy applied to a problematic group that is high in misinformation. The goal of this policy is to reduce the visibility of misinformation on the platform (rather than deleting misinformation or banning users). The results show that while prominence reduction diminishes misinformation dissemination in the focal group, this method also results in a spillover of misinformation to topically related spaces. This spillover is short-lived and driven primarily by a small set of problematic users. As misinformation is not contagious, we find that this spillover to external groups diminishes over time. Finally, prominence reduction is found to have no impact on non-misinformation contribution on the studied platform. The findings of this study have important implications for platform operations and provide useful recommendations for managers regarding effective ways to reduce the spread of misinformation.

The Future of Content Marketing: Developing KPIs for Metaverse Content Marketing

In recent times, the Metaverse technology has gathered attention due to its futuristic capabilities. Being a relatively new concept, it is important to keep on exploring the possibilities of this technology. Content Marketing is a marketing method, which has been tried and tested by marketers and researchers since the beginning of internet. The study envisions the use of Metaverse as a platform for content marketing activities of the brands. To bring this vision into perspective, the study identifies a set of Key Performance Indicators (KPIs) that can be used to assess the performance of content marketing strategies used by marketers in the Metaverse platforms. The paper follows an analytical research approach to conceptualize the Key Performance Indicators for measuring marketing performance within the Metaverse ecosystem. By analyzing existing literature, the study extends conventional media performance metrics to develop KPIs for evaluating marketing performance inside Metaverse. The study identifies three broad KPIs namely Traffic, Engagement and Brand Awareness. In addition to this, the study also identified eight metrics specific to measuring content marketing performance inside the Metaverse. These include, Spatial Analysis, User Journey Analysis, Virtual Events Metrics, User Contribution and Creation, Time Spent, Frequency of Interaction, Community Growth and Cost Metric.

Opening the Metaverse: The Next Frontier in Sport and Leisure

The metaverse is at the forefront of digital innovations transforming sport and leisure. The metaverse, a shared digital environment accessible via the Internet, currently operates as separate entities, challenging the promise of an integrated space. This highlights a critical dilemma—the need to balance centralized control with individual agency. The purpose of this article is to examine the metaverse’s development at the intersection of technological innovation and user interaction. Guided by a uses and gratifications perspective, we underscore the critical role of collaboration between firms shaping the metaverse and the individuals enhancing it. Despite centralization challenges, maintaining a balance between firm-led initiatives and user contributions is instrumental for an interactive metaverse. Addressing this tension will help shape whether the metaverse evolves into a shared space or remains constrained by centralized control. Overall, the metaverse is poised to impact sport and leisure, making it a crucial area for exploration and development.

Impacts of generative AI on user contributions: evidence from a coding Q &A platform

AbstractThis study investigates the short-term impact of generative AI, exemplified by the introduction of ChatGPT, on user contributions in a coding Q&A platform. We find that the introduction of ChatGPT led to a reduction in the number of high-quality answers provided by users, particularly among highly engaged contributors, despite an overall increase in answers. We identify two key mechanisms: (1) increased perceived question sophistication despite no actual change in content and (2) reduced motivation of loyal users in providing answers in the face of AI-generated alternatives. The findings suggest that while generative AI can facilitate value creation on user-generated content (UGC) platforms, it also poses challenges in retaining core contributors and managing content quality. The paper contributes to the literature on the impact of AI adoption on platforms and suggests practical implications for UGC platform management, such as the need for AI content disclosure measures to retain engaged users.

Unsupervised Social Bot Detection via Structural Information Theory

Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, for example, Graph Neural Network, Transformer, and so on, which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective, and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: posting type distribution , posting influence , and follow-to-follower ratio . Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Third, a new community labeling method is presented to distinguish social bot communities by computing the user’s stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with 10 representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on 4 real social network datasets.

A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design.

Recent advances in text-conditioned generative models have provided us with neural networks capable of creating images of astonishing quality, be they realistic, abstract, or even creative. These models have in common that (more or less explicitly) they all aim to produce a high-quality one-off output given certain conditions, and in that they are not well suited for a creative collaboration framework. Drawing on theories from cognitive science that model how professional designers and artists think, we argue how this setting differs from the former and introduce CICADA: a Collaborative, Interactive Context-Aware Drawing Agent. CICADA uses a vector-based synthesis-by-optimisation method to take a partial sketch (such as might be provided by a user) and develop it towards a goal by adding and/or sensibly modifying traces. Given that this topic has been scarcely explored, we also introduce a way to evaluate desired characteristics of a model in this context by means of proposing a diversity measure. CICADA is shown to produce sketches of quality comparable to a human user's, enhanced diversity and most importantly to be able to cope with change by continuing the sketch minding the user's contributions in a flexible manner.

Differentially Private Stream Processing at Scale

We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system - Differential Privacy SQL Pipelines (DP-SQLP) - is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google. Towards designing DP-SQLP we make both algorithmic and systemic advances, namely, we (i) design a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys, and can scale to one billion keys that users have contributed, (ii) design a preemptive execution scheme for DP key selection that avoids enumerating all the keys at each triggering time, and (iii) use algorithmic techniques from DP continual observation to release a continual DP histogram of user contributions to different keys over the stream length. We empirically demonstrate the efficacy by obtaining at least 16× reduction in error over meaningful baselines we consider. We implemented a streaming differentially private user impressions for Google Shopping with DP-SQLP. The streaming DP algorithms are further applied to Google Trends.

The role of aesthetics in tourist satisfaction in the Ghanaian hospitality industry

PurposeThe study examines the role of aesthetics in tourist satisfaction in the Ghanaian hospitality industry. Design/methodology/approachThe study is quantitative and cross-sectional in nature. A total of 5 tourist destinations and 350 tourists were selected using purposive sampling technique. Data for the study was gathered using self-reported questionnaires and analysed using Statistical Package for Social Sciences (SPSS). A total of 300 retrieved questionnaires representing 85.7 % of response rate was used for data analysis. FindingsThe study results proved that the three main types of aesthetics that influence tourists' visitation to tourist destinations are visual product aesthetics, package aesthetics and taste aesthetics. The study also discovered that visual product aesthetics, package aesthetics and taste aesthetics had significant positive correlations with tourist satisfaction. Again, the results established that, aesthetic types (visual product aesthetics and package aesthetics) have significant positive impact on tourists' satisfaction whereas taste aesthetics has a negative effect on tourists’ satisfaction. Research limitations/implicationsThe Ghanaian hospitality sector should push creativity to new heights by ensuring that venues change their appearance and ambiance through the use of creative lights, music, and food setups, so as to often attract consumers and offer them a completely unique experience each time. The hospitality sector also need to provide more effective and dependable channels for user complaints, recommendations, and contributions, as well as consumer feedback systems. Originality/valueThe study adds to literature on aesthetics and tourist satisfaction as well as contributes to the agenda towards tourist destination sustainability.

FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing

In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system’s ability to defend against malicious user attacks was validated. The experimental results demonstrate the method’s effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission.

The need for scientific-area-related indicators for effective energy planning in higher education institutions

The quest for improving energy efficiency is transversal to all areas of society. Higher education institutions represent an important sector in this quest due to their high demand, but also for the role model that they can play in educating energy-efficient citizens and piloting new approaches and experiences. Thus, decreasing energy consumption in higher education institutions, in addition to reducing the carbon footprint, contributes to ameliorating countries' energy bills, and, most importantly, contributes to a more sustainable society. The purpose of the paper, based on the energy consumption of the University of Minho, Portugal, between 2007 and 2022, is threefold: first, to evaluate how energy consumption and associated carbon footprint indicators have performed under a sustainable strategy program, second to reflect on total energy and specific energy indicators, and lastly to emphasize the need to improve energy metering and planning systems to account for the distinctive needs of the different scientific area buildings. This is not only relevant but also rare to find in scientific literature. Findings suggest that UMinho's energy consumption is in line with the numbers reported in the literature. Moreover, detailed indicators, specified by scientific area building, show diverse patterns in energy use, demonstrating the limitations of an overall analysis of buildings in the university campi. The results show that energy efficiency improved as a result of the implemented action plan, and demonstrate the need for detailed and specific indicators that reflect the different needs of each scientific area. The results provided by this refinement call for the design of tailored initiatives to decrease energy consumption, since they allow the planning of specific measures and programs for different energy use patterns, and therefore improve their efficiency. Finally, the preliminary results of the analysis of building specific energy use point to the need for more detailed data on hourly and daily consumption and academic term given the relative contribution of users' behaviour.

Customisation and co-creation revisited: Do user types and engagement strategies matter for product innovation success?

User engagement is widely recognised as beneficial for the innovation efforts of firms scarce with internal resources but our knowledge about the relevance of user types and engagement strategies is scarce and mostly limited to the front-end phases of the innovation process. To fill this gap, we use data from over 10,000 companies operating in four emerging innovation ecosystems in Central Europe and examine the contribution of users to product development, distribution, market positioning and the commercialisation of innovations. The results show that user engagement through customisation and co-creation promotes the success of the innovation process, with the impact of co-creation being much stronger. Consumers, intermediary users (businesses) and public sector users play different roles at different stages of innovation. These findings provide important insights for firms seeking to optimise their user engagement strategies in product innovation management.

Heritage Identification of monuments using Deep Learning Techniques

The Heritage Identification of Monuments web application employs different Machine learning technologies like Convolutional Neural Networks(CNN) and Deep Learning, to identify historical sites and monuments. The project focuses on training machine learning model to identify heritage sites and monuments with the use of CNN to extract features from images and help in identifying historical monuments. This project focuses on collection data from satellite images and user contributions, thus expanding the database and offering information on different identified monuments. This initiative aims to promote general awareness among the public and promote heritage preservation with labelled datasets and machine learning technologies. Machine learning techniques have evolved over the years, in this project we highlight the main techniques and algorithms by which we extract data from images and compare different mathematical models to test the accuracy. Additionally, the web-app helps to create a good reach among the masses and raise awareness. Keywords— Machine learning, Deep Learning, Convolution Neural Network (CNN), Pattern Recognition, Indian Monuments, Open source.

Something Happened with the Way We Work: Evaluating the Implementation of the Reducing Coercion in Norway (ReCoN) Intervention in Primary Mental Health Care.

Current policies to reduce the use of involuntary admissions are largely oriented towards specialist mental health care and have had limited success. We co-created, with stakeholders in five Norwegian municipalities, the 'Reducing Coercion in Norway' (ReCoN) intervention that aims to reduce involuntary admissions by improving the way in which primary mental health services work and collaborate. The intervention was implemented in five municipalities and is being tested in a cluster randomized control trial, which is yet to be published. The present study evaluates the implementation process in the five intervention municipalities. To assess how the intervention was executed, we report on how its different elements were implemented, and what helped or hindered implementation. We assessed the process using qualitative methods. Data included detailed notes from quarterly progress interviews with (i) intervention coordinators and representatives from (ii) user organisations and (iii) carer organisations. Finally, an end-of-intervention evaluation seminar included participants from across the sites. The majority of intervention actions were implemented. We believe this was enabled by the co-creating process, which ensured ownership and a good fit for the local setting. The analysis of facilitators and barriers showed a high degree of interconnectedness between different parts of the intervention so that success (or lack thereof) in one area affected the success in others. Future implementation should pay attention to enhanced planning and training, clarify the role and contribution of service user and carer involvement, and pay close attention to the need for implementation support and whether this should be external or internal to services. It is feasible to implement a complex intervention designed to reduce the use of involuntary admissions in general support services, such as the Norwegian primary mental health services. This could have implications for national and international policy aimed at reducing the use of involuntary care.

A global dataset of publicly available dengue case count data

OpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.

Platform Organizations and Fields: Exploring the Influence of Field Conditions on Platformization Processes

This article contributes to the debate on the platformization of society by presenting a theoretical framework that describes the influence of strategic action fields (SAFs) on the emergence of platform architectures. It shifts the focus from already successful platforms to contested processes of platformization, understood as the gradual reorganization of fields around the architectures of digital platforms. To assess the influence of fields on platformization processes, the article combines the architectural view of platforms and the theory of SAFs. The resulting framework emphasizes that platform organizations may occupy powerful positions in fields, but they also depend on the contributions of users and complementors to establish these positions in the first place. Therefore, the forms and outcomes of platformization are influenced by the conditions in SAFs, particularly the stage in the field’s temporal trajectory and the field position of platform organizations. By re-examining a growing body of empirical research, the article distinguishes platformization processes in emerging, stable, and transforming fields, and presents scenarios for each case. By theorizing a wide range of platformization processes and outcomes beyond disruption, the article offers a nuanced understanding of the proliferation of digital platforms and the influence of platform organizations in contemporary societies.

Nonverbal Peer Feedback and User Contribution in Online Forums: Experimental Evidence of the Role of Attribution and Emotions

Peer feedback is often associated with an increase in the contributions of members in online communities. Verbal feedback (such as a review) can give details about how the recipient can improve their contribution, but it requires the recipient to read and process the feedback. Conversely, nonverbal feedback (such as an upvote) is easy to comprehend but does not convey much helpful information. Prior studies have mainly focused on the impact of verbal feedback. However, little has been done to explore the underlying mechanism of the effect of nonverbal peer feedback on people’s tendency to contribute more. We present two experimental studies conducted on Amazon Mechanic Turk. Study 1 demonstrates how verbal and nonverbal feedback impact user contributions differently. Next, building on attribution-emotion-action theory, we use Study 2 to establish a causal mechanism between nonverbal feedback and users’ knowledge contribution. Specifically, users who receive nonverbal peer feedback make internal and external attributions, which in turn impact their emotions and contribution decisions. We find that users receiving more positive feedback attribute this in equal measure internally to perceived self-efficacy and externally to perceived fairness, whereas users who receive negative feedback attribute it more to the lack of perceived fairness of peer feedback. These findings have important implications for both content-sharing platforms and researchers trying to better understand the drivers of online content-sharing behavior.

An empirical investigation into social and platform endorsement of user-generated content in the online Q&A community

Online communities employ recognition mechanisms to signal their acknowledgment of and support for the contributions of their members. These endorsements usually take the form of platform conferment, such as badges, or user interactions via social media, such as Facebook’s “Like” button. Although previous research has shown that both types of endorsements can significantly impact users’ online contributions, little attention has been paid to the relative effectiveness of and interaction between these two common endorsement formats. By analyzing a sample collected from China’s largest online social Q&A platform and conducting multiple robustness checks, we demonstrate that while both social and platform endorsements positively impact users’ online contributions, the formers are largely more effective than the latter in driving user contributions. Moreover, we find a complementary relationship between social and platform endorsements, suggesting that the combination of the two can further stimulate content generation. Our findings offer practical insights for content providers seeking to encourage content generation in online communities.

User circularity practices: Adopting a user stewardship perspective for a circular economy

Humanity is depleting the planet’s natural resources at an unsustainable rate. The pursuit of a circular economy is a strong, viable means of reversing this trend; however, it will require users to take responsibility for the proper application and protection of resources for future generations. While the daily practices of users play a significant role in enabling a circular economy, this role has largely been overlooked in current literature. Our research synthesizes knowledge from the circular economy and marketing literatures, and draws on stewardship theory to better understand the user’s role in the circular economy. Specifically, we introduce a resource stewardship framework from a user perspective. This framework specifies a set of user circularity practices to minimize the extraction of finite resources, while conserving and regenerating resources already in circulation for future use. These practices occur at various stages in the resource life cycle and include minimizing the sourcing and use of finite resources, (re)designing products and services to use less resources, and optimizing the value potential from resources through extending their life cycle and recovering resources for future use. With this framework, we redefine the role of users as resource stewards and advance the rather narrow and fragmented considerations of user contributions to the circular economy, laying the foundation for more caring and responsible users and a future research agenda.