Search Results (34)

The rapid diffusion of user-generated content on social networks has amplified the reach of fake news, creating an urgent need for detection methods that combine scalability with epistemic robustness. This paper proposes a Collective Intelligence (CI) framework for fake news detection that formalizes the crowd assessing a news item as an intelligent collective characterized by diversity, independence, decentralization, and aggregation. A directed weighted graph is used to represent the collective, where vertices denote users, edge weights encode reputation-derived influence, and each user contributes a veracity judgment together with a set of stance-bearing content and context features. We instantiate collective independence through a reputation-based influence measure adapted from prior work and integrate the resulting independence-aware weights into a two-stage aggregation pipeline: (i) a supervised classifier that produces machine-generated veracity scores from news content, and (ii) a consensus operator that fuses machine scores with independence-weighted crowd signals. The framework is evaluated on the GossipCop split of the FakeNewsNet corpus, treating the tweet propagation graph associated with each news item as the collective. Experimental results show that the CI-based model outperforms content-only and unweighted crowd baselines in accuracy, precision, recall, and F1-score, and that independence-aware aggregation contributes the largest incremental gain among the four CI principles. These findings support the view that treating social-media crowds as structured collectives, rather than as bags of independent votes, yields measurable robustness against coordinated misinformation. Full article

Background/Objectives: Diagnostic performance of angiography-derived physiological measures has been benchmarked against two-dimensional (2D) and three-dimensional (3D) quantitative coronary angiography (QCA), which are known for their poor correlation with hemodynamic lesion severity. Relying on the statistical concept of the wisdom of the crowd, we devised a human-performance reference for FFR surrogates, called vox populi FFR (vpFFR), and examined the comparative diagnostic performance of vpFFR, as well as 2D- and 3D-QCA, using invasively measured FFR as the gold standard. Methods: Analyses were performed in a single-center, prospective registry of consecutive FFR procedures. We calculated vpFFR as a mean of five independent, blinded predictions of the invasively measured FFR. Pearson’s correlation coefficient and receiver operating characteristic (ROC) curve analyses were used for diagnostic performance comparisons. Results: In 116 patients (156 vessels), Pearson’s correlation coefficients for vpFFR, 2D-, and 3D-QCA with invasively measured FFR are 0.56, −0.26, and −0.01, respectively (p < 0.001, p = 0.001 and p = 0.918). vpFFR has a sensitivity of 56%, specificity of 84%, positive predictive value of 67%, and negative predictive value of 76%. It correctly classified hemodynamic severity of lesions in 73% of vessels compared to 65% and 51% for 2D- and 3D-QCA, respectively. vpFFR has a larger area under the ROC curve than 2D- and 3D-QCA for predicting positive FFR (0.78, 0.63, and 0.45, respectively, p < 0.001). Conclusions: vpFFR, a mean value of five predictions of invasively measured FFR, has moderate diagnostic performance, superior to 2D- and 3D-QCA using FFR as the gold standard, and can be used as a human-performance reference for existing and emerging angiography-derived physiological measures. Full article

Green perception underlies pro-greenspace behavior, but external stimuli and behavior are not always aligned. Understanding how residents’ perceived external green stimuli influence pro-greenspace behavior, and how the “good citizen” image (face) shapes this relationship, is essential. The study aims to deepen the understanding of the complex mechanisms driving urban residents’ pro-greenspace behavior by constructing an extended Stimulus-Organism-Response theoretical framework (C-SOR) that includes contextual factors. Using data from a 2024 field survey of 959 residents from Shanghai, China, this study employs Ordinary Least Squares (OLS) regression to examine the main effect of green perception on pro-greenspace behavior. A mediation model is employed to analyze the mediating role of nature connectedness, while a moderation model tests the moderating effect of “good citizen” image (face) on the stimulus–behavior relationship. The results show that green perception significantly promotes pro-greenspace behavior, positively influencing it through nature connectedness. However, the “good citizen” image (face) exerts a motivational crowding-out effect on green perception. Further analysis reveals individual heterogeneity in the expression of these effects across different types of pro-greenspace behavior. The findings highlight the importance of green space experience and the activation of environmental wisdom in traditional culture, offering new perspectives for developing strategies to guide pro-greenspace behavior. Full article

This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange “Tadawul” across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed ‘X’) messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm’s news. Full article

Odontogenesis, the process of tooth formation, is complex and susceptible to disruptions that can result in dental anomalies such as tooth impaction. The mandibular second premolar, though less commonly impacted than wisdom teeth, presents a unique challenge in pediatric dentistry due to its intricate etiology and the need for timely intervention. This case report aims to highlight the significance of early diagnosis and conservative management strategies in treating mandibular second premolar impaction. The case involves a pediatric patient with impacted mandibular second premolars. Initial treatment included the use of a lower removable appliance with an expansion screw to alleviate crowding, followed by a fixed space maintainer and a Haas rapid palatal expander. These interventions created the necessary space for the premolars to erupt. Self-ligating brackets were later applied, reducing friction and improving periodontal health. The patient underwent two CBCT examinations to monitor progress, which confirmed the successful eruption and alignment of the impacted premolars without the need for surgical exposure. This case underscores the effectiveness of early diagnosis and minimally invasive treatment in managing mandibular second premolar impaction. The tailored approach facilitated the natural eruption of the teeth, highlighting the importance of individualized treatment plans. Future research should focus on optimizing these conservative strategies to enhance patient outcomes in similar cases. Full article

The paper addresses the problem of distinguishing the leading agents in the group. The problem is considered in the framework of classification problems, where the agents in the group select the items with respect to certain properties. The suggested method of distinguishing the leading agents utilizes the connectivity between the agents and the Rokhlin distance between the subgroups of the agents. The method is illustrated by numerical examples. The method can be useful in considering the division of labor in swarm dynamics and in the analysis of the data fusion in the tasks based on the wisdom of the crowd techniques. Full article

This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting (“wisdom of the crowd”) and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents’ expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents’ classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods. Full article

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development. Full article

The evaluation of earthquake damage is central to assessing its severity and damage characteristics. However, the methods of assessment encounter difficulties concerning the subjective judgments and interpretation of the evaluators. Thus, it is mainly geologists, seismologists, and engineers who perform this exhausting task. Here, we explore whether an evaluation made by semiskilled people and by the crowd is equivalent to the experts’ opinions and, thus, can be harnessed as part of the process. Therefore, we conducted surveys in which a cohort of graduate students studying natural hazards (n = 44) and an online crowd (n = 610) were asked to evaluate the level of severity of earthquake damage. The two outcome datasets were then compared with the evaluation made by two of the present authors, who are considered experts in the field. Interestingly, the evaluations of both the semiskilled cohort and the crowd were found to be fairly similar to those of the experts, thus suggesting that they can provide an interpretation close enough to an expert’s opinion on the severity level of earthquake damage. Such an understanding may indicate that although our analysis is preliminary and requires more case studies for this to be verified, there is vast potential encapsulated in crowd-sourced opinion on simple earthquake-related damage, especially if a large amount of data is to be handled. Full article

Disinformation and fake news are used by multiple actors to manipulate and influence the public with the purpose of gaining a series of advantages. This paper describes a promising solution to the increased spread of disinformation on the Internet. Our approach leverages blockchain technology combined with both crowd intelligence and federated artificial intelligence to develop efficient capabilities that address the disinformation phenomenon. The blockchain-based architecture of the platform creates a decentralized ecosystem that ensures transparency and trust, enabling the users to make correctly informed decisions in the face of disinformation. The key differentiating factor of the platform is the incorporation of both crowd and artificial intelligence in a system that can identify and respond to disinformation quickly and efficiently. The presented architecture can be used to build reactive and proactive platforms to effectively challenge disinformation. Full article

The present systematic review updates the evidence on wisdom teeth contributing to lower incisor crowding following orthodontic treatment. Relevant literature was searched on online databases, namely Pubmed, Scopus, and Web of Science, up to December 2022. Eligibility criteria were formulated using the PICOS approach and PRISMA guidelines. Eligible research included original clinical studies involving patients previously being treated orthodontically with permanent dentition at the end of treatment, regardless of sex or age. The initial search yielded 605 citations. After considering eligibility criteria and removing duplicates, only 10 articles met the criteria for inclusion. The risk of bias of eligible studies was evaluated using the Cochrane Handbook for Systematic Reviews and Interventions tool. The majority were highly biased, mainly regarding allocation concealment, group similarity, and assessment blinding. The vast majority did not report statistically significant associations between the presence of third molars and crowding relapse. However, a minor effect has been suggested. Seemingly, there is no clear connection between mandibular third molars and incisor crowding after orthodontic treatment. The present review did not find adequate evidence to advocate preventative removal of the third molars for reasons of occlusal stability. Full article

Environmental problems are often highly complex and demand a great amount of knowledge of the people tasked to solve them. Therefore, a dynamic polit-economic institutional framework is necessary in which people can adapt and learn from changing environmental and social circumstances and in light of their own performance. The environmentalist literature refers to this knowledge producing and self-correcting capacity as ecological reflexivity. Large parts of the literature agree that deliberative democracy is the right institutional arrangement to achieve ecological reflexivity. Our paper sheds doubt on this consensus. While we agree with the critique of centralized, technocratic planning within the literature on deliberative democracy and agree that ecologically reflexive institutions must take advantage of the environmental ‘wisdom of the crowd’, we doubt that deliberative democracy is the right institutional arrangement to achieve this. Ecological deliberation fails to address its own epistemic shortcomings in using crowd wisdom: Rational ignorance, rational irrationality and radical ignorance weaken the performance of deliberative institutions as an alternative and reflexive form of ecological governance. Instead, we propose an institutional order based on market-based approaches as the best alternative for using the environmental wisdom of the crowd. Full article

Improvements in communication and networking technologies have transformed people’s lives and organizations’ activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises’ functional and communication processes. People use numerous platforms to broaden their social contacts, select items, execute duties, and learn new things. Context: Crowdsourcing is an internet-enabled problem-solving strategy that utilizes human–computer interaction to leverage the expertise of people to achieve business goals. In crowdsourcing approaches, three main entities work in collaboration to solve various problems. These entities are requestors (job providers), platforms, and online users. Tasks are announced by requestors on crowdsourcing platforms, and online users, after passing initial screening, are allowed to work on these tasks. Crowds participate to achieve various rewards. Motivation: Crowdsourcing is gaining importance as an alternate outsourcing approach in the software engineering industry. Crowdsourcing application development involves complicated tasks that vary considerably from the micro-tasks available on platforms such as Amazon Mechanical Turk. To obtain the tangible opportunities of crowdsourcing in the realm of software development, corporations should first grasp how this technique works, what problems occur, and what factors might influence community involvement and co-creation. Online communities have become more popular recently with the rise in crowdsourcing platforms. These communities concentrate on specific problems and help people with solving and managing these problems. Objectives: We set three main goals to research crowd interaction: (1) find the appropriate characteristics of social crowd utilized for effective software crowdsourcing, (2) highlight the motivation of a crowd for virtual tasks, and (3) evaluate primary participation reasons by assessing various crowds using Fuzzy AHP and TOPSIS method. Conclusion: We developed a decision support system to examine the appropriate reasons of crowd participation in crowdsourcing. Rewards and employments were evaluated as the primary motives of crowds for accomplishing tasks on crowdsourcing platforms, knowledge sharing was evaluated as the third reason, ranking was the fourth, competency was the fifth, socialization was sixth, and source of inspiration was the seventh. Full article

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports. Full article

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations. Full article