Related papers: Data Leak Aware Crowdsourcing in Social Network
This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional crowdsourcing, which relies on human labels, the framework…
Complex data in social and natural sciences find effective representation through networks, wherein quantitative and categorical information can be associated with nodes and connecting edges. The internal structure of networks can be…
Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e. requesters, workers, and platform owners) is a critical…
As freelancing work keeps on growing almost everywhere due to a sharp decrease in communication costs and to the widespread of Internet-based labour marketplaces (e.g., guru.com, feelancer.com, mturk.com, upwork.com), many researchers and…
Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accurate model, as measured by overall error on a holdout set,…
Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small…
Crowdsourcing systems often have crowd workers that perform unreliable work on the task they are assigned. In this paper, we propose the use of error-control codes and decoding algorithms to design crowdsourcing systems for reliable…
In an organization, tasks called projects that require several skills, are generally assigned to teams rather than individuals. The problem of choosing a right team for a given task with minimal communication cost is known as team formation…
Many groups with diverse convictions are interacting online. Interactions in online communities help people to engage each other and enhance understanding across groups. Online communities include multiple sub-communities whose members are…
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is not observable. This problem finds applications in…
The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on…
Computations related to learning processes within an organizational social network area require some network model preparation and specific algorithms in order to implement human behaviors in simulated environments. The proposals in this…
People participate and activate in online social networks and thus tremendous amount of network data is generated; data regarding their interactions, interests and activities. Some people search for specific questions through online social…
In this article, we develop a clique-based method for social network clustering. We introduce a new index to evaluate the quality of clustering results, and propose an efficient algorithm based on recursive bipartition to maximize an…
Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values are not mutually exclusive even for functional predicates due…
Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they…
Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often…
Misinformation propagation in online social networks has become an increasingly challenging problem. Although many studies exist to solve the problem computationally, a permanent and robust solution is yet to be discovered. In this study,…
Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the…
Many real networks that are inferred or collected from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access…