# Selecting Workers Wisely for Crowdsourcing When Copiers and Domain Experts Co-exist
## 1 Introduction  
With the rapid development of mobile Internet and the popularity of intelligent terminal devices, the wide use of crowdsourcing is gradually being integrated into people's lives [1]. Crowdsourcing platforms, such as Amazon Mechanical Turk (AMT ([[https://www.mturk.com](https://www.mturk.com)]([https://www.mturk.com](https://www.mturk.com)) (accessed on 19 December 2021))), have been widely applied in many fields, including video analysis [2], knowledge discovery [3], Smart Citizen ([[http://www.smartcitizen.me](http://www.smartcitizen.me)]([http://www.smartcitizen.me](http://www.smartcitizen.me)) (accessed on 19 December 2021)), and human-robot interaction studies [4]. On the platforms, requesters publish questions to be answered. The participating public obtains material rewards or meets their interests by completing these tasks. After the answers are collected from all workers, the crowdsourcing platform comprehensively integrates the data to identify the truth. However, different workers may provide conflicting answers. The quality of their answers is also uneven. Some answers may be close to the truth, while some answers may be far from the truth. To resolve the conflicts, many methods have been proposed in the crowdsourcing area, which can be divided into two categories:  
* The methods that adopt a redundancy-based strategy [3, 5]: Majority voting chooses the answer given by the majority of workers as the estimated truth. Gold-injected methods [6] use a small number of tasks with basic facts to evaluate the credibility of workers. The expectation-maximization-based method [7] evaluates worker credibility and forecasts the truth at the same time;* The methods [8] that improve accuracy by eliminating bad workers: These methods believe that aggregating answers from a small amount of high-credibility workers may achieve better accuracy than blindly pursuing more workers. A typical method [9] is to use qualification tests to distinguish bad workers and stop assigning tasks to them. Most current methods quantify worker credibility as a single value [10; 11; 12] or a confusion matrix [7; 13]. They assume each worker has the same professional level on different tasks. However, each worker has different domain expertise. For example, a basketball fan has higher credibility in answering basketball-related tasks than volleyball-related tasks. Assigning tasks to workers who are not good at the related domain may lead to obtaining a low-quality collection of workers' answers. Therefore, it is very important to consider the credibility of workers in different domains. ARE [14] considers the domain in task assignment to select one expert to complete each task. Actually, in crowdsourcing, it often needs not a worker, but a large number of high-quality workers. MDC [15] aggregates truth by considering domains of workers in the truth discovery stage instead of the task assignment stage, which may assign tasks to low-quality workers. When considering the domain expertise of workers, initializing the accuracy of workers is also a problem that needs to be studied. Most current methods in crowdsourcing and truth discovery initialize the accuracy of workers to a fixed value, hoping that workers can complete a large number of tasks to approach their real accuracy. We propose an initialization method to make the initialization accuracy closer to the real accuracy of workers. The existing methods also make another assumption that all workers are independent of each other. However, due to the convenience of the Internet and the accessibility of information, it is easy for workers to copy, crawl, and aggregate information published by others. Therefore, copying is common among workers [16]. Answers provided by one worker, no matter whether true or false, can be copied by many other workers. In the case that wrong answers spread among workers via copying, if we do not distinguish copiers, we may identify the wrong answers as the truth.