## 2 Background
### Crowd Selection in Crowdsourcing
#### 2.1.4 Social-Network-Based Crowd Selection  
One of the means for finding suitable workers to accomplish a variety of tasks is harnessing the enormous untapped crowd available on social network sites. Indeed, our work involves this type of crowd selection since it selects a suitable subset of a crowd from the social network site Twitter to contribute to crowdsourced tasks. [PERSON] et al. [42] have proposed an intelligent system to select a crowd from Twitter to participate in a crowdsourced task. First, they identified features which indicate a crowd's willingness to participate in a task based on their posts and social interactions. Second, they developed and trained a statistical model to predict willingness to participate using the previously identified features. Finally, a recommendation algorithm was built to select a subset of the crowd based on the predictions produced by the statistical model. Moreover, [PERSON] et al. [43] have used Twitter as a source of the crowd. They have introduced a framework which selects a crowd with the required expertise matching a crowdsourced task domain of knowledge. They trained a naive Bayes model using a Yahoo Answer dataset and then transferred this model to develop a new one used for inferring expertise of Twitter users. [PERSON] et al. [44] have designed a task routing approach in social networks called CrowdSTAR which finds an available crowd with the required expertise in a topic to contribute to a crowdsourced task. As part of their approach, an expertise detection component was built where they select crowd experts in a topic based on the textual content on social networks such as tweets on Twitter and posts on Quora. The authors of the documents (e.g., tweets and posts) are matched to a specific topic and their social availability is captured by measuring their responsive rate. Therefore, these knowledge and social features are utilized for routing tasks to the best-fitting crowd of experts. In addition, [PERSON] et al. [45] proposed a classification-based recommendation approach to select and recommend an appropriate subset of the crowd to a requester. Their approach used crowd workers' information by extracting their social media data, specifically from Facebook, which included their profile information and behavior such as likesand comments. In addition, they leveraged history data about previously accomplished tasks to construct crowd workers' profiles. They have developed a classification model and a ranking algorithm to select and rank crowd workers based on their observed weight. Another study conducted by [PERSON] et al. (2018) presented an approach to pick the right crowd by utilizing social network activities on Facebook called pick-a-crowd. The interests and skills of the crowd are deduced from this social information, and their profiles are constructed and matched with suitable tasks. The matching is conducted by measuring similarity between entities from crowd workers' profiles and task descriptions using linked open data (LOD). Furthermore, in ([PERSON] et al., 2018) the authors state that a diverse crowd can increase experts' performance in a task. Hence, they proposed an algorithm to select a crowd who are diverse in their opinions. They utilized social networks to detect crowd workers' interests and opinions, and a crowd worker is associated with a probability which represents these interests and opinions. The probability is obtained by analyzing the information from social network activities. [PERSON] et al. (2018) proposed a framework to identify a crowd which possess the required knowledge and willingness to participate in a task under a budget constraint. They modeled the crowd's knowledge and willingness using certain attributes derived from their social network activities on Flickr. The number of likes on an image reflects the quality of the images, and tags on images (e.g., food, art) reflect the domain of expertise. Therefore, these indicate a Flickr user has the required skill and expertise in photography.