of internalization is not enough, that is, it has not been fully recognized by the actor. The drawback to explicit incentives is that they may cause the user to be subject to the overjustification effect, eventually believing the only reason for participating is for the explicit incentive. This reduces the influence of the other form of social or altruistic motivation, making it increasingly costly for the content host to retain long-term contributors. Ranking and assessment The distribution of UGC across the Web provides a high volume data source that is accessible for analysis, and offers utility in enhancing the experiences of end users. Social science research can benefit from having access to the opinions of a population of users, and use this data to make inferences about their traits. Applications in information technology seek to mine end user data to support and improve machine-based processes, such as information retrieval and recommendation. However, processing the high volumes of data offered by UGC necessitate the ability to automatically sort and filter these data points according to their value. Determining the value of user contributions for assessment and ranking can be difficult due to the variation in the quality and structure of this data. The quality and structure of the data provided by UGC is application-dependent, and can include items such as tags, reviews, or comments that may or may not be accompanied by useful metadata. Additionally, the value of this data depends on the specific task for which it will be utilized and the available features of the application domain. Value can ultimately be defined and assessed according to whether the application will provide service to a crowd of humans, a single end user, or a platform designer. The variation of data and specificity of value has resulted in various approaches and methods for assessing and ranking UGC. The performance of each method essentially depends on the features and metrics that are available for analysis. Consequently, it is critical to have an understanding of the task objective and its relation to how the data is collected, structured, and represented in order to choose the most appropriate approach to utilizing it. The methods of assessment and ranking can be categorized into two classes: human-centered and machine-centered. Methods emphasizing human-centered utility consider the ranking and assessment problem in terms of the users and their interactions with the system, whereas the machine-centered method considers the