# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
A large number of scholars have created their own author profiles on Google Scholar. A scholar's author profile contains a brief introduction, interested research fields, a paper list, a co-author list and some automatically calculated citation metrics. The author profile is an important reference when evaluating a scholar. It should be noted that the papers added to a scholar's author profile are just claimed by the scholar based on the recommendations given by Google Scholar, without further verification. Moreover, after the author profile is initially established, Google Scholar will continuously add new papers that are believed to be authored by the scholar based on Google Scholar's recommendation algorithm if the author allows. Therefore, some author profiles may turn to be mis-configured even if it used to be reliable. To make the results of the researches about academic impacts as accurate as possible, some corrections and normalization were applied to raw data from Google Scholar. Most of the previous studies about the reliability of academic data are concentrated on author name disambiguation [10; 11; 12; 13]. However, there is little research focusing on the reliability of a scholar's author profile. The mis-configured author profiles may lead to the statistical error of scholars' evaluation metrics and their academic social networks, which may further cause some studies about academic impacts to be inaccurate. The validity of researches mentioned above can be further improved once the author profiles from Google Scholar become more reliable. Therefore, the vulnerability of the information of author profiles is noteworthy, and to detect the mis-configured author profiles, which include papers written by other scholars, is necessary. In this paper, we focus on the configuration of the author profiles from Google Scholar. We define an author profile that contains papers not authored by the profile owner as a mis-configured one; otherwise it is regarded as a reliable one. We aim at checking the configuration of an author profile automatically and we analyze the data reliability of Google Scholar author profiles according to the judgment results using our proposed model. To the best of our knowledge, it is the first time that the reliability checking has been used upon an academic author profile dataset. Overall, the contributions of our work are summarized as follows:  
(1) We reveal the fact that several scholars' author profiles on Google Scholar include papers that are not authored by the scholar, and formulate the problem of detecting mis-configured author profiles on Google Scholar. (2) We propose and implement a novel neural network model to distinguish mis-configured author profiles on Google Scholar from the reliable ones, with the assistance of graph embedding, classic machine learning, and deep learning techniques. (3) We construct a dataset of author profiles from Google Scholar and perform a comprehensive data-driven evaluation by applying the model to the real-world dataset. F1-score of the best results reaches 0.9610, which demonstrates the effectiveness of the model. (4) We conduct an analysis about the mis-configuration phenomenon of Google Scholar by introducing two application case studies and comparing their results using the data before and after filtering out the mis-configured author profiles, which demonstrates the meaningfulness of our work. The paper is organized as follows. Section 2 defines the problem we will solve. Then, in Section 3 we introduce our dataset and the method to obtain the ground truth to verify the reliability of profiles. In Section 4, we compare the mis-configured author profiles with the reliable ones using some selected features. We introduce our integrated model to identify mis-configured author profiles in Section 5 and evaluate its performance in Section 6. Then, we perform two application case studies and conduct some analysis based on the results of detection in Section 7. In Section 8, we discuss some limitations of our work.