# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 7 Application Case Study
### Case Study 2: University Ranking  
The metrics on the Google Scholar author profiles, such as h-index and the total number of citations, are widely used for evaluating scholars. In this part, we try to evaluate a university based on the scholars working for it. We design the rank criteria and compare the ranking list before and after excluding our detected mis-configured author profiles. The difference between the ranking results reflects the negative impact of mis-configuration on the data analysis, which demonstrates the importance and effectiveness of our detection. In our dataset, 91.4% of the scholars verified their profiles using institutional email addresses. We utilize the data from Google Scholar to generate university ranking lists and compare these lists with two well-accepted ranking lists, the QS World University Rankings by Subject (Computer Science & Information Systems) [38] and the Times Higher Education World University Ranking table for computer science [39], to evaluate the quality of our ranking lists. Since the dataset is obtained in January 2019, we use the 2018 version of these two ranking lists for comparison. Three metrics are designed to rank universities in this part. The first metric is the h2-index [40] of each university. In detail, since h-index is chosen as the metric to evaluate a single scholar, we use h2-index to aggregate different scholars, which means that there are at least h2 scholars belonging to the university whose h-index values are not less than h2. The second metric is the number of scholars who are highly cited. The definition of a highly cited scholar here is that the total number of citations received by the scholar ranks top 1% among all the scholars in the dataset. The third metric is the number of scholars whose h-index ranks top 1% among all the scholars in the dataset. Since we only study computer science related fields, we do not need to consider the issue of normalization across different fields. The ranking method is relatively simple. We sort universities involved in our dataset by these three metrics, respectively, and obtain the top 20 universities before and after excluding the mis-configured author profiles. Then, we observe their rankings in the two comparative ranking lists. The ranking result may be a little bit different from the two comparative ranking lists, because our ranking methods are totally based on the publication information from Google Scholar. Although the methods used to rank the universities are different, it can be seen that the selected 20 universities are all ranked relatively high in the two comparative lists especially when we exclude the mis-configured author profiles. In order to quantitatively measure the similarity of different ranking lists, we use two metrics, square deviation value (SDV) and mean deviation value (MDV). The definition of them are as follows:  
\[SDV(A,B)=\frac{1}{n}\sum_{i=0}^{n-1}(A[i]-B[i])^{2} \tag{16}\]  
\[MDV(A,B)=\frac{1}{n}\sum_{i=0}^{n-1}|A[i]-B[i]| \tag{17}\]  
where \(A[i]=i+1\) represents the the ranking of the university based on our defined metrics. \(B[i]\) is the relative ranking of the university in the comparative ranking list. \(n\) is the length of ranking list. The smaller SDV and MDV are, the more similar two ranking lists are. The results of these two metrics are shown in Table 7. After excluding the mis-configured author profiles, the resulted top universities rank generally better. In other words, leaving out the mis-configured author profiles can improve the quality of university ranking. The results show that our work to detect mis-configured author profiles is necessary and meaningful. The ranking result based on highly cited scholars is a little bit abnormal. We believe that the reason is that the probability of highly cited scholars having mis-configured author profiles is relatively higher. They usually have a large number of papers in their paper lists, therefore, it may be hard to keep the profiles accurate.