# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 4 Comparison between Reliable Author Profiles and Mis-Configured Author Profiles
### The Differences between Reliable and Mis-Configured Author Profiles  
We analyze features of reliable author profiles and mis-configured ones to demonstrate their differences. It is noteworthy that we only discuss the differences in the overall distribution without talking about each individual. For each feature, we show the distributions of reliable author profiles and mis-configured ones (Figure 1).  
We can find that the mis-configured author profiles mostly have much larger numbers of papers in their paper lists compared with the reliable ones. Furthermore, the total numbers of citations are also larger. For half of the reliable author profiles, the standard deviation of citation numbers are smaller than 4, while the value is larger than 21 for mis-configured author profiles. From the cross-entropy between topic distributions of different paper entries and the value of maximum silhouette score, we find that the research topics on  
Figure 1: Comparison between reliable author profiles and mis-configured author profiles on different features. **(a–I)** show the comparison on features in Table 1 (except Verified, which is a two-value feature), respectively.  
the mis-configured author profiles are relatively richer and more diverse. Furthermore, the largest time intervals between two papers are longer. Moreover, the h-index and i10-index of the mis-configured author profiles are relatively higher. The h-index values of 83% of the reliable author profiles are lower than 10, while only 44% of the mis-configured author profiles have h-index less than 10. The median value of other i10-index metric is only 1 for the reliable author profiles. However, the median value for the mis-configured ones is 21. Since these two metrics are widely used to evaluate a scholar, this phenomenon can partly explain the possible purpose for some intentional mis-configuration.  
We conduct a two-sided \(t\)-test [19] on these two kinds of author profiles. The \(p\)-value is shown in the third column of Table 1. The values of all these features except the number of claimed interested research fields are less than 0.05, which means that the difference between two kinds of author profiles is significant on these features. As for the research fields, most of the scholars list less than five tags, so this feature has relatively smaller differences between reliable and mis-configured profiles. Scholars with reliable profiles may be more rigorous to maintain it. Therefore, the number of listed research fields may be more focus on three to five. However, more mis-configured author profiles have less than three research fields.  
Owing to these differences, these features can in turn assist the mis-configuration detection. To evaluate their effects quantitatively, we calculate the information gain (IG) [20] for further demonstration. The result is shown in the last column of Table 1. The larger the IG value is, the more important the feature is for classification. According to IG values, we find that the standard deviation of citations (i.e., citation-std) and the features of research topics between two single paper entries (i.e., maxCrossEntropy, meanCrossEntropy, crossEntropyStd) would make relatively less contributions to the classification. So we use the other nine features to assist the detection of mis-configured author profiles.