## 6 Topic Modeling Analysis  
To complement our manual analysis of the surveyed papers, our SLR leverages an advanced, yet now well-recognized, text mining technique, that is, topic modeling. Topic modeling allows for an automatic, hence, more objective, in-depth analysis of the content of the reviewed papers to extract the main topics discussed in the text and provide a number of insightful numerical and visual advanced descriptive analytics about the corpus [66]. More specially, this study utilizes the Latent Dirichlet Allocation (LDA) method. LDA was introduced by [67] and has evolved significantly since then. It is currently considered as one of the most mature topic modeling approaches in computer science literature. LDA is a probabilistic method that relies on an unsupervised algorithm to identify and depict the prevailing themes in a corpus. It considers that documents are composed of different topics and \"each topic is a probability distribution over words\" [68]. We initiate our advanced analyses by presenting a word-cloud of the corpus (Figure 10). Then, we use a battery of metrics and the insights of subject matter experts to decide on the optimal number of topics for our LDA analysis. Specifically, we rely on the following four statistics: Arun2010, CaoJuan2009, Deveaud2014, and Griffiths2004. Arun2010 measures the symmetric Kullback-Leibler divergence between the distribution of words in topics and the distribution of topics in the corpus, whereas CaoJuan2009 calculates the pairwise similarity between the topics and aggregates them. Both scores should be minimized for an optimal number of topics. Arun2010 ensures that topics are neither too broad nor overly fragmented and CaoJuan2009 minimizes topic similarity to avoid the selection of redundant topics. In contrast, the Deveaud2014 and Griffiths2004 metrics need to be maximized for an optimal number of topics. Deveaud2014 is a coherence score that appraises the co-occurrence of top words in a topic within the whole corpus. The objective is to select more coherent, semantically consistent, and, hence, meaningful and interpretable topics. Griffiths2004 is a log-likelihood model-fitting statistic that ensures an adequate fit between the LDA model and the corpus. As depicted in Figure 11, collectively, the four scores suggest an optimal number of topics between a minimum of 8 and a maximum of 15. Therefore, we conduct LDA analyses for each of these numbers and for various values of the lambda statistics, a weighting parameter that takes values between 0 and 1 and that balances words' relevance and topics' interpretability [69]. Next, we ask two subject matter experts to analyze the LDA outputs, that is, the selected topics and the top 10 words associated to each topic, for all our fitting scenarios. After a few rounds of discussions, the two experts concurred that nine is the optimal number of topics that provides the best human interpretability, which is consistent with prior topic modeling studies in computer science. Figure 12 represents the relative importance of each topic in our corpus. Figure 10: A word-cloud of the corpus. Additionally, Figures A1-A9 in Appendix B provide the detailed results of our LDA analysis when the number of topics is fixed at nine, including the importance of each theme, its most relevant terms, and its proximity to the other topics. Additionally, similar to the optimal number of topics, theme labels were determined through the consensus of two subject matter experts. Furthermore, Table 1 shows the top 10 terms for each topic. Finally, Figure 13 depicts the evolution of each topic's relative importance over the study period. The heat map graph indicates that, proportionally, the relative importance of the topics, features, depression, stress, and social media has grown significantly in recent years.