# Bibliometric Analysis of Social Robotics Research: Identifying Research Trends and Knowledgebase
## 2 Materials and Methods  
A summary of the data collection and treatment is shown in Figure 1. Data were collected from the Web of Science, a widely-used database of bibliographic information. The Web of Science incorporates the Science Citation Index Expanded, the Social Sciences Citation Index and the Arts & Humanities Citation Index, covering more than 18,000 journals and conference proceedings. We searched for social robotics articles by querying \"robot\" and soci\" in the Web of Science search engine. The asterisk serves as a wild card, allowing one to extract articles having words starting with \"robot\" or \"soci\" (e.g., robots, robotics, social, society) in either the title, abstract or keywords. A total of 7129 articles was found. However, such an approach is prone to retrieve articles containing the query that may not be directly related to the field. To filter out the unrelated articles, we used the properties of the citation networks. We focused on articles that included at least one reference to another article in the retrieved dataset and neglected the rest. Ultimately, 3334 (46.8%) articles matched this condition.  
To study the position of social robotics in relation to the greater field of robotics, we also retrieved articles with the query \"robot\", obtaining 200,139 articles out of which 142,587 (71.2%) were connected by references. Data were retrieved on 1 July 2017.  
As mentioned above, the list of references was used to create a citation network of articles. A direct linkage is established between two articles if one of them mentions the other in its references. This is known as an intercitation or direct citation. Compared to other linkage methodologies like co-citation [17] and bibliographic coupling [18], direct citation networks have been found to bring  
Figure 1: Data collection and methodology described in this article. (**a**) Data retrieval; (**b**) A citation network is created based on the references of the articles; (**c**) The largest connected component is extracted; (**d**) Clusters are obtained from the network.  
out the most accurate representation of knowledge taxonomies [19] and be better at identifying research fronts [20]. We extracted the largest connected component, which has the most tightly connected structures of knowledge related to our research target. Within the largest component, we identify clusters of papers densely connected by applying a topological algorithm based on modularity maximization. Modularity is a measure of the strength of connections within partitions or clusters. A high modularity value implies that intra-cluster connections are dense, whereas inter-cluster connections are sparse. It is defined as follows [21]:  
\[Q=\sum_{s=1}^{N}\left[\frac{l_{s}}{l}-\left(\frac{d_{s}}{2l}\right)^{2}\right] \tag{1}\]  
where \(N\) is the number of clusters, \(l_{s}\) and \(d_{s}\) are the number of links and the sum of the degrees of nodes within cluster \(s\), respectively. The algorithm iterates until the maximum modularity is reached, thus obtaining the number of clusters automatically. The network is then visualized by applying a spring layout suitable for large networks, which places similar clusters close to each other [22]. To facilitate the interpretation of the network, we visualized only the internal edges of each cluster and then assigned them different colors.  
Once the clusters are obtained, we revised the articles within them and labeled each cluster based on the contents of their most cited academic articles.