Abstract
The relationship between culture and the appeals in TV advertisements has been extensively studied. We attempted to reveal the image structure produced by TV commercials in Japan, which may show the cultural features of the country, and to evaluate its temporal change. For this purpose, we constructed and analysed a co-occurrence network of keywords related to TV commercials by using immense data that include the records of all TV commercials aired in the Kanto area in Japan including Tokyo for a period of 15 years. We found a strong heterogeneity of the co-occurrence relationship, where a few keywords, e.g., ‘woman’, ‘man’, ‘animation’, and ‘logo’, co-occur with a huge number of other keywords every year. A community on a co-occurrence network can be regarded as a set of keywords that are mutually associated with each other through TV commercials. We examined the characteristics of the communities by associating them with categories of advertised products and found a temporal change in which the relationship between the communities possessing the image of entertainment and children and the category of PC and A/V gradually increases in strength. However, there was a consistent tendency in the examined period for the product categories related to communities that include ‘man’ to be less associated with those that include ‘woman’ and vice versa, which implicates a gender role inequality underlying the various appeals in TV commercials.
Highlights
A network representation is an effective way to determine a hidden characteristic in the objects observed (Newman 2003)
By the analysis on the temporal change of community structure, we found that the community whose nodes are associated with the keyword ‘woman’ seems to have a significant relationship to that associated with ‘product’
The mean degree and the mean strength gradually increase with the year. The reason for this increase may not be attributed to the meaningful change in the characteristics of the TV commercials or of the culture, but presumably to the editorial aspects of the dataset, because the mean number of co-occurrence keywords per commercial continuously increases with the year (Table 1)
Summary
A network representation is an effective way to determine a hidden characteristic in the objects observed (Newman 2003). Keyword co-occurrence networks, where nodes denote the keywords in articles, have been constructed and analysed to investigate the knowledge structure in the academic fields (Radhakrishnan et al 2017; Su and Lee 2010). In these networks, an edge represents the co-occurrence of two keywords in the same article, and an edge occasionally has a weight that represents the frequency of their co-occurrence. A node with a high degree or strength, which is the sum of the weights of edges connected to the focal node, can be regarded as the core of the knowledge structure in the sense that it significantly appears with various topics in the field. Previous studies have investigated such cores in the knowledge structure and their neighbouring nodes to reveal the trends or important topics in the field
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