We will now define and present our different networks with their preliminary analysis. We are focusing in this sections on the different ways we can create links of social networks directly derived from the analysis of the video data. Most of the following networks use the people as the same set of nodes, but with different families of ties.
Network of people overlapping on screen
Our first network connects two people when two face-tracks overlap in time. This means that we create a link between two people when they have been detected simultaneously on screen. These links are enriched with the screen duration of the overlapping of tracks as weights.
This network presents 35 nodes and 44 edges, with a main connected component of 29/41 (Fig. 6, left). This connected component is only composed of politicians, with one business person (M. Shirakawa, connected to Y. Hatoyama). It is worth noting that J. Koizumi, the top individual among all other metrics, only presents here a degree of 2. Four nodes stand out in terms of betweenness centrality (S. Abe:0.16, I. Ozawa:0.14, Y. Hatoyama:0.18, and Y. Noda:0.15, with the rest of the dataset below 0.09), and 2 nodes in terms of degree (Y. Hatoyama:10 and Y. Noda:7), however no clear convincing cut of communities is shown by Louvain’s algorithm (Blondel et al. 2008).
A few links stand out in terms of screen duration (over 1000), connecting: Y. Noda and S. Tanigaki, in 2012, I. Ozawa and N. Kan in 2003, 2006, and 2010, Y. Hatoyama and I. Ozawa in 2006, 2010, and 2012, Y. Hatoyama and B. Obama in 2009, J. Koizumi and Kim Jong Il 2002, V. Putin and S. Abe in 2012, T. Aso and Y. Fukuda in 2009. When looking at the number of days in which two different people appear together, we can notice stronger links between: S. Tanigaki and T. Aso in 2006, J. Koizumi and S. Abe in 2002, and H. Clinton and B. Obama in 2008.
Network of people appearing in a same shot
This second family of ties defines links between people appearing in a same shot (i.e. an uncut segment of video). This network roughly extends the previous network, with the difference that people do not need to appear on screen together. Because shot duration greatly varies depending on the cut of the video, we cannot use it as a meaningful metric to weigh edges, instead, we will consider the number of different days that include these shots.
The network (Fig. 6, center) presents 49 nodes for 75 edges with a main component of 41/71. The maximum k-core (k=3) (Seidman 1983) presents a very intricate subnetwork of 18 nodes (Fig. 6, right). It includes the PM, and the main anchorman (S. Takeda), later referred as the ‘main actors’. All the other nodes are politicians, including I. Ozawa. Getting their full list and description may go beyond the scope of this paper, but it is interesting to notice that N. Yamaguchi stands out as the only politician not directly connected to any of the PM. The main component presents a wider range of types of people, including 3 athletes, 3 business people, and O. Bin Laden. A Louvain segmentation does not present a clear cut of denser subgroups in this network. If we remove the ‘main actors’, we can interestingly observe two communities of politicians (the colored areas in Fig. 6, center), one centered on M. Fukushima and N. Yamaguchi, and the other one on K. Okada. However one should carefully interpret the meaning of these links given the low amount of common shots (at most three).
Three edges stand out with links displaying between 5 and 8 days of connections, T. Aso and S. Tanigaki, N. Kan and Y. Hatoyama, J. Koizumi and Kim Jong Il. If we consider links connecting two people over one day only as ‘casual’ and discard them, we can reveal a network of stronger ties of people with ‘recurrent’ interactions (23/26). In this network, I. Ozawa displays the highest betweenness centrality, followed then by the different PM.
Networks of people appearing during a same news segment
The following network connects individuals when they have been detected during a same news segment, based on the topic segmentation described in Section 4. This means that two people are connected when they took part of a same media event. The graph connects 107 people over 507 links with a main connected component of 96/499 (Fig. 7, left). This graph presents characteristics closer to complex networks with a long tail distribution of node degrees (actually fitting a lognormal distribution).
Knowing that co-detection during a news segment is the reason linking nodes, we should first remove the journalists – occurring a lot in the dataset, in order to focus on other people’s interactions. The resulting graph presents a maximal k-core (k=12) gathering 15 Japanese politicians and the 7 PM in a subgraph \(G^{\prime }_{k=12}\) with a density \(D_{G'_{k=12}}=0.79\) (Fig. 7, right).
A degree and centrality analysis will bring focus to the same people identified in the previous networks. To go beyond, we will look at the graph without the ‘main actors’, leaving 67 nodes for 221 edges. This graph clearly presents community structures, and by running a Louvain algorithm, we obtain a very interesting clustering result. The two main partitions (in light greenand orange in Fig. 8) clearly present international politicians and national politicians (respectively). We are now able to spot the non-PM Japanese politicians who played an active role in international matters by highlighting them (circled in purple in the Fig. 8, mostly at the right frontier of the orange community). We do so by counting the number of their ties with international representatives and threshold them based on their cumulative probability distribution (Herman et al. 2000). As a result, we find Y. Edano, S. Tanigaki, S. Maehara, M. Fukushima, Y. Sengoku, I. Ozawa, T. Kanzaki, M. Khomura. With the same process on the other side, we can identify (circled in red) Yu Jiang, Jiabao Wen, and Lee Myung-bak as having redundant apparition on topics with national politicians. The case of Lee Myung-Bak seems to have particularly raised a great interest among national politicians, totalizing 5 connections.
Time slicing the common segment network
The topic network has a dynamic multiplex characteristic – although we do not fully exploit this characteristic in this paper. A link is established between two people when matching in different points in time, which means we have virtually an individual link for each matching at different moment in time. Looking at the network in different timeframes will result in different arrangements of the links between nodes and different weights if these multiple links were to be collapsed in one single interaction. Thus, thanks to well defined periods of time corresponding to PM cabinets, we can use topic segmentation as a support to observe not the overall network but each slice involving the people’s interactions over the different cabinets (Fig. 9).
Before we tackle the political analysis in Section 4, we can quickly compare the political landscapes of each cabinet. To do so, we pick out the top 3 Japanese politicians in ranking of centrality and number of news segments, who are neither a PM nor have been detected during the preceding cabinets. In total we have collected 21 prominent politicians, which will be used to compare cabinets one to another. This creates a vector of all politicians per cabinet.
Based on these vectors of 21 (+ 7 PM) politicians, we can finally estimate a rough (Jaccard) proximity between cabinets as shown in Fig. 10. The periods from Abe 1 to Noda known for the series of resigning PM, shows the highest proximity one to another, and interestingly to Koizumi 1. However, Koizumi’s two following cabinets appear very different, suggesting that he set a very different media/politics scene during this time.
Preliminary observations
Before we solely focus on a political analysis of the news landscape, the exploration of these networks led us to some understanding of the media/politics scene presented by NHK News 7. Based on this data, together with the knowledge of people, we can confirm that the different PM stand out like no one else in the NHK news. They can be directly identified in all aspects of the data: first, purely quantitatively speaking, they occupy most of the media scene during their own cabinets; then, in the different networks, they also occupy a very central place; the different time-related analysis makes it especially obvious during their cabinets.
We also learn by looking at individual PM: most of them show some level of activity before their mandate and we can observe two opposite cases. On one side, Abe is actually more central than Koizumi himself during Koizumi 3 (Fig. 9(d)). On the other side, Noda came ‘out of nowhere’ before becoming PM (Fig. 4). Additionally, despite of Hatoyama and Aso appearing quite strong nodes in the different networks, they have never been detected on screen together (Fig. 6) even if they were heading two consecutive cabinets in period of time where the media/politics scene of consecutive cabinets is very similar – maybe because they are the leaders of two opposite parties.
Strikingly, one very particular politician comes out all along this study, I. Ozawa, who is (in)famously known as the “Shadow Shogun”. Getting into the details of Ozawa’s role in the Japanese politics is a fascinating work on its own (Meyer 2014), but put in short, after being leader of the opposition, he is known for all the connections and roles he has played behind the scene, building alliances and often changing side – although never he became PM.
Another very interesting point which is worth noting concerns the Imperial family. The Japanese Constitution forbids the Imperial family to take any part in politics, and observing the links surrounding the members of the family are of high interest to survey their actions. Our system finds very little connections (purple nodes in Fig. 9(c), (d), and (h)): they mostly concern the revision of the Imperial Household Law because of the issue concerning the succession to the Imperial Throne.