# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 3 Proposed Approach
### Defining Social Issue Candidates and Detecting Social Issues  
First, in this step, this study defines social issue candidates in the time period which was identified in the previous step as social issues occurred. At this point, the community detection algorithm is utilized for defining social issue candidates from the keyword co-occurrence network of the identified time period. A community in a network is a group of nodes that are densely connected to each other [37]. At the same time, the nodes that constitute the community are weakly connected to nodes of other communities (Figure 2). In the case of a keyword co-occurrence network, since a community is formed by gathering links between keywords that frequently appear simultaneously, the community means a topic composed of keywords related to each other. In this study, various communities were extracted by applying the community detection algorithm to the keyword co-occurrence network in the identified time period as social issues occurred. Since the extracted communities refer to topics mentioned by people in the time period in which social issues occurred, they can be defined as social issue candidates.  
Second, once the social issue candidates are defined, this study derives a priority of the social issue candidates to detect the most urgent social issues among them. The priority of the social issue candidates is derived using the centrality index of the keywords utilized in the previous step for calculating the network structure entropy. According to the equations of network structure entropy in Table 3, if the degree centrality of a specific keyword is  
Figure 2: An example of a network with community structure.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Entropy** & **Equation** \\ \hline Node & \(N_{q}^{\prime}=-\sum\limits_{i=1}^{N}\frac{(p_{i}^{q})-p_{i}}{1-q+\epsilon}\) & \(p_{i}=\frac{DegreeCentrality(Node_{i})}{\sum\limits_{i=1}^{N}DegreeCentrality( Node_{i})}\), \\  & \(q_{i}=1-(max(v(i))-v(i))\), \\  & \(v(i)=BetweennessCentrality(Node_{i})\), \\  & \(\epsilon>0\) \\ \hline Link & \(p_{j}=\frac{\frac{Weight(Link_{j})}{\sum\limits_{j=1}^{N}Weight(Link_{j})}}{ \sum\limits_{j=1}^{N}Weight(Link_{j})}\), \\  & \(q_{j}=1-(max(l(j))-l(j))\), \\  & \(l(j)=BetweennessCentrality\Big{(}Link_{j}\Big{)}\), \\  & \(\epsilon>0\) \\ \hline Network & \(S_{q}^{\prime}=N_{q}^{\prime}+L_{q}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equations of structure entropy.  
low and suddenly increases, the network structure entropy also changes rapidly under the influence of the centrality change of the keyword. Therefore, an impact score of each social issue candidate for the change in network structure entropy is computed using the degree centrality change of the keywords constituting a social issue candidate. The impact score of a social issue candidate \(c\) at time period \(t\) is defined as follows:  
\[ImpactScore_{t}(c)=\frac{\sum_{i\in K_{t}(c)}f(i)}{\sum_{i\in K_{t}(c)}g(i)} \tag{1}\]  
\[K_{t}(c)=Set\text{ of keywords constituting community }c\text{ at time period }t \tag{2}\]  
\[f(i)=\begin{cases}DC_{t}(i)-DC_{t-1}(i),&\text{ if keyword }i\text{ exists at both time periods }t\text{ and }t-1\\ 0,&\text{ otherwise}\end{cases} \tag{3}\]  
\[DC_{t}(i)=Degree\text{ centrality of keyword }i\text{ at time period }t \tag{4}\]  
\[g(i)=\begin{cases}1,&\text{ if keyword }i\text{ exists at both time periods }t\text{ and }t-1\\ 0,&\text{ otherwise}\end{cases} \tag{5}\]  
Finally, since social issue candidates with high impact scores are the topics that members of society suddenly focused on, this study detects the most urgent social issues by identifying the candidates with high impact scores.