# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
### Motivation  
Despite the continuous improvement of the above studies, there are some parts to be supplemented. First of all, it is challenging to describe the time when the issues occurred quantitatively. The majority of prior studies that took dynamic patterns of social issues into account classified a group of similar documents as an issue. If a group of similar documents is defined as an issue, it is recognized as a significant issue when sufficient documents are accumulated over time. In order to illustrate the time when the social issue occurred, by quantitatively assessing the moment when society suddenly changed, it is required to use the concept that an issue is a topic or problem that suddenly captures people's attention. Second, it can be hard to rank issues by urgency or importance. Prior studies either stopped at identifying various issues or presented events with a higher frequency of occurrence as important issues. When some documents were merged into a new issue, it was unclear whether the issue was created by concentration from people or by a minor social event. With this method, multiple issues must be continuously reviewed, making it hard to detect urgent issues in a timely manner. In addition, according to the concept of an issue mentioned above, issues that are frequently encountered or have a lot of relevant documents are not always the highest priorities. As a result, it is necessary to rank the issues that people suddenly became focused on at a particular time while taking the definition of an issue into consideration.  
In light of the above, this study proposes an approach for detecting social issues from web-based data using network analysis, considering the definition and characteristics of issues. The first step of this approach is constructing co-occurrence networks of keywords extracted from web-based data by time period. As mentioned earlier, an issue is a specific topic or problem that is formed from a variety of stories. A point to note here is that a story includes several topics, and each topic is composed of various combinations of keywords [12]. In other words, various keywords gather to form a topic, various topics gather to form a story, and issues are created from the stories. Therefore, in this approach, the keyword co-occurrence networks are constructed to consider combinations of various keywords forming topics and stories. At this time, since the issue has organic evolution patterns over time, a keyword co-occurrence network is constructed for each time period. Second, in order to identify the time period in which social issues occurred, the network structure is quantified for each time period and the structural change is monitored. In a keyword co-occurrence network, the influence of keywords and the relationship between keywords can be quantitatively presented, such as a centrality index, based on the co-occurrence frequency of the keywords [13]. After calculating a network structure entropy using the centrality indices of the keywords constituting the networks [14], the time period in which the entropy value changes rapidly is identified. Third, a community detection algorithm that detects a group more densely clustered than other groups within a network is applied to extract social issue candidates from the network in the identified time period. And finally, the most urgent social issues are detected by deriving the priority of the extracted social issue candidates using the centrality index of the keywords utilized in the process of calculating the network structure entropy. Through the amount of change in the keyword centrality index at the time period when the network structure entropy drastically changed, it is possible to detect which topic the members of society suddenly focused on.