# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 4 Illustrative Example: Diesel Exhaust Fluid Issue in South Korea
### Experimental Results  
This study utilized web news articles among web-based data to monitor social streams. Because a large volume of web news articles covering a wide range of topics, including economics, technology, and business, are generated in real time, web news data is suitable for monitoring our society over time [39]. This study collected Korean web news data through BIGKinds (www.bigkinds.or.kr (accessed on 20 July 2023)), which is a news big data analytics service run by the Korea Press Foundation. Since 1990, BIGKinds has accumulated news articles published by 54 Korean media organizations, including newspapers and broadcasting, and provides about 70 million articles. To evaluate the effectiveness of the proposed social issue detection approach in detecting the DEF issue that occurred in late 2021, this study first collected news article in the late 2021 from BIGKinds. Specifically, since social issues appear without any restrictions, such as field or region, this study collected news article data from 11 national daily newspapers, excluding regional daily newspapers and specialty newspapers. As a result, information such as publication date, media outlet, and title were collected for 129,747 news articles from 1 September 2021 to 31 November 2021. In addition, since BIGKinds only provides partial text of news articles, this study collected the full text of the articles through web scraping. To roughly identify when the social issue related to DEF occurred, this study measured the number of news articles in which the keyword, '(D.\(\Delta\)\(\pm\)\(\sim\)' (Diesel exhaust fluid, DEF), was mentioned in the text by publication date, and found that the number increased sharply from the beginning of November 2021 (Figure 3). Therefore, out of the 129,747 news articles data collected earlier, 23,925 articles from all fields of society published between 25 October and 14 November were selectively used. Next, 234,975 unique keywords were extracted from the keyword data of the selected news articles. BIGKinds provides keyword data for each news article, which is a list of noun keywords that appear in the title and body of the article using a morpheme analyzer and a Korean dictionary. BIGKinds' keyword data is suitable for keyword analysis, such as topic model analysis or network analysis [40,41]. Since there are many stopwords and meaningless keywords among the extracted keywords, pre-processing was performed with the following conditions (Table 4) to select 155,609 valid keywords for constructing keyword co-occurrence networks. \begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Examples of Stopword** \\ \hline Keywords with a length of 1 & “A”, “B”, “\(\overline{\text{A}}\)” (Finish) \\ \hline Keywords starting with a number & “\(1181\$\)” (1181 trillion), “\(282.8\%\)”, \\  & “\(632\$\)” (632 places) \\ \hline Keywords in foreign languages other than & “\(\bar{\mathcal{E}}\$\)” \(\bar{\mathcal{E}}\)”, “\(\bar{\mathcal{E}}\)”, “\(\bar{\mathcal{E}}\)”, “\(\bar{\mathcal{E}}\)”, “\(\bar{\mathcal{E}}\)”, “\(\bar{\mathcal{E}}\)xx\(\Delta\)\(\eta\)i\(\alpha\)” \\ \hline Keywords that are meaningless or have a & “\(\bar{\mathcal{  
Before constructing the keyword co-occurrence network using the selected valid keywords, time periods were set for monitoring. When the number of news articles per day was computed based on the publication date, there were significantly fewer news articles on weekends compared to weekdays, as shown in Figure 4. Therefore, given the pattern of the number of news articles per day, this study set each time period to 7 days so that one time period always includes Saturday and Sunday. As a result, a total of eight time periods were defined from 25 October 2021 to 14 November 2021, with each time period overlapping (Table 5). Next, in order to construct a keyword co-occurrence network for each defined time period, this study derived keyword co-occurrence pairs using the previously selected valid keywords.