# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
## 2 Literature Review
### Social Issue Detection  
The studies detect social issues or important events by utilizing web-based data mainly proposed an event detection approach, and as mentioned in the introduction, they can be divided into three types: feature-based approach, topic-modeling-based approach, and incremental approach. A summary of the related literature using various web-based data and data analysis methods can be found in Table 2.  
First, the feature-based approach uses various features that can be extracted from documents or posts for detecting events in our society. For example, some researchers collected data from the web and used keywords extracted from the text of the data to detect events [6; 7; 25], and others have used keywords along with other information such as hashtags, timestamps, and locations of web posts [26; 27]. The second approach is the topic-modeling-based approach which utilizes probabilistic models to detect issues or events. The topic-modeling-based approach assumes that latent topics are always present in the documents being processed [11]. A document is made up of a mixture of topics, each topic having a probability distribution for the keywords included in that document. And each topic distribution is considered an event. Latent Dirichlet Allocation (LDA) is a probabilistic topic model that assumes the topic distribution has a Dirichlet prior [28]. The LDA is the most widely used topic model due to its high performance in handling large documents and interpreting the identified latent topics. Therefore, many studies proposed a topic-modeling-based approach based on LDA. The Evolutionary Context-Aware Sequential Model (ECSM), which uses an LDA-like topic layer to capture the context-aware se \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Methods** & **Purpose** & **Reference** \\ \hline Music streaming app reviews & Topic model, Text regression & Investigation of determinants for customer satisfaction in music streaming services & [17] \\ \hline Online hotel reviews & \begin{tabular}{c} K-means clustering, TOPSIS, \\ Classification and Regression Trees \\ \end{tabular} & \begin{tabular}{c} Examination of green hotel selection \\ behavior of travelers \\ \end{tabular} & [18] \\ \hline Pizza, car, smart phone tweets & Text mining, Sentiment analysis & Comparison of users’ sentiments before and after the launch of three new products & [19] \\ \hline Smart speaker reviews & \begin{tabular}{c} Event detection and tracking \\ algorithm, Opportunity algorithm, \\ Sentiment analysis \\ \end{tabular} & \begin{tabular}{c} Identification of time-evolving \\ product opportunities \\ \end{tabular} & [20] \\ \hline Smart phone reviews & \begin{tabular}{c} Opportunity algorithm, Topic \\ model, Sentiment analysis \\ \end{tabular} & Identification of product opportunities & [21] \\ \hline Web news, Patent & Topic model, Sentiment analysis & Identification of emerging technologies development trends & [23] \\ \hline Web news, Patent & \
\begin{tabular}{c} Text mining, Vector \\ autoregressive model \\ \end{tabular} &
\begin{tabular}{c} Investigation of long-run relationship and \\ coevolution patterns of technology \\ and society \\ \end{tabular} & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Various studies related to web-based social data analysis. mantic coherences, was proposed to track the topic evolution [29]. In addition, a retweeting behavior-based topic model called RL-LDA [30], which leverages retweet behavior to handle event evolution, and a topic model for microblogs called Bursty Event dEtection (BEE+) [31], which can detect bursty events from short-text and model the temporal information, were proposed. And many other topic-modeling-based approaches have also been suggested [8; 32; 33]. The feature-based and topic-modeling-based approaches require parameters to be determined, such as the number of issues and the period to be monitored before applying the approaches. The dynamic patterns of social issues make it difficult to define the parameters in advance.