Using Text Mining to Examine the Role of Topics of Election Promises in Predicting Election Results - Jin, Xin (The 6th International Conference on Computational Social Science, 2020)
As one of the most popular topics within political communication research, elections have been paid a lot of attention to, especially the predictors of the election result. Previous literature has proposed and adequately verified the effect of demographics (e.g. age, gender, and education), party effect (e.g. liberal and conservative), personal image (e.g. clothing and smile), political party funding, media strategy, and some other factors of candidates through the process of election. While, as an important tool for voters to evaluate candidates, form prospective beliefs about them, and make a voting decision (Born et al., 2018), the election promise made by candidates through their campaigns and discourses has received less scholarly attention. Generally, voters have their own policy preferences and candidates who can implement the preference in their election promises will maximize the chances of winning the election (Jasim Alsamydai et al., 2013). Meanwhile, online political information provider like voter guidance website has improved the openness and transparency in organizing and carrying out elections, which can improve the engagement of the public (Xenos and Moy, 2007) and make the relationship between the election promise and result more observable. And the online political information is the data source of this study. To identify different policy focuses of candidates, one feasible way is to extract topics from the election promises of each candidate and quantify them. Compared with traditional quantitative methods like content analysis, computational methods such as text mining could save both manpower and time and avoid coder bias to a certain degree. In this study, the data of candidates of the 2018 local councilor election in Taiwan were used to answer the following questions:1. How can researchers extract and quantify topics computationally from election promises? 2. Do policy preferences exist in the local councilor election in Taiwan? If it exists, which topic of election promises could predict a successful election result?