公務人員關注議題之文字探勘：以 PTT 公職板為例
A Computational Text Analysis on the Core Issues for Public Servants: Evidence from the PTT
Background: The government used to ignore the voice of public servants from the bottom levels, the reasons being not only a lack of communication channels but also the fact that such information often exists exclusively within personal networks. However, along with the rise of online forums and social media, there are some communities, discussing all kinds of issues related to public jobs, mainly for public servants. One key element of public personnel management is to realize the problems raised by and needs of the internal customer, namely civil service, and thus this study is to investigate what issues are most discussed online by public servants.
Methods and Data: Survey is a common way to understand the voice of internal customers, but it is also accompanied by certain problems, such as a narrower survey framework decided by the survey administrator, and respondents' fear of identity disclosure to their supervisors. Using textual data from online forum posts, without the downside of the survey method, and exploiting the techniques of computational text analysis are helpful to depict the issues public servants truly care about. This article focuses on textual data from the PublicServan Board (公職板) of the PTT (like a Taiwanese version of Reddit), collects 20,616 posts which ask a question, and summarizes the content of posts through an unsupervised machine learning method.
Results: Based on the result of a structural topic model, this article examines these posts and can be clustered into 13 topics: benefits, activities, interactions, leave, examinations, procurement, reports, recruitment, reform, transfers, job rankings, appraisals, and position seeking, and compares them to the government practices we recognize, and visualizes the relationships between the topics. In this corpus, the amount of posts on each topic varies. The top four topics include: job ranking (11.15%), position seeking (10.32%), recruitment (9.61%), and interactions (9.43%), and the results reveal that qualified people tend to raise questions about how to find an "appropriate" agency and a "suitable" position. This finding is in line with the fact that the numbers of posts are higher every March and September because that is when the results of the public service exams are announced.
Apart from discerning different topics, there are some discrepancies and contradictions between the findings from these textual data and the annual survey administered by the Directorate-General of Personnel Administration, Executive Yuan (行政院人事行政總處). The high turnover rate is attributed to dissatisfaction with promotion by the authority, but the relationship between turnover and dissatisfaction with promotion rarely shows evidence from the posts we collect. Interactions with line managers and colleagues in the workplace often result in doubts and complaints which we can discover from textual data, yet no numeric data explicitly indicate this situation in the annual survey. Finally, the topic network adds more contextual information to specify topic categories and uncover their relationships.
Conclusions: Computational text analysis does not replace traditional ways of comprehending public servants, but can complement and confirm what we know about public servants. This type of analysis provides not merely higher or lower numbers but a meaningful context, and can, more importantly, help enhance interpretations. Some suggestions are made for the reference of the government personnel authority and future research agendas.