公務人員關注議題之文字探勘：以PTT公職板為例

王貿

doi:10.7014/SRMA.2020100005

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P119 - 154

社会科学 > 社会科学综合

DOI： 10.7014/SRMA

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公务人员关注议题之文字探勘：以PTT公职板为例

A Computational Text Analysis on the Core Issues for Public Servants: Evidence from the PTT

王贸(Mao Wang)

调查研究-方法与应用； 45 期 (2020 / 10 / 01) ， P119 - 154

繁体中文 DOI： 10.7014/SRMA.2020100005

公务人员；文字探勘；批踢踢实业坊；内部顾客；结构式主题模型； public servant ； text mining ； PTT ； internal customer ； structural topic model

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摘要 │ 参考文献 (31) │ 被引用文献 (7) │ 国际计量

摘要〈TOP〉

并列摘要〈TOP〉

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 (公职板)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 (行政院人事行政总处). 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.

参考文献 ( 31 ) 〈TOP〉

汪明生, Ming-Shen,陈正料, Cheng-Liaou,林锦郎, Chin-Lang(2005)。政府内部顾客参与行为之研究：以都市垃圾清运业务委外案为例。公共行政学报，15，81-129。
连结：
曹开明, Kai-Ming,刘大华, Ta-Hau(2019)。网路论述的语艺生态学初探：以网路社群讨论军中同志议题为例。传播研究与实践，9(1)，65-103。
连结：
陈世荣, Roger(2015)。社会科学研究中的文字探勘应用：以文意为基础的文件分类及其问题。人文及社会科学集刊，27(4)，683-718。
连结：
傅文成, Wen-Cheng,陶圣屏, Sheng-Ping(2018)。以大数据观点探索网路谣言的「网路模因」传播模式。中华传播学刊，33，99-135。
连结：
管琼瑛, Chiung-Ying,谢宁, Ning,陈洁, Chieh,张桂萍, Kuei-Ping,高翊玮, Yi-Wei,谢邦昌, Ben-Chang,张嘉芳, Chia-Fang,张耀懋, Yao-Mao(2017)。长期照顾政策是照顾老人还是失能者？─以蔡英文脸书为例探勘民众认知。台湾公共卫生杂志，36(55)，511-520。
连结：

被引用文献 ( 7 ) 〈TOP〉

国际计量

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