YouTube Comments and Party Manifestos (Japanese), and VAA Statements (English and Japanese)
Description
These datasets were utilized in the study to build a system for supporting the design of voting advice applications. Datasets are: 1. Party manifesto data contains party manifestos uploaded before the 2021 Japanese Upper House and the 2022 House of Councillors elections from all the Japanese political parties. 2. VAA data contains statements from 2 European and 6 Japanese VAAs issued for the 2019 European Parliament elections and 2021 Japanese Upper House elections, respectively, from the following VAAs: EUandi, EUvox, Zero Senkyo, Shimotsuke Shimbun, Japan Choice, FokusJapan, Asahi Shimbun and Mainichi Shimbun. In total, there were 135 and 50 statements covering the most important political issues in Japan and the EU, respectively. 3. YouTube comments data contains comments from the 18 most popular Japanese YouTube channels related to politics and economics uploaded between January 2021 and July 2021 (3,998,590 comments from 17,773 videos). The research was conducted by Daniil Buryakov, Mate Kovacs, Victor Kryssanov, and Uwe Serdült. Abstract of the paper: The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30\% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.