Kamoen Naomi, Liebrecht Christine
Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands.
Front Artif Intell. 2022 May 12;5:835505. doi: 10.3389/frai.2022.835505. eCollection 2022.
In election times, millions of voters consult Voting Advice Applications (VAAs) to learn more about political parties and their standpoints. While VAAs have been shown to enhance political knowledge and increase electoral turnout, research also demonstrates that voters frequently experience comprehension problems when responding to the political attitude statements in a VAA. We describe two studies in which we test a new type of VAA, called Conversational Agent VAA (CAVAA), in which users can easily access relevant information about the political issues in the VAA statements by asking questions to a chatbot. Study 1 reports about an online experiment ( = 229) with a 2 (Type: traditional VAA/CAVAA) x 2 (Political sophistication: low/high) design. Results show that CAVAA users report higher perceived political knowledge scores and also answer more factual knowledge questions correctly than users of a regular VAA. Also, participants' CAVAA experience was evaluated better. In Study 2 ( = 180), we compared three CAVAA designs (a structured design with buttons, a non-structured design with an open text field, and a semi-structured design with both buttons and an open text field), again for higher and lower politically sophisticated users. While the three designs score equally high on factual and perceived knowledge indicators, the experience of the structured CAVAA was evaluated more positively than the non-structured version. To explore the possible cause for these results, we conducted an additional qualitative content analysis on 90 chatbot-conversations (30 per chatbot version). This analysis shows that users more frequently access additional information in a structured design than in a non-structured design, whereas the number of break-offs is the same. This suggests that the structured design delivers the best experience, because it provides the best trigger to ask questions to the chatbot.
在选举期间，数百万选民会参考投票建议应用程序（VAA）来更多地了解各政党及其立场。虽然研究表明VAA能增强政治知识并提高投票率，但也有研究显示，选民在回答VAA中的政治态度陈述时经常会遇到理解问题。我们描述了两项研究，在这两项研究中，我们测试了一种新型的VAA，即对话代理VAA（CAVAA），在这种VAA中，用户可以通过向聊天机器人提问轻松获取与VAA陈述中的政治问题相关的信息。研究1报告了一项在线实验（n = 229），采用2（类型：传统VAA/CAVAA）×2（政治成熟度：低/高）设计。结果显示，CAVAA用户报告的感知政治知识得分更高，并且比普通VAA用户正确回答的事实性知识问题也更多。此外，参与者对CAVAA体验的评价也更好。在研究2（n = 180）中，我们再次针对政治成熟度较高和较低的用户比较了三种CAVAA设计（一种带按钮的结构化设计、一种带开放文本框的非结构化设计以及一种既有按钮又有开放文本框的半结构化设计）。虽然这三种设计在事实性和感知知识指标上得分同样高，但结构化CAVAA的体验比非结构化版本得到了更积极的评价。为了探究这些结果的可能原因，我们对90次聊天机器人对话（每个聊天机器人版本30次）进行了额外的定性内容分析。该分析表明，与非结构化设计相比，用户在结构化设计中更频繁地获取额外信息，而中断次数相同。这表明结构化设计提供了最佳体验，因为它为向聊天机器人提问提供了最佳触发条件。
