Zörgő Szilvia, Peters Gjalt-Jorn, Jeney Anna, Kovács Szilárd Dávid, Crutzen Rik
Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, Maastricht, 6200 MD, The Netherlands, 31 308622466.
Faculty of Psychology, Open University of the Netherlands, Heerlen, The Netherlands.
J Med Internet Res. 2025 Jul 14;27:e64901. doi: 10.2196/64901.
The recent increase in online health information-seeking has prompted extensive user appraisal of encountered content. Information consumption depends crucially on the quality of encountered information and the user's ability to evaluate it; yet, within the context of web-based, organic search behavior, few studies take into account both these aspects simultaneously.
We aimed to explore a method to bridge these two aspects and grant even consideration to both the stimulus (web page content) and the user (ability to appraise encountered content). We examined novices and experts in information retrieval and appraisal to demonstrate a novel approach to studying information foraging theory: stimulus-engagement alignment (SEA).
We sampled from experts and novices in information retrieval and assessment, asking participants to conduct a 10-minute search task with a specific information goal. We used an observational and a retrospective think-aloud protocol to collect data within the framework of an interview. Data from 3 streams (think-aloud, human-computer interaction, and screen content) were manually coded in the Reproducible Open Coding Kit standard and subsequently aligned and represented in a tabularized format with the R package {rock}. SEA scores were derived from designated code co-occurrences in specific segments of data within the stimulus data stream versus the think-aloud and human-computer interaction data streams.
SEA scores represented a meaningful comparison of what participants encountered and what they engaged with. Operationalizing codes as either "present" or "absent" in a particular data stream allowed us to inspect not only which credibility cues participants engaged with with the most frequency, but also whether participants noticed the absence of cues. Code co-occurrence frequencies could thus indicate case-, time-, and context-sensitive information appraisal that also takes into account the quality of information encountered.
Using SEA allowed us to retain epistemic access to idiosyncratic manifestations of both stimuli and engagement. In addition, by using the same coding scheme and designated co-occurrences across participants, we were able to pinpoint trends within our sample and subsamples. We believe our approach offers a powerful analysis encompassing the breadth and depth of data, both on par with each other in the feat of understanding organic, web-based search behavior.
近期在线健康信息搜索量的增加促使用户对所遇到的内容进行广泛评估。信息消费在很大程度上取决于所遇到信息的质量以及用户对其进行评估的能力；然而，在基于网络的自然搜索行为背景下，很少有研究同时考虑这两个方面。
我们旨在探索一种方法来弥合这两个方面，并同等地考虑刺激因素（网页内容）和用户因素（评估所遇到内容的能力）。我们研究了信息检索和评估方面的新手和专家，以展示一种研究信息觅食理论的新方法：刺激 - 参与度对齐（SEA）。
我们从信息检索和评估方面的专家和新手样本中选取参与者，要求他们在有特定信息目标的情况下进行一项10分钟的搜索任务。我们使用观察法和回顾性出声思考协议在访谈框架内收集数据。来自3个数据流（出声思考、人机交互和屏幕内容）的数据按照可重复开放编码工具标准进行人工编码，随后使用R包{rock}以表格形式对齐并呈现。SEA分数源自刺激数据流与出声思考和人机交互数据流中特定数据段内指定代码的共现情况。
SEA分数代表了参与者所遇到的内容与他们所参与内容之间有意义的比较。将代码在特定数据流中操作化为“存在”或“不存在”，使我们不仅能够检查参与者最频繁参与的可信度线索，还能检查参与者是否注意到线索的缺失。代码共现频率因此可以表明对案例、时间和上下文敏感的信息评估，同时也考虑到所遇到信息的质量。
使用SEA使我们能够保留对刺激因素和参与度独特表现形式的认知访问。此外，通过对所有参与者使用相同的编码方案和指定的共现情况，我们能够确定样本和子样本中的趋势。我们相信我们的方法提供了一种强大的分析，涵盖了数据的广度和深度，在理解基于网络的自然搜索行为方面两者旗鼓相当。
