Over the past 15 years, the volume, richness and quality of data collected from the combined social networking platforms has increased beyond all expectation, providing researchers from a variety of disciplines to use it in their research. Perhaps more impactfully, it has provided the foundation for a range of new products and services, transforming industries such as advertising and marketing, as well as bringing the challenges of sharing personal data into the public consciousness. But how to make sense of the ever-increasing volume of big social data so that we can better understand and improve the user experience in increasingly complex, data-driven digital systems. This link with usability and the user experience of data-driven system bridges into the wider field of HCI, attracting interdisciplinary researchers as we see the demand for consumer technologies, software and systems, as well as the integration of social networks into our everyday lives. The fact that the data largely posted on social networks tends to be textual, provides a further link to linguistics, psychology and psycholinguistics to better understand the relationship between human behaviours offline and online. In this thesis, we present a novel conceptual framework based on a complex digital system using collected longitudinal datasets to predict system status based on the personality traits and emotions extracted from text posted by users. The system framework was built using a dataset collected from an online scholarship system in which 2000 students had their digital behaviour and social network behaviour collected for this study. We contextualise this research project with a wider review and critical analysis of the current psycholinguistics, artificial intelligence and human-computer interaction literature, which reveals a gap of mapping and understanding digital profiling against system status.
翻译:在过去15年中,从综合社交网络平台收集的数据的数量、丰富程度和质量已超出所有预期,增加了从综合社交网络平台收集的数据的数量、丰富程度和质量,为来自不同学科的研究人员提供了使用机会和数据驱动系统连接到更广阔的HCI领域的用户经验,吸引了跨学科研究人员,因为我们看到对消费技术、软件和系统的需求,以及社会网络与我们日常生活的整合,使广告和营销等行业转型,以及将分享个人数据的挑战纳入公众意识。但是,如何理解日新月异的大型社会数据数量,从而更好地了解日益复杂、数据驱动的数字系统,使我们更好地了解并改进用户在日益复杂、数据驱动的数字系统上的经验。这一联系与可用性和数据驱动系统连接在一起,吸引跨学科研究人员,因为我们看到对消费技术、软件和系统的需求,以及社会网络与日常生活的整合。 大部分在社交网络上发布的数据往往具有文字性质,从而进一步联系到语言、心理学和心理语言和语言学,从而更好地了解互联网和在线数字系统之间的关系。在这个理论中,我们提出了一个新的概念框架以一个复杂的数字系统为基础,利用收集的纵向数据和在线数据分析,从在线数据系统,从在线数据库中提取了一个图像和预测数据,从这一系统,用个人行为分析,从这一系统建立到个人行为状态数据系统,从这一系统建立了一个图像和预测状态状态状态状态数据系统。