Modelling and Analysing Behaviours and Emotions via Complex User Interactions
Summary: arXiv:1902.07683v1 Announce Type: cross
Abstract
Over the past 15 years, the volume, richness, and quality of data collected from combined social networking platforms has increased beyond all expectation. This surge in data availability has provided researchers from a variety of disciplines with invaluable resources for their studies. More impactfully, it has laid the groundwork for a range of new products and services that have transformed industries such as advertising and marketing. At the same time, it has brought the challenges of sharing personal data into the public consciousness.
However, the question remains: how can we make sense of the ever-increasing volume of big social data to better understand and improve user experiences in increasingly complex, data-driven digital systems? This intersection with usability and user experience within data-driven systems bridges into the wider field of Human-Computer Interaction (HCI). It attracts interdisciplinary researchers, as we witness the growing demand for consumer technologies, software, and systems, coupled with the integration of social networks into our daily lives.
Moreover, the predominantly textual data posted on social networks creates a further link to linguistics, psychology, and psycholinguistics, enabling a better understanding of the relationship between human behaviours both offline and online.
Research Overview
In this thesis, we present a novel conceptual framework based on a complex digital system utilizing collected longitudinal datasets to predict system status. This prediction is based on the personality traits and emotions extracted from text posted by users. The framework was developed using a dataset collected from an online scholarship system, where the digital behaviour and social network interactions of 2000 students were recorded for this study.
Contextual Framework and Literature Review
We contextualize this research project with a comprehensive review and critical analysis of the current literature in psycholinguistics, artificial intelligence, and human-computer interaction. This analysis reveals a significant gap in mapping and understanding digital profiling against system status, highlighting an area ripe for exploration and development.
Significance of the Study
The implications of this research are vast. By enhancing our understanding of user interactions and emotional responses through complex data analysis, we can create more responsive and user-centric digital environments. This can lead to improved product offerings, more effective marketing strategies, and a heightened awareness of the ethical considerations surrounding personal data usage.
Future Directions
As technology continues to evolve, the methodologies and frameworks developed in this research will be essential for further studies. Future research could explore:
- The integration of additional data sources beyond social networks.
- Advanced techniques for emotion detection and analysis.
- The development of real-time user experience feedback systems.
- Ethical considerations in data collection and usage.
In conclusion, the intersection of data science, psychology, and HCI offers a rich landscape for future research, fostering an understanding that not only benefits academic inquiry but also enhances the user experience across digital platforms.