A review on the visual design styles in data storytelling based on user preferences and personality differences

The proliferation of data analytics has led to a vast application of data visualization and storytelling in a variety of disciplines extending across banking, sports to healthcare. Data, information, and knowledge are transformed into interactive visual representations that convey a meaningful story. In big data analytics, relevant and high-quality graphical insights ought to be factually accurate and relevant to make a key decision. Data storytelling has become an effective way to apply information visualization as it can enhance communication effectiveness. Using visualization as a tool to enhance narrative for the viewers in enforcing data storytelling as a way to understand data and information. Findings suggest that an individual's personality variations correspond strongly with a user's preference toward visual design styles for visualization and storytelling. This paper investigates previous studies regarding personality, information visualization, narrative, and storytelling, as well as their interrelationships through online databases. The futuredirection of the present study

Human factors visualization and storytelling design questionnaire: validity and reliability tests

In the era of data and the quest for data-driven decision-making, the capacity to tell stories from data is proving to be increasingly valuable. Storytelling is not only a great method displaying facts, but also an effective approach to package knowledge and information so that everyone can understand it. While numerous tools are available to produce a strong storytelling visualization at the moment, the tools' ability to be personalized by the user is almost non-exist. To determine the connection between personality and user preferences toward different visual designs, this study employs the user preferences on visual design related to the data visualization questionnaire that was used in the previous research. In this research, the data storytelling element was added; hence the questionnaire needs to be evaluated based on validity and reliability test. In this paper, the validity and reliability tests of the questionnaire will be explained thoroughly. The outcome of this test will help in producing a questionnaire that is valid and reliable for upcoming research related to the field

Visual design elements for data storytelling based on personality traits: a case of undergraduate students

Prior studies found a strong correlation between a user's preference for information visualization and the variances in their personality. Visualization software have evolved recently and have given users a number of advantages. Although the usability aspects of visualization software have advanced significantly, they previously adopted a more general approach with their user interface and ignored user variations. There is a dearth of prior study examining user traits and preferred visual data storytelling elements. The use of individual variations as an adaptation metric in visualization tools has shown promise in overcoming the drawbacks of general approach from the user interface of visualization tools. This study intends to investigate into how users' preferences from the various visual design elements for storytelling relate to their personalities among university students in one institution. The study investigates whether personality affects user preferences for various visual design elements such as font type, font size, hierarchical, and comparative visualizations, which are extensively used in contemporary research and are associated with personality. To this end, a personality indicator will be performed to gauge each participant's personality. The participants will next answer a different series of questions on their preferences in visual design elements for data visualization and storytelling. This study may help to address the general issue with visualization tools and may suggest significant implications that may be used in the future design of visualization tools to improve the intuitiveness of the tool to suit various type of users, as well as for the visualization designers by choosing the best graphical visualizations for any given target audience to provide greater comprehension of the information in making critical decision

Personality differences and user preferences in visual design styles for data storytelling: a work in progress

In recent years, the Information Visualization (InfoVis) tools have improved and benefited many users. Although the available tools have evolved increasingly in terms of general use and usability, they have historically followed a one-size-fits-all model, ignoring user variations, forcing users to adapt to the interfaces and features that are more general with no emphasis on the visual design styles that are appropriate for different groups of users. According to the literature, research on human factors that include users’ characteristics and personality differences, and their visual design preferences for storytelling is scarce. This study aims to investigate the relationship between personality and the users’ preferences in visual design styles for the storytelling of data to make impactful and insightful reports. This paper presents a work-in-progress relating to personality differences based on the Five-Factor Model (FFM) and user preferences with five elements of data storytelling, to produce meaningful reports

Investigating User Preferences Towards Visualization Types with a Focus on Neuroticism and its Implications on Mental Health

This study delves into the importance of visualizing mental health data to attain a comprehensive understanding of psychological disorders through the means of data storytelling and information visualization. Utilizing the Five Factor Model, the article investigates the correlation between two prevalent mental health conditions, depression and anxiety, with a specific focus on the neuroticism trait. The researchers aim to underscore the potential of data-driven techniques in shaping mental health interventions and promoting a deeper comprehension of these conditions by exploring the association between neuroticism and various visualization approaches. To achieve this objective, the study employs quantitative research methods to support population association analysis, cause-and-effect analysis, and the assessment of the link between independent and dependent variables. The findings show that neuroticism has no relationship to any sort of visualization types, and this paper presented several explanations for the lack of a relationship between visualization type and neuroticism

Investigating User Preferences towards Visualization Types with a Focus on Neuroticism and its Implications on Mental Health

This study delves into the importance of visualizing mental health data to attain a comprehensive understanding of psychological disorders through the means of data storytelling and information visualization. Utilizing the Five Factor Model, the article investigates the correlation between two prevalent mental health conditions, depression and anxiety, with a specific focus on the neuroticism trait. The researchers aim to underscore the potential of data-driven techniques in shaping mental health interventions and promoting a deeper comprehension of these conditions by exploring the association between neuroticism and various visualization approaches. To achieve this objective, the study employs quantitative research methods to support population association analysis, cause-and-effect analysis, and the assessment of the link between independent and dependent variables. The findings show that neuroticism has no relationship to any sort of visualization types, and this paper presented several explanations for the lack of a relationship between visualization type and neuroticism