scholarly journals Pushing the (Visual) Narrative: the Effects of Prior Knowledge Elicitation in Provocative Topics

2020 ◽  
Author(s):  
Jeremy Heyer ◽  
Nirmal Kumar Raveendranath ◽  
Khairi Reda
Keyword(s):  
Future Research ◽  
Controversial Issues ◽  
Visual Narrative ◽  
Attitudinal Change ◽  
Prior Beliefs ◽  
Research Directions ◽  
New Information ◽  
Textual Data ◽  
Data Representations ◽  
Future Research Directions

Narrative visualization is a popular style of data-driven storytelling. Authors use this medium to engage viewers with complex and sometimes controversial issues. A challenge for authors is to not only deliver new information, but to also overcome people’s biases and misconceptions. We study how people adjust their attitudes toward (or away from) a message experienced through a narrative visualization. In a mixed-methods analysis, we investigate whether eliciting participants’ prior beliefs, and visualizing those beliefs alongside actual data, can increase narrative persuasiveness. We find that incorporating priors does not significantly affect attitudinal change. However, participants who externalized their beliefs expressed greater surprise at the data. Their comments also indicated a greater likelihood of acquiring new information, despite the minimal change in attitude. Our results also extend prior findings, showing that visualizations are more persuasive than equivalent textual data representations for exposing con- tentious issues. We discuss the implications and outline future research directions.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

Information Processing: A Critical Literature Review and Future Research Directions

2003 ◽  
Vol 45 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Graham Spickett-Jones ◽  
Philip J. Kitchen
Keyword(s):  
Information Processing ◽  
Cognitive Mapping ◽  
Research Direction ◽  
Future Research ◽  
Extensive Literature ◽  
Attitudinal Change ◽  
Research Directions ◽  
Cognitive Information Processing ◽  
Cognitive Information ◽  
Future Research Directions

This conceptual paper concerns information processing, and focuses on the methods and mechanisms used by marketers and academics in attempting to explore mental processes, particularly regarding perception and cognitive mapping in relation to marketing communications. The paper reviews the extensive literature in this domain, deriving information and models from a wide variety of disciplines including: cognitive information processing, attitudes and attitudinal change, elaboration and receiver involvement, sub-routines and sub-processors, semiotics, cognitive science and psycholinguistics. We conclude by suggesting that each of these disciplines has a role to play in terms of future research direction, and that the field of information processing still provides a rich and fertile basis for significant developments to take place.

scholarly journals The Support Person's Preferences and Perspectives of Physical Activity Programs for Older Adults With Cognitive Impairment

2021 ◽  
Vol 9 ◽  
Author(s):  
Terence W. H. Chong ◽  
Emily You ◽  
Kathryn A. Ellis ◽  
Kay L. Cox ◽  
Karra D. Harrington ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Female Gender ◽  
Imaging Biomarkers ◽  
Future Research ◽  
Research Directions ◽  
New Information ◽  
Cognitive Benefits ◽  
Future Research Directions

Objectives: Physical activity (PA) is beneficial for older adults' cognition. There is limited research investigating perspectives of support persons (SPs) of next-of-kins (NOKs) with cognitive impairment. This exploratory study aimed to investigate perspectives of SPs of older adults with Alzheimer's Dementia (AD) or Mild Cognitive Impairment (MCI).Methods: A telephone survey of 213 SPs of NOKs from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) was undertaken to quantitatively assess SPs' beliefs and knowledge about PA benefits, current PA level of their NOK, and PA program preferences. The contribution of age, gender, diagnosis and mental health symptoms was assessed using multiple logistic regression analyses.Results: Many SPs were aware of PA benefits for memory (64%) and believed it would help their NOK (72%). Older SP age was associated with less awareness of benefits (p = 0.016). SPs caring for male NOKs were more likely to believe that PA would be helpful than those caring for female NOKs (p = 0.049). NOK AD diagnosis (rather than MCI) (p = 0.014), older age (p = 0.005) and female gender (p = 0.043) were associated with lower PA levels. SPs were mixed regarding preference for their NOKs to participate in individual (45%) or group (54%) PA. Many SPs wanted to participate in PA with their NOK (63%).Conclusions: The results highlight that SPs have high levels of awareness of the cognitive benefits of PA, and describe their preferences regarding PA programs. The findings provide new information to inform targeted public health messaging, PA prescribers and providers, and future research directions.

Other Important Questions: When, How, and Why Do Cultural Values Influence Performance Management?

2016 ◽  
Vol 9 (2) ◽  
pp. 343-350 ◽  
Author(s):  
Inchul Cho ◽  
Stephanie C. Payne
Keyword(s):  
Performance Management ◽  
Cultural Values ◽  
Performance Appraisal ◽  
Future Research ◽  
Controversial Issues ◽  
Research Needs ◽  
Rating Systems ◽  
Research Directions ◽  
Diverse Workforce ◽  
Future Research Directions

Adler et al. (2016) raise some controversial issues about whether performance rating systems should be eliminated or not. We strongly believe that the decision to do away with performance ratings is premature because more research needs to be done, as suggested by “the better questions” that Adler et al. listed at the end of the focal article. We propose that those questions can be extended further by testing them in other cultures and supplemented with these questions: When, how, and why do cultural values influence performance management? Given the nature of our increasingly diverse workforce in which employees with different cultural values work together within the same organization, it is crucial to identify and document the influence of culture on performance appraisal practices. In this commentary, we briefly summarize research to date on the influence of cultural values on performance management and identify some important future research directions.

Analysis of Online Social Networks for the Identification of Sarcasm

2018 ◽  
pp. 92-105 ◽  
Author(s):  
Pulkit Mehndiratta
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Future Research ◽  
Research Directions ◽  
Textual Data ◽  
Future Research Directions

With the ever-increasing acceptance of online social networks (OSNs), a new dimension has evolved for communication amongst humans. OSNs have given us the opportunity to monitor and mine the opinions of a large number of online active populations in real time. Many diverse approaches have been proposed, various datasets have been generated, but there is a need of collective understanding of this area. Researchers are working around the globe to find a pattern to judge the mood of the user; the still serious problem of detection of irony and sarcasm in textual data poses a threat to the accuracy of the techniques evolved till date. This chapter aims to help the reader to think and learn more clearly about the aspects of sentiment analysis, social network analysis, and detection of irony or sarcasm in textual data generated via online social networks. It argues and discusses various techniques and solutions available in literature currently. In the end, the chapter provides some answers to the open-ended question and future research directions related to the analysis of textual data.

scholarly journals A Systematic Survey on Deep Generative Models for Graph Generation

2020 ◽  
Author(s):  
Xiaojie Guo ◽  
Liang Zhao
Keyword(s):  
Generative Models ◽  
Future Research ◽  
Specific Domain ◽  
Research Directions ◽  
Wide Range ◽  
Data Representations ◽  
Future Research Directions ◽  
Definition Of ◽  
Rich History ◽  
Graph Generation

Graphs are important data representations for describing objects and their relationships, which appear in a wide diversity of real-world scenarios. As one of a critical problem in this area, graph generation considers learning the distributions of given graphs and generating more novel graphs. Owing to its wide range of applications, generative models for graphs have a rich history, which, however, are traditionally hand-crafted and only capable of modeling a few statistical properties of graphs. Recent advances in deep generative models for graph generation is an important step towards improving the fidelity of generated graphs and paves the way for new kinds of applications. This article provides an extensive overview of the literature in the field of deep generative models for graph generation. Firstly, the formal definition of deep generative models for the graph generation as well as preliminary knowledge is provided. Secondly, two taxonomies of deep generative models for unconditional, and conditional graph generation respectively are proposed; the existing works of each are compared and analyzed. After that, an overview of the evaluation metrics in this specific domain is provided. Finally, the applications that deep graph generation enables are summarized and five promising future research directions are highlighted.

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