# Beyond Numbers: Creating Analogies to  Enhance Data Comprehension and
Communication with Generative AI

Qing Chen  Intelligent Big Data Visualization Lab, Tongji
UniversityShanghaiChina<EMAIL_ADDRESS>,  Wei Shuai  Intelligent Big
Data Visualization Lab, Tongji UniversityShanghaiChina<EMAIL_ADDRESS>,  Jiyao Zhang  Intelligent Big Data Visualization Lab, Tongji
UniversityShanghaiChina<EMAIL_ADDRESS>,  Zhida Sun  College of
Computer Science & Software Engineering, Shenzhen UniversityShenzhenChina
<EMAIL_ADDRESS>and  Nan Cao  Intelligent Big Data Visualization Lab,
Tongji College of Design and InnovationShanghaiChina<EMAIL_ADDRESS>
(2024)

###### Abstract.

Unfamiliar measurements usually hinder readers from grasping the scale of the
numerical data, understanding the content, and feeling engaged with the
context.  To enhance data comprehension and communication, we leverage
analogies to bridge the gap between abstract data and familiar measurements.
In this work, we first conduct semi-structured interviews with design experts
to identify design problems and summarize design considerations. Then, we
collect an analogy dataset of 138 cases from various online sources. Based on
the collected dataset, we characterize a design space for creating data
analogies. Next, we build a prototype system, AnalogyMate, that automatically
suggests data analogies, their corresponding design solutions, and generated
visual representations powered by generative AI. The study results show the
usefulness of AnalogyMate in aiding the creation process of data analogies and
the effectiveness of data analogy in enhancing data comprehension and
communication.

creativity support, interview, lab study, prototyping/implementation,
qualitative methods, quantitative methods

††submissionid: 5015††copyright: acmcopyright††journalyear: 2024††doi:
10.1145/3613904.3642480††conference: Make sure to enter the correct conference
title from your rights confirmation email; May 11–16, 2024; Honolulu,
USA††price: 15.00††isbn: 978-1-4503-XXXX-X/18/06 Figure 1. An example of the
AnalogyMate system automatically suggests data analogy for the inputs. The
system first suggests data analogies according to the input, then generates
corresponding design solutions and visual representations for illustrations.
Finally, we show a user-rendered version of one of the analogy designs.

##  1\. Introduction

In our daily lives, we constantly encounter a wide range of numerical data
(Feigenson et al., 2004), whether in news articles, government reports,
financial statements, or scientific journals. However, numerical data’s sheer
scale or intricacy, often coupled with unfamiliar measurements and abstract
representations, poses barriers to comprehension and communication. This
barrier can lead to misunderstandings or misinterpretations, hindering readers
from grasping the key information that authors intend to convey through the
data. To illustrate this, consider the headline “Every day, 1.3 billion
plastic bottles are sold worldwide, stacking up to the height of half the
Eiffel Tower”, with an illustrated figure shown in Fig. 4(a). The primary
purpose of this example is to enable the readers to perceive the numerical
data and realize the underlying significance of the environmental damage
caused by such a large amount of plastic bottles. By introducing such a data
analogy and its corresponding visual presentation, we bridge the gap between
the abstract data “1.3 million” and the familiar measurements “the height of
half the Eiffel Tower”. In doing so, readers could better grasp the scale of
the numerical data, enhancing their understanding of the content while feeling
more engaged with the context.

The data analogy technique, involving juxtaposing abstract data with familiar
objects or measurements, has become a powerful tool to improve the effective
communication of data significance. This approach is rooted in understanding
and empirical research that analogies serve as cognitive bridges, facilitating
the comprehension and communication of numerical information (Riederer et al.,
2018). Despite the benefits of data analogies, it remains challenging for
amateurs to design and create them with infographics. The creation process
involves two stages. First, the ideation stage requires designers to develop
appropriate analogical objects with proper measurements and scales,
demonstrating the associations between the original object and the numerical
data. Second, the design stage demands diverse visual design skills and
knowledge from designers to produce visual representations with proper layout
and contrast to convey the message. Designers used to search for relevant
ideas, numbers, and images on multiple online resources and build everything
from scratch. This process is usually time-consuming for experts and even more
challenging for amateurs.

Previous work has proposed some automatic methods to improve efficiency in the
analogy design process. For example, Kim et al. (Kim et al., 2016) developed
tools for creating personalized spatial analogies using a user’s location and
a comprehensive landmark dataset to provide contextual information for spatial
measurements. Hullman et al. (Hullman et al., 2018) introduced a set of tools
designed to automatically generate re-expressions for unfamiliar measurements
by leveraging measurements of familiar objects. However, existing approaches
have often depended on compiling and curating object-measurement databases
within specific domains, limiting their capacity to generate diverse and
innovative design solutions. Furthermore, there is a significant demand for an
ideation and design tool that offers an integrated approach to facilitate the
process of creating data analogies.

Recently, the emergence of Large Language Models (LLMs), such as GPT-3 (Brown
et al., 2020), has expanded beyond the confines of domain-specific datasets
and could efficiently provide more diverse inspirations. Meanwhile, Text-to-
Image Generative Models, such as Stable Diffusion (Rombach et al., 2022),
offer an additional efficiency dimension in producing visual representations
for data analogies. These advanced Generative AIs possess the capability to
generate design ideas and produce illustrative materials based on the
generated text descriptions, thus streamlining the data analogy creation
process.

However, using generative AI techniques to assist in the data analogy creation
process presents several challenges. First, there is a lack of systematic
design space for data analogies. Such a design space could serve as a guiding
framework for large language models in their design process, ensuring that
they could generate contextually relevant and insightful analogies. Second,
crafting a data analogy solution involves multiple steps. It requires a
nuanced understanding of the underlying meaning of numbers and objects and
several design considerations, such as the style of illustrations, color
palettes, and their alignment with the thematic context. To streamline the
intricate process, it is imperative to develop a systematic pipeline to
generate analogy solutions step-by-step. This pipeline should encompass the
important stages of data analogy creation, from concept development to visual
representation designs, ensuring a cohesive and efficient workflow. Last, to
facilitate the creation process and provide a user-friendly experience, we
need to carefully design tailored user interactions while integrating
different Generative AI techniques into such a creativity support tool for
data analogies.

In addressing these issues, this paper embarks on a mission to leverage data
analogy to bridge the gap between abstract data and familiar measurements,
thereby  facilitating data comprehension and enhancing data communication.  We
elaborate on our iterative research process following the framework
illustrated in Fig. 2. We first conduct semi-structured interviews with design
experts to identify the problems in design practices and derive design
considerations from the feedback. Then, we collect exemplars from various
online sources to characterize the design space when creating illustrations of
data analogies. Next, we propose a data analogy generation pipeline and
develop a prototype system that automatically suggests data analogy choices
with their corresponding design solutions. The system also supports
generalizing detailed images powered by generative AI to illustrate the
analogies.  Finally, we conduct two user studies. The first user study is a
within-subject lab study with 16 amateur designers to evaluate the utility of
our prototype system in facilitating the creation of data analogies. The
second user study is a crowdsourcing task with 80 participants to examine the
effectiveness of the created data analogies using AnalogyMate in improving
data comprehension.  The major contributions of the paper are as follows:

  * •

A design space for data analogies based on the collected dataset of 138
classified data analogy cases.

  * •

A creativity support tool called AnalogyMate that facilitates the creation of
data analogies using generative AI, and two user studies that demonstrate its
effectiveness in enhancing data comprehension and communication.

Figure 2.  The research process of the paper, where the blue boxes are
different research activities while the green boxes are the output of such
activities.

##  2\. Related Work

###  2.1. Data Analogy

The importance of data is increasingly evident in both scientific and societal
contexts (Kjelvik and Schultheis, 2019), underscoring the crucial need for a
profound understanding, precise analysis, accurate interpretation, and
effective communication of data results (Muñiz-Rodríguez et al., 2020).
Unfamiliar measurements or numbers are frequently encountered in textual
content, often posing challenges for readers due to their lack of immediate
meaning without the appropriate contextual cues (Barrio et al., 2016).  A
strategic way to improve how well people understand and communicate data is to
use data analogy, which connects abstract data to objects or measurements that
people are familiar with. This technique addresses the challenge of making
data more accessible, as Riederer et al. (Riederer et al., 2018) provided
empirical evidence showcasing the pivotal role of analogies in facilitating
numerical comprehension and recall.

From a theoretical standpoint, Chevalier et al. (Chevalier et al., 2013)
proposed a framework to facilitate the comprehension of quantitative
information through visual representations. Barrio et al. (Barrio et al.,
2016) extended this by introducing a set of perspective templates,
specifically designed to convey unfamiliar measurements to a broad audience
across formats.  Riederer et al. (Riederer et al., 2018) introduced the
concept of “re-expression” using measurements of concrete objects for
unfamiliar measurements.  These approaches employ the re-expression method,
substituting familiar measurements for less accessible ones, whereas the
perspective template approach distills data into simpler forms using
percentages, ratios, and multiples. The data analogy technique synthesizes
both methods, incorporating elements of both to transform complex information
into more comprehensible terms. Despite these valuable contributions, prior
research had yet to categorize data analogies systematically. To fill this
gap, this paper introduces a design space of data analogies by systematically
classifying 138 cases collected from various prominent websites.

Meanwhile, some researchers explored automatic methods for generating data
analogies. Hullman et al. (Hullman et al., 2018) pioneered the creation of a
comprehensive database encompassing familiar objects and  their corresponding
measurements.  They also created an innovative tool capable of translating
less known measurements into terms of familiar objects’ measurements. For
example, Kim et al. (Kim et al., 2016) introduced a tool for generating
personalized spatial analogies to help readers understand distances and areas
in text articles. However, traditional automatic analogy generation tools
relied heavily on curated data sets  while we utilized the capabilities of
GPT-3.5 to assist users in analogy designs. Traditional methods are typically
confined to analogies within the same measurement, whereas our approach
enables measurement transformation. Additionally, we employ stable diffusion
to generate illustration materials based on design schemes.

###  2.2. Creativity Supporting Tool

Creativity was once considered a unique skill that makes humans distinctive
(Sawyer, 2011). However, with significant advancements in LLMs and natural
language processing (NLP) techniques,  the utilization of artificial
intelligence to construct Creativity Support Tools (CSTs, e.g., (Wan and Lu,
2023; Wang et al., 2010)), has emerged as a trend. These tools facilitate a
spectrum of tasks performed by designers (Kim et al., 2022), physicians (Zhou
et al., 2023), and researchers (Portenoy et al., 2022).

In the realm of AI-supported CSTs, two major groups could be delineated based
on the specific aspect of the process they support. One group of CSTs focuses
on background research and ideation evaluation (Frich et al., 2019). For
instance, Zhang et al. (Zhang et al., 2022) have developed Story Drawer, a
cooperative drawing platform between children and AI that aims to promote
children’s imaginative visual storytelling. Wang et al. (Wang et al., 2010)
designed Idea Expander, which facilitates group brainstorming by providing a
dynamic visual communication channel and pictorial stimuli. Moreover, Karolus
et al.(Karolus et al., 2023) generated meaningful task-related proficiency
feedback to improve user awareness of the issues in their writing samples.

The other group of CSTs aims to assist the designing process by directly
generating new ideas or giving hints on the target topics (Frich et al.,
2019). Wan et al. (Wan and Lu, 2023) proposed a StyleGAN-driven digital mood
board, GAN-Collage, integrating AI-generated visual ideas into the ideation
phase to support creativity. Mozafari et al.(Mozaffari et al., 2022)
introduced an approach for both targeted and serendipitous UI design
inspiration on image-based inputs. Moreover, some researchers leverage
metaphor and analogy to find connections between ideas that share similar
characteristics. For example, Sun et al. proposed using metaphor’s target-
source concepts to facilitate visual design ideation (Kang et al., 2021) and
gesture design (Sun et al., 2022) imaginatively. To support science writers in
explaining unfamiliar concepts, Kim et al. (Kim et al., 2023) launched
Metaphorian, which facilitates the search, extension, and iterative revision
of extended metaphors.

The current trend of CSTs could be summarized as low maturity, all-process
support, multi-device compatibility, low complexity, low availability, and low
expertise (Frich et al., 2019).  Our work focuses on facilitating the entire
creativity process, by highlighting generating, evaluating, and utilizing
ideation in analogy designs and their downstream tasks. Building on the
concept of inspiring creativity in CST design (Reed et al., 2023; Krauß et
al., 2022), our tool supports the designer and authors in performing end-to-
end analogy generation tasks through analysis, revision, and utilization of
inspirations.

###  2.3. LLM-Supported Design

The emergence of Large Language Models(LLM) such as GPT-3 (Brown et al., 2020)
and Large-scale Text-to-image Generation Models(LTGM) like Stable Diffusion
(Rombach et al., 2022) has spurred a wave of research in the Human-Computer
Interaction (HCI) domain. Researchers have been investigating the integration
of advanced artificial intelligence technologies to support various tasks.
These tasks span a wide spectrum of HCI applications, encompassing question-
answering systems (Ma et al., 2023), story generation (Dang et al., 2023;
Chung et al., 2022), automatic generation of visualizations (Dibia, 2023) and
personalized news recommendation (Liu et al., 2023). For example, CodeToon
(Suh and An, 2022) is a visual programming environment that fosters
collaborative comic creation through generative conversational AI. Opal (Liu
et al., 2022) leads users in a systematic exploration of visual concepts and
offers a pathway for generating illustrations based on article tone, keywords,
and relevant artistic styles using semantic search and methods of prompt
engineering GPT-3.

In the context of pre-trained generative models, prompt engineering methods
offer an alternative approach to model fine-tuning (Wang et al., 2023). Recent
work in prompt engineering has proposed several methods to improve prompts
(Haviv et al., 2021; Jiang et al., 2020). Specifically, the development of
Low-code LLM (Cai et al., 2023) and PromptCrafter (Baek et al., 2023) have
been dedicated to capturing user intent through well-crafted text prompts and
optimizing the wording of prompts. Dang et al. (Dang et al., 2023) integrated
a user interface (UI) for phrase suggestions and a UI for zero-shot prompt
inputs into an LLM. Our approach integrates the chain-of-thought technique
(Wei et al., 2022) and leverages few-shot examples (Brown et al., 2020) to
enhance the prompt optimization, ultimately aiming for improved and more
effective outcomes. In our study, we employ GPT-3.5 as a knowledge repository
to assist users in interactively designing data analogies and illustrations.
Additionally, we offer prompting solutions that establish connections between
Large Language Models and Large-scale Text-to-image Generation Models.

##  3\. Preliminary Study

To better understand the workflow and design choices in creating data
analogies, we conducted in-depth interviews with four experienced domain
experts and summarized the design considerations from expert feedback.

###  3.1. Expert Interview

We conducted semi-structured interviews with four design experts (E1-E4, two
females) following established research guidelines (Lazar et al., 2010). Each
interview lasted approximately one hour and was conducted online via meeting
software that enables screen sharing. All the interviewees have over 7 years
of experience in data-related design and have personally created or frequently
encountered data analogies. We first introduced our research topic and the
core concepts (i.e., data analogy) to the experts. Next, we asked them a set
of prepared questions under two topics: the usage scenarios of data analogies
and the general workflow for creating data analogies. Example questions
included queries such as, “what are the common use cases, and what motivates
their adoption?”, “could you outline the typical creation process for data
analogies?”, and “what factors and design requirements should be considered
during the creation process?”. Throughout the interview process, we asked
follow-up questions if we noticed that an interviewee’s answer was unclear or
if we wanted to dig deeper into the details of their daily conduct.

We collected a substantial amount of qualitative data from the interviews. We
transcribed the audio recordings and then coded the texts to achieve our
interview objectives (design workflow and design consideration) through
thematic analysis (Braun and Clarke, 2006). Two authors were responsible for
the coding process. Initially, we independently coded the transcriptions,
marking sentences relevant to our research questions. Subsequently, for each
research question, we read through all marked sentences, generated codes from
them, and grouped similar codes. After independent coding, we met to compare
our codes and discussed any discrepancies until we reached an agreement. We
then coded all transcriptions using the latest codes. Our findings are
summarized into two parts: design workflow of data analogy creation, and
design considerations for building a creativity support system.

###  3.2. Design Workflow

During the interview process, we identified a common need to provide a
systematic design pipeline from all the experts. The pipeline of data analogy
creation can be typically divided into two sequential stages: analogy design
and illustration design. We summarize the related expert feedback as follows.

Provide a systematic pipeline. All four experts mentioned that establishing a
well-organized analogy design process is necessary and beneficial. E1
expressed the desire for users to engage in interactive decision-making at
crucial moments. E2 also shared his ideal workflow, stating that “I hope the
system can provide automatic recommendations for different analogy solutions
and design schemes.”

Facilitate analogy design. All four experts agree that analogy design is a
time-consuming process that requires domain knowledge and iterative
refinement. E1, E3, and E4 emphasize that analogy design demands sensitivity
to data, an understanding of conceptual nuances within the data, and relevant
domain expertise. E1 suggests, “Data analogy design typically involves
systematic brainstorming to find the most fitting metaphorical expressions.”

Assist illustration design. Two experts (E1 and E2) believe that the visual
design of data analogy illustrations is crucial. E2 shared his personal
experience of spending a significant amount of time searching for suitable
image materials, stating that “the main challenge lies in the
conceptualization of the design process.” E1 added, “it is important to align
the color palette of the illustrations with the style of the article.”

###  3.3. Design Cosideration

Combined with expert interviews and our previous literature review, we have
derived the following design considerations for the proposed system,
specifically aimed at addressing their key concerns.

C1. Understand the implications of the data content. In designing data
analogies, it is crucial to help the audience quickly grasp the underlying
implications of the data content descriptions. As mentioned by E3, “the goal
of designing data analogies is not merely to present the data but to encourage
readers to engage in deeper contemplation.”

C2. Enhancing data understanding through analogy designs. Based on previous
literature and expert feedback, we have summarized four key factors
influencing data comprehension: object familiarity, concreteness, similarity
(Hullman et al., 2018; Kim et al., 2016), and the perceptibility of numerical
values (Bautista et al., 2011). For the first three factors, we aim to
leverage user-friendly interactions, allowing users to customize their
preferences according to their needs. Concerning perceptibility, we also
considered this when designing prompts. The specific implementation of the
four key factors can be found in Section 5.3.

C3. Ensure the consistency of illustration styles with the topic domain. All
the experts concurred that the design of illustrations should align with the
thematic essence conveyed by data analogies. For instance, if a data analogy
addresses a serious subject, the design of the illustrations should exude a
sense of seriousness. Maintaining a consistent style across multiple
illustrations is imperative. Illustration design involves considering numerous
factors, encompassing emotions, stylistic elements, and color palettes. Our
system adheres to various design principles to ensure the coherence of visual
elements with the narrative topic throughout the design process.

C4. Enable user control for customization and personalization. All experts
expressed their preferences for controlling the generated results at various
stages. First, they wish to have control over different analogy strategies to
explore different analogy ideas. Second, when designing for different
scenarios, they hope to adjust parameters to meet specific requirements. After
the system generates automatic analogy descriptions, they are more inclined to
review and modify the generated analogies for fine-tuning. Finally, when
creating final illustrations using generative models, users would like to
adjust keywords or prompts for better control over the generated illustrative
materials.

##  4\. Design Space

Previous studies lacked a systematic design space of data analogies. To
provide guidance for our system, we collected a dataset of 138 cases from
online sources. Based on a systematic classification of these cases, we
proposed the design space for creating data analogies.

###  4.1. Dataset Construction

In our data collection and labeling process, two researchers with different
backgrounds (one majoring in HCI and one with a data science background) were
involved. To ensure a standardized procedure, they carried out the data
collection, labeling, and screening process separately. We initiated our study
by conducting a comprehensive search for relevant datasets that had been
previously released in academic research and other sources, including
infographics (Borkin et al., 2013) and data videos (Yang et al., 2023). These
databases were sourced from reputable sources (e.g., The Washington Post,
Reuters) and were of high quality (e.g., winners of Kantar Information is
Beautiful Awards). To compile a diverse set of analogy cases, we then
collected our analogy exemplars by querying and browsing multifacet data-
related online sources. Our source website covers various types of media,
including news articles (30%), social media (20%), infographic websites (30%),
and institute websites (20%). After identifying the analogy cases from the
websites, we deconstructed them into analogy pairs. After the dataset was
ready, the two researchers conducted the data labeling process independently.
They labeled each case with predefined data types and representation styles.
Inter-rater reliability was quantified by Cohen’s kappa coefficient: 0.95
(analogy strategy), 0.97 (measurement transformation), 0.82 (data binding
types), 0.98 (presentation form) and 0.98 (layout). For the inconclusive
cases, two researchers proceeded with the screening process by first
extracting the controversial ones. Subsequently, they engaged in a discussion
to assess whether these cases met the definition of data analogy and
eliminated any unqualified items. The resulting data analogy cases were
compiled into an Excel file, where any duplicates or unachievable cases (i.e.,
protected by property rights or refused to be reprinted) were removed.
Finally, after all the data were ready, two researchers resolved the
differences of opinion arising from the collection and labeling processes
through further discussions.

The final dataset consists of 138 analogy cases, totaling 3,178 analogy pairs,
which were extracted from 66 different online pages. The topic of these
analogy cases covers fields of health (15.4%), entertainment (23.1%), ecology
(10.1%), popular science (23.3%), and society (18.5%).  The dataset is
available at https://analogymate.github.io/designspace/.

Figure 3. Data analogy design space, which consists of two perspectives: data
analogy design (analogy strategy, measurement transformation, and data binding
type) and data analogy presentation (presentation form and layout).

###  4.2. Data Analogy Classification

Our deconstructive analysis of the collected data serves two primary purposes:
to classify data analogy cases and to enhance our understanding of how
concepts are conveyed through various relationships between objects and
entities. We then construct our design space from two perspectives: data
analogy design and data analogy presentation (as shown in Fig. 3). The former
relates to the data analogy ideation process, including analogy strategies,
measurement transformation, and data binding types. The latter corresponds to
the data analogy visual presentation process, including presentation form and
layout.

####  4.2.1. Data Analogy Design

To understand how data analogies are created, we surveyed previous literature
and analyzed the data analogy dataset we collected. We then characterized
three key elements of data analogy design: analogy strategy, measurement
transformation, and data binding type.

In terms of analogy strategy, Chevalier et al. (Chevalier et al., 2013)
demonstrated 3 object-to-object relations with regard to their measurable
properties on a scale: comparisons, containment, and unitization. Comparison
(Fig. 4(a)) focuses on the elementary-level shared properties between objects.
This aligns with one-to-one type analogies, where objects are directly
compared with another analogical object. Containment refers to objects placed
within a container. Unitization (Fig. 4(b)) involves redefining an object as a
new unit of measurement. Similar to the untization strategy, we proposed
another strategy, accumulation, which is also commonly applied in analogy
designs. When the scale of the original object is too small to grasp,
accumulation strategy (Fig. 4(c)) is employed to present a bunch of the
original objects to form another analogical object, which is the opposite
operation as in unitization.  Hullman et al. (Hullman et al., 2018) defined
proportional analogy as expressing a pair of measurements using two familiar
objects that have measurements with the same ratio, and we also adopted
proportion(Fig. 4(c)) strategy. In conclusion, we have expanded the
classification of data analogy strategies, which include comparison,
unitization, accumulation, and proportion. By analyzing our dataset, we found
that the most commonly used strategy is comparison (73.4%), followed by
accumulation (13.7%), proportion (7.2%), and unitization (5.8%).

Figure 4. Four data analogy examples from our collected dataset. (a) 1.3
billion bottles are sold daily compared with the Eiffel Tower. (b) Five grams
of plastic are consumed weekly compared to a porcelain soup spoon. (c) The
wealth difference between Jeff Bezos and an American mid-class family compared
with the size difference between a white blood cell and a finback whale. (d)
Each year, 75.6 trillion gallons of water are added to the ocean, which equals
114.4 million Olympic-size swimming pools.

In terms of measurement transformation, we found that for some measurements
like temperature, time, and wealth, it is sometimes difficult to design proper
analogies within the same data measurements. For example, Fig. 4(c) shows an
analogy example using measurement transformation, in which the wealth gap
between Jeff Bezos and a typical middle-class family is measured as the size
of a white blood cell and a finback whale. The transformation process, or the
switch of measurement, conveys a more intuitive message about the given
objects. In our dataset, a significant 67.6% of analogical cases involve
measurement transformation.

In terms of data binding type, we have identified seven common data binding
types used in data analogies. Four of the seven data binding types have been
included in previous research (Shi et al., 2022), namely length, area, volume,
and quantity. These four types are also common measurements that are easily
depicted in static illustrations. The other three types, temperature, time,
and abstract concepts, are derived from our collected dataset. All three
additional data binding types are highly related to human experience. By
analyzing our dataset, we found that length(50, 35.9%) stands out as the
dominant data binding type, with volume (34, 24.5%), area (28, 20.1%), and
quantity (14, 10.1%) trailing behind. Abstract concepts (10, 7.2%) also play a
vital role in pictorial visualizations while time (2, 1.4%) and temperature
(1, ¡1%) were rarely utilized in pictorial visualizations. Such design
decisions may be due to the relatively low perception of these human
experiences.

####  4.2.2. Data Analogy Presentation

To present an analogy, there are various presentation forms, including
illustrations, infographics, video, interface website, etc. By analyzing our
dataset, we have classified those presentation forms into static and dynamic
forms (as shown in Fig. 3). We observed that the majority of data analogy
cases (89, 64.5%) in our dataset use static forms, while the other cases (49,
35.5%) apply dynamic forms.

The layout of objects is another crucial consideration in creating visual
representations. Phillips and McQuarrie (Phillips and McQuarrie, 2004)
proposed a typology to distinguish different metaphor structures based on the
complexity and ambiguity of the visual structure. We adopted two
classifications, juxtaposition, and fusion, that are applicable in data
analogy scenarios. As shown in Fig. 3, the juxtaposition layout refers to a
graphic in which objects and analogy entities are separated and distributed in
parallel, while the fusion layout refers to a graphic in which objects and
analogy entities blend harmoniously into one.  In our dataset, apart from a
few cases containing only text (14, 10.2%), the juxtaposition layout (82,
59.4%) has a numerical advantage over the fusion layout (42, 30.4%). The
fusion layout, blending original and analogical objects harmoniously into one
design, is more challenging and time-consuming than the juxtaposition layout.
This challenge arises from the need to achieve a seamless integration and
coherent visual representation.

##  5\. System

To support creative design with data analogy, we developed a prototype system
called AnalogyMate through an iterative process. The system (Fig. 5) employs
an automated two-stage pipeline (Fig. 6) to suggest analogy design and
illustration design. In the analogy design process, the underlying meaning of
the sentence is interpreted to suggest analogy objects better match the theme
(C1), and four factors are taken into consideration to enhance data
comprehension through generated design solutions (C2). In the illustration
design process, the system suggests different aspects of the illustrations
according to the theme (C3). Also, users can make crucial decisions
interactively at critical points throughout the process (C4).

Figure 5. System interface (left) and illustration created by users using
generated analogy and materials (right). The system in this screenshot has
suggested an analogy list for the input “Every day 1.3 billion plastic bottles
are sold around the world” and generated illustration design solutions for the
chosen analogy.

###  5.1. Interface Design and User Interaction

AnalogyMate consists of three main views: the Input View, the Generator View,
and the Refinement View. Input View (V1) allows users to input original data
description sentences containing numerical data. Users can select different
design strategies according to their requirements. Generator View (V2)
automatically suggests data analogies, their corresponding design solutions,
and illustration materials. Refinement View (V3) is where users can sort the
analogy list to meet their specific needs and make adjustments to the
generated results.

Here is how the system works. Users begin by entering the Input View (depicted
in Fig. 5-1), where they input description sentences containing numerical
data. Users can choose between simple data and proportion data as their input
formats. For simple data, users have the option to choose from three analogy
strategies: comparison, unitization, and accumulation. If users are unsure
about which analogy strategy to use, they can select the “unclassified”
button, allowing the system to generate various types of analogies using all
the above strategies. Next, users click the “Generate Analogies” button, and
the system returns a sorted list of generated analogies, as shown in Fig. 5-2.
Users can update the sorted list by adjusting the weights of three factors
using the slider bars at the bottom, aligning the results with their
preferences. Subsequently, users can select one of the analogies and click on
the “choose and edit” button. The selected analogy result then appears in the
text area shown in Fig. 5-3(a). Here, users can make modifications to the
analogy description and then click the button to generate an illustrated
design scheme, which includes the interpretation of the data analogy theme
(Fig. 5-3(b)) and the recommended illustration keywords (Fig. 5-3(c)). Among
those keywords, “visual attributes” include keywords related to the
illustration’s emotion, style, and color palette, while “objects” and
“background” respectively represent the recommended illustration objects and
background. Users can add or modify keywords and select multiple keywords to
generate illustration materials (Fig. 5-3(d)).

###  5.2. System Pipeline

We introduce an automated two-stage pipeline (Fig. 6) to construct the
AnalogyMate system, tailored for creating data analogies with analogy designs
and corresponding illustration materials. In the first stage, the system
proceeds through three steps to generate data analogies. In the second stage,
it generates illustration design schemes along with the corresponding
illustration materials.

####  5.2.1. Stage 1: Analogy Design

This first stage consists of three major steps: generate analogy objects,
modify generated analogy objects, and calculate and polish the descriptive
sentence.

Generate Analogy Objects. When a user inputs a sentence containing numerical
data into AnalogyMate, the system generates familiar objects with their
measurements according to the chosen analogy strategies. This process is
accomplished using GPT-3.5, which involves three specific steps: prompt
selection, guided design, and theme interpretation. To design prompts, we
start by selecting representative data analogy examples as few-shot examples.
After that, based on the user’s chosen analogy strategies, we provide
corresponding guidelines to guide the design process. Finally, to interpret
the underlying theme of the input, we utilize both few-shot examples and the
chain-of-thought method.

Modify Generated Analogy Objects. To enhance the data accuracy and clarity of
generated analogy objects, we employ GPT-3.5 to perform a two-step correction.
In the first step, we verify and adjust the numerical values of the analogy
objects. This step enables us to correct obvious errors, such as changing
“depth of swimming pool: 0.2 meters” to “depth of swimming pool: 2 meters”. In
the second step, we address issues where data descriptions lack clarity. For
example, we may modify “the height of a house: 7 meters” to “the height of a
two-story house: 7 meters.” This further refines and optimizes the generated
analogy objects by providing more specific measurement values.

Figure 6. Pipeline of AnalogyMate. In the analogy design process, the system
first generates analogy objects, then modifies the results, and finally
calculates and polishes the analogy sentence. In the illustration design
process, the system suggests illustration design solutions and then generates
illustration materials according to the keywords selected. Users can make
crucial decisions interactively at critical points throughout the process.

In our experiments, we observed that using GPT-3.5 to calculate generated
analogies within the same conversation can lead to calculation errors and
inconsistencies between the calculation results and the answers. To address
these issues, we opt to perform calculations on our backend server. Then, we
construct sentences using predefined text templates. Following this, we
utilize GPT-3.5 for sentence refinement using news report tone.

####  5.2.2. Stage 2: Illustration Design

The second stage involves two major steps: generate illustration design
solutions and generate illustration materials.

Generate Illustration Design Solutions. This step also employs the chain-of-
thought method (Fig. 6). Initially, GPT-3.5 needs to understand the intended
meaning of the analogy sentence to ensure that the generated illustration
schemes align with the theme and emotions users want to convey. Second, our
system suggests different aspects of the illustrations, including visual
attributes, objects, and backgrounds. Visual attributes encompass factors such
as emotion, color palette, and style. Previous research (Wei-ning et al.,
2006) has shown that emotions in images can be conveyed through color factors
such as color temperature, brightness, and color contrast. Therefore, when
generating color palettes, our system suggests these four factors based on the
theme. Finally, each aspect of the illustration design results is extracted
into keywords.

Generate Illustration Materials. We utilize the Stable Diffusion API to
generate illustration materials. To ensure consistency in style among the
generated illustration materials, we maintain constant keywords of visual
attributes when generating the main object and background images. This
approach facilitates the post-processing and integration by designers,
ensuring that the final illustrations maintain a cohesive and harmonious
style.

###  5.3. Implementation Detail

This system was implemented using Python’s Flask for the backend, while the
frontend was developed with React, CSS, JavaScript, and Axios. Additionally,
utilized the Stable Diffusion and GPT-3.5 APIs to perform the analogy design
and illustration design tasks.  To enhance data comprehension, we have
incorporated the factors outlined in C2 into our system: object familiarity,
concreteness, similarity, and numerical value perceptibility. For the initial
three factors, our aim is to enable user-adjustable weights, offering
customization according to individual preferences. Regarding perceptibility,
we consider it when designing prompts.

####  5.3.1. Object-entity Similarity

The similarity between objects and analogy entities strongly influences the
freshness and vitality conveyed by analogy cases. In the field of Natural
Language Processing (NLP), quantifying similarity between concepts has been
achieved, with notable frameworks such as WordNet (Budanitsky and Hirst, 2006)
and ConceptNet (Speer et al., 2017). To ensure consistency and avoid the
impact of different score ranges, we normalized the indicators.

(1) $\displaystyle\text{Norm}(I(x,y_{i}))$
$\displaystyle=\frac{\text{I}(x,y_{i})-\min_{(x,y_{j})\in(x,Y)}\text{I}(x,y_{j})}{\max_{(x,y_{j})\in(x,Y)}\text{I}(x,y_{j})-\min_{(x,y_{j})\in(x,Y)}\text{I}(x,y_{j})}$

Note $x$ refers to the object concept of a given data analogy case. $Y$
represents the set of analogy entity concepts. $I(x,y_{i})$ is the measurement
to be normalized, such as similarity in WordNet.

Accordingly, we define a similarity term to quantify and adjust the semantic
relationship between object and entity, given as

(2) $\displaystyle S_{i}$
$\displaystyle=w_{1}\text{Norm}(\text{Path}(x,y_{i}))+w_{2}\text{Norm}(\text{Corr}(x,y_{i}))$

where $Path(x,y_{i})$ measures the length of the shortest path in the WordNet
hyponym/hypernym graph (Perkins, 2010; Hullman et al., 2018). $Corr(x,y_{i})$
represents relatedness defined in ConceptNet, which exhibits a high
correlation with the human gold-standard ranking, as measured by its Spearman
correlation (Speer et al., 2017).

####  5.3.2. Familiarity

Familiarity is a vital factor to measure in analogy design (Hullman et al.,
2018). A familiar analogy entity provides the audience with abundant
information to relate immediately to their own experiences. While WordNet does
not directly measure familiarity, many researchers use the frequency of each
synset’s occurrence in a semantic concordance as a familiarity score
(Kwankajornkiet et al., 2016; Cohen et al., 2021). Therefore, we include the
word frequency database as a component of our familiarity measurement.  We use
SUBTLEX-UK (Van Heuven et al., 2014) for word frequency statistics, which is a
widely accepted database in the fields of linguistics and natural language
processing research. We define our familiarity measurement as follows:

(3) $\displaystyle F_{i}$
$\displaystyle=w_{3}\text{Norm}(\text{Syn}(y_{i}))+w_{4}\text{Norm}(\text{Freq}(y_{i}))$

where $Syn(y_{i})$ represents the number of synsets in WordNet related to
concept $y_{i}$, and $Freq(e_{i})$ denotes the word frequency based on the
SUBTLEX-UK word database. $w_{3}$ and $w_{4}$ are user-adjustable parameters
that determine the weight of these factors in the familiarity measurement.

####  5.3.3. Concreteness

Concreteness evaluates the perceived level of an entity or concept. Hullman et
al. (Hullman et al., 2018) refers that concrete metaphors should be objects
with physical forms. We quantified the concreteness scores using WordNet
(Budanitsky and Hirst, 2006) and collected scores from human-evaluation-based
database (Brysbaert et al., 2014). Feng et al. (Feng et al., 2011) and
Bolognes et al. (Bolognesi et al., 2020) suggested a correlation between
hypernyms, hyponyms, and concept concreteness. This inspires us to use
hypernyms and hyponyms as a term to measure concreteness. Therefore, we define
our Concreteness term as

(4) $\displaystyle C_{i}$
$\displaystyle=w_{5}\text{Norm}(\text{hypernyms}(y_{i})/\text{hyponyms}(y_{i}))+w_{6}\text{Norm}(\text{Conc}(y_{i}))$

where $Conc(y_{i})$ represents the concreteness score in Brysbaert findings
(Brysbaert et al., 2014). $\text{hypernyms}(y_{i})$ and
$\text{hyponyms}(y_{i})$ denote the number of hypernyms and hyponyms linked
with word or concept $y_{i}$. Both scores are weighted by $w_{5}$ and $w_{6}$.

####  5.3.4. Perceptibility of Numerical Values

In our preliminary study, E1 emphasized the importance of numbers being easily
perceptible to readers when reading articles. Previous systems encouraged
multipliers within the range of 1 to 10 for analogy generation (Hullman et
al., 2018), we adopted this range in our study. Regarding the unitization
strategy, E1 stated, “unitization is often used to convey a large value for
impactful delivery, and readers are sometimes forgiving of precision
discrepancies.” Therefore, we limited the multiplier to values exceeding 1000.

##  6\. Evaluation

We conducted two user studies to evaluate the validity and effectiveness of
our proposed method and prototype system.  In the first user study, we
examined whether and how AnalogyMate contributes to the design of data
analogies and the creation of the corresponding illustrations. The second user
study aimed to examine the effectiveness of the data analogy created by
AnalogyMate in enhancing data comprehension.

###  6.1. User Study I: Creating Data Analogies with AnalogyMate

To gain insights into whether and how AnalogyMate contributes to data analogy
design and related illustration creation, we conducted a within-subject study,
comparing AnalogyMate against the conventional web search results.

####  6.1.1. Experiment Design

As prior research has indicated (Petridis et al., 2021), web search engines
serve as a powerful tool for generating ideas and creating visual materials.
Designers often utilize Internet searches to help find data analogy
inspirations and related illustration materials as raw materials.
Specifically, we assessed whether users could (1) generate more data analogy
designs and (2) improve the efficiency of illustration production with
AnalogyMate. Additionally, we evaluated our tool’s ability to assist users
engaged in creative work through a Creativity Support Index (CSI) evaluation
questionnaire (Cherry and Latulipe, 2014).

####  6.1.2. Participants

We recruited 16 amateur designers through social media and word-of-mouth (P1
P16, 12 females, with an average age of 22.5 years and a standard deviation of
3.37). They come from diverse backgrounds, primarily students, including both
undergraduate and graduate students. Additionally, some work in the internet
industry, while others are research assistants. The recruitment criteria were
that they all had prior experience in design activities, possessed a basic
proficiency in design software, and expressed an interest in creating data
analogy illustrations. These participants would receive a commemorative gift
as a reward for completing the tasks. They were also allowed to receive two
additional awards (one creativity award and one design award) after we
collected all the user study results, which the domain experts would select.

####  6.1.3. Procedure and Tasks

During the study, participants were tasked with creating data analogies and
producing illustrations, with one utilizing the baseline (internet search) and
the other employing the AnalogyMate system. Initially, participants were
randomly assigned two out of four descriptive sentences with numerical
content, which were as follows: “If all of Greenland’s ice melts, global sea
levels are expected to rise by 7 meters”, “In order to rescue the children
trapped in the cave, firefighters pumped out 1.2 billion liters of
floodwater”, “Every day, 1.3 billion plastic bottles are sold around the
world”, and “The ratio of Bezo’s wealth to those of middle-class American
families is about 900,000 to 1”. For the assigned sentences, they were
required to develop as many data analogy design schemes as possible within a
specific time limit. Subsequently, participants were asked to select their
favorite design scheme from each set and generate illustration materials for
the two data analogies. One set of illustration materials was to be generated
using the AnalogyMate system, while the other set required participants to
input keywords into the system or utilize Google Image search to produce
images. Participants alternated between the baseline and system conditions,
with their condition order randomly assigned (either baseline first and then
system or system first and then baseline) to mitigate potential learning
effects. All the provided and generated materials will be presented in the
supplementary materials.

At the outset, participants were introduced to the fundamental concepts of
data analogy design. They were allowed to familiarize themselves with the
dataset we had compiled to understand the concept of data analogy. Prior to
using the AnalogyMate system, they received a brief introduction and
demonstration of the system. Participants were randomly assigned two sentences
with numerical content and were tasked with designing data analogy schemes for
them. For each sentence, they were allocated 15 minutes to brainstorm as many
design schemes as possible. Subsequently, they were required to generate
illustration materials for the selected two proposals independently. We
recorded the number of illustration materials ultimately chosen by the
participants and the total time spent on generating design materials. Finally,
participants were instructed to create the final illustrations using the
selected materials on their own. After the experiment concluded, we invited
participants to complete the CSI evaluation questionnaire, which measures six
dimensions of creativity support (Cherry and Latulipe, 2014): Exploration,
Expressiveness, Immersion, Enjoyment, Results Worth Effort, and Collaboration,
to assess the usability of our system.

####  6.1.4. Result Analysis

We collected qualitative and quantitative feedback from the participants and
summarized them as follows.

The amount of analogy ideas. Participants generated a significantly larger
amount of data analogies than the baseline using AnalogyMate. On average,
participants generated five analogies (SD=1.26) using the AnalogyMate, whereas
the average number of analogies with baseline was 3.873 (SD=1.09). Since this
study employed a within-subjects design and involved count data, we conducted
a paired-sample Wilcoxon test, revealing that the difference between these
means was statistically significant (p ¡ 0.004). In terms of creativity
assistance, all the 16 participants unanimously favored AnalogyMate for the
following reasons: (1) Internet searches for specific keywords relied heavily
on individual knowledge and consistently yielded specific keyword data,
limiting the exploration of related concepts (P2, P9, P12, and P15); (2)
AnalogyMate provided diverse creative ideas and inspired creativity (P2, P3,
P11, and P12); (3) the interpretation of themes and recommendations of
keywords saved time in generating suitable illustration materials (P4 and P6).

The time involved in creation.  AnalogyMate enables participants to find
illustration materials in significantly less time than the baseline. On
average, participants spent 7.92 minutes generating usable materials with the
AnalogyMate (SD=2.21), while the average time spent with the baseline was 5.91
minutes (SD=1.91). Since this study employed a within-subjects design and
involved count data, we conducted a paired-sample Wilcoxon test, which
revealed that the difference between these means was statistically significant
(p ¡ 0.007). When asked which tool was more efficient for finding usable image
materials, all 16 participants unanimously agreed that the AnalogyMate saved
them more time. The two main reasons users mentioned were as follows: (1) The
recommended keywords helped them clarify their desired materials. (2)
AnalogyMate could generate images based on selected keywords, which proved
especially effective for sourcing image materials that were not readily
available online.

System design. In general, users expressed their appreciation for the system’s
ability to generate analogy designs. As stated by P4 (F, age=22), “…When I
first looked at the data, I did not have any ideas, but AnalogyMate provided
many potential analogies and design schemes, some of which proved challenging
for me to conceive independently.” Moreover, P11 (F, age=22) mentioned that
system-generated designs were regarded as inspirations to spark their
creativity. Additionally, a majority of the users found the system’s keyword
recommendation and selection feature valuable. For instance, when users feel
uncertain about illustration design, the recommended keywords can provide
inspiration. As mentioned by P6 (Female, age=20), ”The recommended keywords
cover most of the content that I want to convey. Some detailed keywords, such
as color temperature and emotions, are more detailed than what I could have
thought, and they enhance the final expression of the images.”

The quality of generated results. All participants found the results generated
by AnalogyMate useful and inspiring. Although, despite the two-step correction
provided in our pipeline and system, users still reported several inaccuracy
occasions due to the limitations of GPT-3.5. For example, P6(F, age=20)
mentioned that the ratio of the height of the Himalayas to the height of one
step is not exactly 900,000:1. This discrepancy occurred because the height of
the Himalayas divided by 900,000 is mistakenly calculated by GPT. Nonetheless,
users still found the generated content inspiring and convenient. As in the
inaccuracy case P6(F, age=20) encountered, the participant commented, “I still
think it is helpful to generate the height of the Himalayas as an analogy. I
can easily do the simple calculation myself if I want it to be more precise,
and sometimes, the accuracy is not a major concern for my targeted audience.”

Scale | Avg. Factor Counts (SD) | Avg. Factor Score (SD) | Avg. Weighted Factor Score (SD)
---|---|---|---
Collaboration | 15.19 (3.15) | 0.63 (0.81) | 9.19 (12.18)
Enjoyment | 17.81 (2.01) | 1.94 (1.18) | 35.16 (23.04)
Exploration | 16.56 (2.53) | 4.19 (0.91) | 69.38 (19.32)
Expressiveness | 15.63 (3.05) | 3.63 (0.96) | 56.19 (17.65)
Immersion | 15.25 (3.53) | 1.5 (0.82) | 23.31 (14.48)
Results Worth Effort | 16.69 (1.89) | 3.13 (1.31) | 51.75 (21.73)
Table 1. CSI Results from a Data Analogy Creation Study Using AnalogyMate
(N=16). The average CSI score for AnalogyMate in this study was 81.63
(SD=9.99).

SCI Score of AnalogyMate.  Table 1 displays the average factor counts, average
factor scores, and average weighted factor scores for the six factors of the
CSI (Cherry and Latulipe, 2014). Average factor counts can gauge which factors
are most important for the experimental task, with the highest possible count
for any specific factor being 5. According to Table 1, exploration seems to be
the most crucial for participants, while collaboration appears to be less
relevant or important to users.  Each factor has a factor score, which is the
total of the agreement statement responses for that factor. The responses are
on a 0 to 10 scale, and a higher score indicates that the tool supports that
factor better. In Table 1, we observe that all factors received relatively
high ratings, with Exploration, Enjoyment, and Results Worth Effort scoring
the highest. However, since exploration is particularly vital for this task,
it receives the highest weighted factor score. Weighted factor scores are
calculated by multiplying a participant’s factor agreement scale score by the
factor count to make them more sensitive to the factors most important for the
given task. In the end, we computed the average CSI score using the formula
mentioned in (Cherry and Latulipe, 2014) and obtained a score of 81.63
(variance = 9.99), which can be considered a good creativity support score
(B-grade). This implies that AnalogyMate can provide reasonable creativity
support for users engaged in designing data analogies and illustrations but
still has room for improvement. Participants were asked to provide
explanations for each score, with P9 (F, age=21) giving a lower score for
exploration and mentioning the desire to include more historical exploration
and exploration records.

####  6.1.5. Expert Evaluation.

To further evaluate the results created by our participants in the user study,
we invited two domain experts: a designer with eight years of experience in
data visualization (E5) and an academic researcher with over ten years of
experience in the interdisciplinary domain of design and technology (E6). We
asked the experts to provide their professional opinions and relative ratings
on the illustration drafts produced by our participants. Their assessments
covered four aspects: whether the analogy design makes it easier to understand
the data content, whether the analogy design helps to grasp the scale of the
given data, whether the analogy design helps engage the audience with the
context, and whether the analogy design is creative.

We asked them to think aloud their opinions and ideas any time they wanted
during the interview sessions. We also posed follow-up questions whenever we
observed that an interviewee’s response was unclear or if we sought deeper
insights into the specifics of their daily practices.  We organized and coded
the interview data using the same method described in Section 3.1. After
analyzing the interview data from the two experts, we identified several
common traits in the works deemed as “good analogy designs”.

Clarity in expressing data and relationships. All the works regarded as good
representations of data analogies exhibited a high degree of clarity in the
expression of data and relationships. For instance, in Fig. 7, P6 (F, age=20)
compared the distance between the connected plastic bottles with the distance
from Earth to the Moon. These representations are clear and intuitive in their
visual presentations, facilitating immediate understanding.

Relevance of chosen analogy objects and themes. Another common trait of good
data analogies was the relevance of chosen analogical objects and themes. For
example, in Fig. 7, P5 (F, age=22) compared the thickness of a sheet of paper
(0.104mm) with the thickness of 900,000 sheets of paper (93.6m) to illustrate
a 900,000:1 disparity. Using paper invoked associations with paper money,
which emphasized the theme of “wealth disparity”. P11 (F, age=22) associated
plastic bottles with the Earth, a linkage that both experts noted as being
related to environmental conservation.

Metaphorical information embedded within the theme. The inclusion of
metaphorical information within the theme was also a common characteristic of
good data analogy examples. For instance, in P16’s (F, age=20) illustration
draft, 7 meters of seawater submerged two stories of a building. Both experts
noted that this analogy effectively conveys the gravity of the consequences of
global warming. It prompts readers to contemplate the repercussions of people
and buildings being inundated, offering them meaningful inspiration.

Despite the shared opinions of the common traits for good analogy designs, we
also found the two experts have their own personal preferences regarding
analogy designs. E2, the academic researcher, tended to prefer analogy objects
that were more surprising and creative, while E1, the industry practitioner,
leaned towards more familiar analogy objects. This divergence may stem from
the two experts’ different backgrounds and working scenarios. For example, P4
(F, age=22) compared the amount of melted ice in Greenland to “40,000 years of
the water flow of Niagara Falls.” E1 praised its creativity and impact, while
E2 expressed concerns about the reader’s familiarity with Niagara Falls.

Figure 7. The four analogy design examples created by the participants in User
Study I.

###  6.2. User Study II: Evaluating Data Comprehension with AnalogyMate

Figure 8. One example case used in User Study II. The baseline without analogy
is on the left, while the analogy design created with AnalogyMate is on the
right.

To validate the effectiveness of the system-generated data analogies in
enhancing data comprehension, we conducted a crowdsourced study, comparing raw
data (without analogy) with results generated by AnalogyMate.

####  6.2.1. Experiment Design

We adapted the methods used by Sun et al. (Sun et al., 2022), in which a
crowdsourcing task is designed to collect participants’ feedback on their
understanding of autogenerated data. In general, our study aims to collect
participants’ responses regarding three perspectives: their understanding of
the given data, the extent of measurement of the data scale, and the degree of
engagement with the context.

####  6.2.2. Participants

We posted our study form online and recruited participants through the
Prolific crowdsourcing platform, restricting potential participants to those
residing in the US or UK (who speak fluent English) to reduce language
barriers. Notifications were provided for participants to acknowledge the
study’s purpose and the fact that no sensitive information was required. We
recruited 5 participants for a pilot study and a total of 80 participants for
the main experiment. The experiment was only available to participants who did
not enroll in our pilot study. Contributors were rewarded $\$1.24$ for an
estimated (and actual) completion time of 10 minutes. The time estimation was
based on contributions from in-person participants.

####  6.2.3. Procedure and Tasks

In the user study, eight analogy cases were selected for testing, with each
analogy strategy comprising two, covering each topic. Each case presents
visualizations of the baseline (raw data, referred to as infoPoor) and the
data analogy design (infoRich). By rearranging them, we designed eight user
study forms of 18 pages each. Each stimulus occupies one page (16 pages in
total). The experiment starts on page 2, with notifications on the first page
and validation checks on the last.

On each stimulus page, participants were asked to rate according to the
following three statements:

  1. (1)

The statement and graphics make it easier for me to understand the data (S1).

  2. (2)

The statement and graphics help me grasp the scale of the given data (S2).

  3. (3)

The statement and graphics engage me with the context (S3).

Participants were allowed to revisit previous pages at any time during the
experiment.

Fig. 8 presents an example case in our user study, with the infoPoor stimuli
on the left, infoRich stimuli on the right. The aforementioned three rating
statements were listed under every stimulus. After finishing all the
questions, every participant must complete our validation check by confirming
they had done our user study carefully. The validation check contains the
first stimulus that appears in the given form and one stimulus that does not
appear.

####  6.2.4. Result Analysis

We got valid data from $N=80$ participants ($N=10$ per form). The number was
chosen to achieve an available sample size for the t-test. Participants are
46.0% female, with a mean age of 39 and a standard deviation of 13.4. Most of
them are from the UK (66.3%).

Out of the 113 contributors who agreed to the consent form, 33 did not finish
the experiment, resulting in an attrition rate of 29.3%. Among those, 54.5%
failed the validation check, and 45.4% quit for unknown reasons. We then
manually examined the time of completion and rating sequence to drop out
obvious duplicate answers. To further eliminate threats to validity, we
compared the standard deviation of rating scores between the infoRich and
infoPoor to make sure participants did not drop out considerably more often in
one condition than the other. The difference in standard deviations between
the two distributions inforich and infopoor are 0.05 (S1), 0.01 (S2), 0.06
(S3). Thus, we found no evidence that rating scores were distributed
differently across two aspects of the same data analogy case. In other words,
participants did not exhibit significant differences in the distribution of
ratings when evaluating different aspects of data analogy cases. This
contributes to ensuring the stability and consistency of the evaluation
results, enhancing the credibility of the research findings.

S1 revolves around the understanding of data, which refers to the ability to
interpret, analyze, and make meaningful sense of information presented in a
structured or unstructured form. S2 focuses on the “scale of data”, or the
level of measurement or the type of data you are dealing with. S3 quantifies
how well the audience is engaged with the analogy context. We illustrate a
t-test to determine whether there are significant differences between infoRich
and infoPoor. We set a null hypothesis $H_{0}$ that the distribution of the
two subsets is the same. One-tail t-test demonstrates a result less than
$<1\%$. It is safe to conclude that data analogy enhances the audience’s
understanding of the data, helps them measure the scale of data more
precisely, and participants feel engaged in the data context.

##  7\. Discussion

In this section, we further discuss the findings of our study. First, we
reflect on some of the lessons learned and design reflections during the
implementation of AnalogyMate. Next, we explore the generality of data
analogies and propose potential application domains that could benefit from
our tool. Finally, we summarize the limitations of our study and outline
directions for future work.

###  7.1. Design Reflections

Leveraging the strengths of AIGC in data analogy design. Artificial
Intelligence Generated Content (AIGC) offers a significant advantage in
providing quick access to extensive knowledge. It can respond to user queries
with standard information based on its vast knowledge base. Moreover,
achieving precise and stable mathematical calculations and understanding using
the current version of LLMs and Generative Models remains a challenge.
Therefore, it is essential to leverage AIGC techniques in specific steps where
they excel and complement them with human intervention to bridge this gap
effectively.

Balancing diverse results with precise control in data analogy generation. In
the process of generating data analogies, we observed the impact of utilizing
GPT-3.5 for divergent thinking in analogy creation. Allowing GPT-3.5 to
generate analogies without restrictions on object types yields diverse and
imaginative responses, but when imposing limitations on object types, the
generated lists tend to converge, resulting in a slight reduction in overall
creativity. Therefore, when harnessing the power of GPT-3.5 to assist in the
design process, it is essential to strike a careful balance between leveraging
its divergent thinking capabilities and imposing constraints, a balance that
should be determined based on the specific usage scenarios and design
requirements of the task at hand.

###  7.2. Usage Scenario and Application Domains

Our evaluation results demonstrate the substantial potential of data analogies
in conveying intricate information in a more accessible manner, thereby
enhancing data comprehension and communication effectiveness. Drawing insights
from preliminary study interviews, the collected dataset, and feedback from
participants in user studies, we ascertain its broad applicability across
diverse domains. Notable application areas include news reporting, where data
analogy proves valuable in presenting challenging numerical information in a
more understandable form (E3, E4, P11). In popular science and education
materials, data analogy serves as a powerful medium for explaining complex
concepts to audiences with varying levels of numerical proficiency, making the
learning process more engaging (P14). In marketing and advertising, data
analogy can elevate the appeal and effectiveness of advertisements through
conceptual transformations and visual effects, capturing the interest of
potential customers (E2). Furthermore, in cross-industry collaboration and
reporting, the use of data analogy facilitates better understanding among
participants from different domains, encouraging participation, cooperation,
and knowledge transfer in discussions (E3, E4, P11).

###  7.3. Limitations and Future Work

This work has several limitations which could provide avenues for future
exploration.

First, due to the scope of our research, we primarily focused on designing
data analogies with the assistance of large language models which can only
provide general knowledge. Nevertheless, individuals from different regions,
age groups, and cultures may possess varying levels of familiarity with
certain concepts. Therefore, a future focus is the integration of knowledge
bases alongside large language models to tailor analogies to specific audience
groups.

Second, the current analogy objects are generated by GPT-3.5, which may
introduce occasional errors in size measurements and calculations, despite our
implementation of various strategies to minimize such inaccuracies.
Additionally, there are numerous ethical and responsibility-related issues
associated with the generated models that warrant discussion (Xiao et al.,
2023). These issues may result in biases in the generated analogies and
illustration materials, as well as an increased risk of copyright
infringement. While some research is focused on mitigating these biases
(Luccioni et al., 2023; Schramowski et al., 2023), further studies are still
required. Therefore, fostering critical thinking is crucial for both creators
and audiences of AI-generated content.

Third, in AnalogyMate, the unitization strategy may yield results with large
numerical values, potentially challenging for users to perceive. In future
work, our goal is to allow users to choose whether they want to redesign the
results for enhanced perceptibility when necessary.

Finally, our future endeavors involve the integration of personal attributes
with large language models to facilitate personalized design recommendations.
Additionally, we aspire to develop a more cohesive and integrated tool that
enables the direct output of well-designed illustrations by the system,
providing a more seamless user experience.

##  8\. Conclusion

In this work, we introduced AnalogyMate, a design support tool that
facilitates data analogy creation. Specifically, we first characterized a
design space of data analogy based on the collected dataset, previous
literature, and preliminary interviews. Then, we proposed an automatic
pipeline for data analogy creation and implemented a prototype system
following the pipeline. Our prototype system has been proven to significantly
assist users in generating more creative ideas and reducing the time spent
searching for illustration materials. The user study results further
demonstrated the effectiveness of the data analogies created with AnalogyMate
in enhancing data comprehension and communication.

###### Acknowledgements.

The corresponding author of this paper is Nan Cao. This work was supported in
part by the NSFC 62372327, 62002267, 62072338 and NSF Shanghai 23ZR1464700. We
would like to thank Xuechen Li for the interface design and our domain
experts, including Guanhong Liu, Xian Bai, Yanqiu Wu, Xingyu Lan, Lai Xu, and
Yumin Hong for their valuable advice. Additionally, we extend our thanks to
the anonymous reviewers for providing constructive feedback.

## References

  * (1)
  * Baek et al. (2023) Seungho Baek, Hyerin Im, Jiseung Ryu, Juhyeong Park, and Takyeon Lee. 2023.  PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM.  arXiv preprint arXiv:2307.08985.
  * Barrio et al. (2016) Pablo J. Barrio, Daniel G. Goldstein, and Jake M. Hofman. 2016\.  Improving Comprehension of Numbers in the News. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ (San Jose, California, USA) _(CHI ’16)_. Association for Computing Machinery, New York, NY, USA, 2729–2739.  https://doi.org/10.1145/2858036.2858510
  * Bautista et al. (2011) Susana Bautista, Raquel Hervás, Pablo Gervás, Richard Power, and Sandra Williams. 2011\.  How to Make Numerical Information Accessible: Experimental Identification of Simplification Strategies. In _Human-Computer Interaction – INTERACT 2011_ , Pedro Campos, Nicholas Graham, Joaquim Jorge, Nuno Nunes, Philippe Palanque, and Marco Winckler (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 57–64.
  * Bolognesi et al. (2020) Marianna Bolognesi, Christian Burgers, and Tommaso Caselli. 2020.  On abstraction: decoupling conceptual concreteness and categorical specificity.  _Cognitive Processing_ 21, 3 (2020), 365–381.
  * Borkin et al. (2013) Michelle A Borkin, Azalea A Vo, Zoya Bylinskii, Phillip Isola, Shashank Sunkavalli, Aude Oliva, and Hanspeter Pfister. 2013\.  What makes a visualization memorable?  _IEEE transactions on visualization and computer graphics_ 19, 12 (2013), 2306–2315.
  * Braun and Clarke (2006) Virginia Braun and Victoria Clarke. 2006.  Using thematic analysis in psychology.  _Qualitative research in psychology_ 3, 2 (2006), 77–101.
  * Brown et al. (2020) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020\.  Language models are few-shot learners.  _Advances in neural information processing systems_ 33 (2020), 1877–1901.
  * Brysbaert et al. (2014) Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. 2014\.  Concreteness ratings for 40 thousand generally known English word lemmas.  _Behavior research methods_ 46 (2014), 904–911.
  * Budanitsky and Hirst (2006) Alexander Budanitsky and Graeme Hirst. 2006.  Evaluating wordnet-based measures of lexical semantic relatedness.  _Computational linguistics_ 32, 1 (2006), 13–47.
  * Cai et al. (2023) Yuzhe Cai, Shaoguang Mao, Wenshan Wu, Zehua Wang, Yaobo Liang, Tao Ge, Chenfei Wu, Wang You, Ting Song, Yan Xia, et al. 2023\.  Low-code LLM: Visual Programming over LLMs.  arXiv preprint arXiv:2304.08103.
  * Cherry and Latulipe (2014) Erin Cherry and Celine Latulipe. 2014.  Quantifying the Creativity Support of Digital Tools through the Creativity Support Index.  _ACM Trans. Comput.-Hum. Interact._ 21, 4, Article 21 (jun 2014), 25 pages.  https://doi.org/10.1145/2617588
  * Chevalier et al. (2013) Fanny Chevalier, Romain Vuillemot, and Guia Gali. 2013\.  Using concrete scales: A practical framework for effective visual depiction of complex measures.  _IEEE transactions on visualization and computer graphics_ 19, 12 (2013), 2426–2435.
  * Chung et al. (2022) John Joon Young Chung, Wooseok Kim, Kang Min Yoo, Hwaran Lee, Eytan Adar, and Minsuk Chang. 2022.  TaleBrush: Sketching Stories with Generative Pretrained Language Models. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 209, 19 pages.  https://doi.org/10.1145/3491102.3501819
  * Cohen et al. (2021) Jed J Cohen, Valeriya Azarova, Christian A Klöckner, Andrea Kollmann, Erica Löfström, Giuseppe Pellegrini-Masini, J Gareth Polhill, Johannes Reichl, and Douglas Salt. 2021.  Tackling the challenge of interdisciplinary energy research: A research toolkit.  _Energy Research & Social Science_ 74 (2021), 101966.
  * Dang et al. (2023) Hai Dang, Sven Goller, Florian Lehmann, and Daniel Buschek. 2023\.  Choice Over Control: How Users Write with Large Language Models Using Diegetic and Non-Diegetic Prompting. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 408, 17 pages.  https://doi.org/10.1145/3544548.3580969
  * Dibia (2023) Victor Dibia. 2023\.  LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models.  arXiv preprint arXiv:2303.02927.
  * Feigenson et al. (2004) Lisa Feigenson, Stanislas Dehaene, and Elizabeth Spelke. 2004\.  Core systems of number.  _Trends in cognitive sciences_ 8, 7 (2004), 307–314.
  * Feng et al. (2011) Shi Feng, Zhiqiang Cai, Scott Crossley, and Danielle S McNamara. 2011.  Simulating human ratings on word concreteness. In _Twenty-Fourth International FLAIRS Conference_. AAAI, San Francisco.
  * Frich et al. (2019) Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, Michael Mose Biskjaer, and Peter Dalsgaard. 2019.  Mapping the Landscape of Creativity Support Tools in HCI. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–18.  https://doi.org/10.1145/3290605.3300619
  * Haviv et al. (2021) Adi Haviv, Jonathan Berant, and Amir Globerson. 2021\.  BERTese: Learning to speak to BERT.  arXiv preprint arXiv:2103.05327.
  * Hullman et al. (2018) Jessica Hullman, Yea-Seul Kim, Francis Nguyen, Lauren Speers, and Maneesh Agrawala. 2018.  Improving Comprehension of Measurements Using Concrete Re-Expression Strategies. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ (Montreal QC, Canada) _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–12.  https://doi.org/10.1145/3173574.3173608
  * Jiang et al. (2020) Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020\.  How can we know what language models know?  _Transactions of the Association for Computational Linguistics_ 8 (2020), 423–438.
  * Kang et al. (2021) Youwen Kang, Zhida Sun, Sitong Wang, Zeyu Huang, Ziming Wu, and Xiaojuan Ma. 2021\.  MetaMap: Supporting Visual Metaphor Ideation through Multi-Dimensional Example-Based Exploration. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (Yokohama, Japan) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 427, 15 pages.  https://doi.org/10.1145/3411764.3445325
  * Karolus et al. (2023) Jakob Karolus, Sebastian S. Feger, Albrecht Schmidt, and Paweł W. Woźniak. 2023\.  Your Text Is Hard to Read: Facilitating Readability Awareness to Support Writing Proficiency in Text Production. In _Proceedings of the 2023 ACM Designing Interactive Systems Conference_ (Pittsburgh, PA, USA) _(DIS ’23)_. Association for Computing Machinery, New York, NY, USA, 147–160.  https://doi.org/10.1145/3563657.3596052
  * Kim et al. (2023) Jeongyeon Kim, Sangho Suh, Lydia B Chilton, and Haijun Xia. 2023\.  Metaphorian: Leveraging Large Language Models to Support Extended Metaphor Creation for Science Writing. In _Proceedings of the 2023 ACM Designing Interactive Systems Conference_ (Pittsburgh, PA, USA) _(DIS ’23)_. Association for Computing Machinery, New York, NY, USA, 115–135.  https://doi.org/10.1145/3563657.3595996
  * Kim et al. (2022) Kevin Gonyop Kim, Richard Lee Davis, Alessia Eletta Coppi, Alberto Cattaneo, and Pierre Dillenbourg. 2022.  Mixplorer: Scaffolding Design Space Exploration through Genetic Recombination of Multiple Peoples’ Designs to Support Novices’ Creativity. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 308, 13 pages.  https://doi.org/10.1145/3491102.3501854
  * Kim et al. (2016) Yea-Seul Kim, Jessica Hullman, and Maneesh Agrawala. 2016\.  Generating Personalized Spatial Analogies for Distances and Areas. In _Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems_ (San Jose, California, USA) _(CHI ’16)_. Association for Computing Machinery, New York, NY, USA, 38–48.  https://doi.org/10.1145/2858036.2858440
  * Kjelvik and Schultheis (2019) Melissa K Kjelvik and Elizabeth H Schultheis. 2019.  Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy.  _CBE—Life Sciences Education_ 18, 2 (2019), es2.
  * Krauß et al. (2022) Veronika Krauß, Michael Nebeling, Florian Jasche, and Alexander Boden. 2022.  Elements of XR Prototyping: Characterizing the Role and Use of Prototypes in Augmented and Virtual Reality Design. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 310, 18 pages.  https://doi.org/10.1145/3491102.3517714
  * Kwankajornkiet et al. (2016) Chonlathorn Kwankajornkiet, Atiwong Suchato, and Proadpran Punyabukkana. 2016.  Automatic multiple-choice question generation from Thai text. In _2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE)_. IEEE, Khon Kaen, Thailand, 1–6.  https://doi.org/10.1109/JCSSE.2016.7748891
  * Lazar et al. (2010) Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser. 2010.  _Research Methods in Human-Computer Interaction_.  Wiley Publishing, Hoboken, NJ.
  * Liu et al. (2023) Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. 2023\.  A First Look at LLM-Powered Generative News Recommendation.  arXiv preprint arXiv:2305.06566.
  * Liu et al. (2022) Vivian Liu, Han Qiao, and Lydia Chilton. 2022.  Opal: Multimodal Image Generation for News Illustration. In _Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology_ (Bend, OR, USA) _(UIST ’22)_. Association for Computing Machinery, New York, NY, USA, Article 73, 17 pages.  https://doi.org/10.1145/3526113.3545621
  * Luccioni et al. (2023) Alexandra Sasha Luccioni, Christopher Akiki, Margaret Mitchell, and Yacine Jernite. 2023.  Stable Bias: Analyzing Societal Representations in Diffusion Models.  arXiv:2303.11408 [cs.CY]
  * Ma et al. (2023) Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang. 2023.  Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System.  arXiv preprint arXiv:2304.00477.
  * Mozaffari et al. (2022) Mohammad Amin Mozaffari, Xinyuan Zhang, Jinghui Cheng, and Jin L.C. Guo. 2022.  GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 537, 15 pages.  https://doi.org/10.1145/3491102.3517511
  * Muñiz-Rodríguez et al. (2020) Laura Muñiz-Rodríguez, Luis J Rodríguez-Muñiz, and Ángel Alsina. 2020.  Deficits in the statistical and probabilistic literacy of citizens: Effects in a world in crisis.  _Mathematics_ 8, 11 (2020), 1872.
  * Perkins (2010) Jacob Perkins. 2010\.  _Python text processing with NLTK 2.0 cookbook_.  PACKT publishing, Birmingham.
  * Petridis et al. (2021) Savvas Petridis, Hijung Valentina Shin, and Lydia B Chilton. 2021.  SymbolFinder: Brainstorming diverse symbols using local semantic networks. In _The 34th Annual ACM Symposium on User Interface Software and Technology_. ACM, New York, NY, USA, 385–399.
  * Phillips and McQuarrie (2004) Barbara J Phillips and Edward F McQuarrie. 2004.  Beyond visual metaphor: A new typology of visual rhetoric in advertising.  _Marketing theory_ 4, 1-2 (2004), 113–136.
  * Portenoy et al. (2022) Jason Portenoy, Marissa Radensky, Jevin D West, Eric Horvitz, Daniel S Weld, and Tom Hope. 2022\.  Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 309, 13 pages.  https://doi.org/10.1145/3491102.3501905
  * Reed et al. (2023) Courtney N. Reed, Paul Strohmeier, and Andrew P. McPherson. 2023\.  Negotiating Experience and Communicating Information Through Abstract Metaphor. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 185, 16 pages.  https://doi.org/10.1145/3544548.3580700
  * Riederer et al. (2018) Christopher Riederer, Jake M. Hofman, and Daniel G. Goldstein. 2018.  To Put That in Perspective: Generating Analogies That Make Numbers Easier to Understand. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ (Montreal QC, Canada) _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–10.  https://doi.org/10.1145/3173574.3174122
  * Rombach et al. (2022) Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022\.  High-Resolution Image Synthesis With Latent Diffusion Models. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_. CVPR, New Orleans, Louisiana, 10684–10695.
  * Sawyer (2011) R Keith Sawyer. 2011\.  _Explaining creativity: The science of human innovation_.  Oxford university press, New York, NY, USA.
  * Schramowski et al. (2023) Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting. 2023.  Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_. CVPR, Seattle, 22522–22531.
  * Shi et al. (2022) Yang Shi, Pei Liu, Siji Chen, Mengdi Sun, and Nan Cao. 2022.  Supporting expressive and faithful pictorial visualization design with visual style transfer.  _IEEE Transactions on Visualization and Computer Graphics_ 29, 1 (2022), 236–246.
  * Speer et al. (2017) Robyn Speer, Joshua Chin, and Catherine Havasi. 2017.  ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. In _Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence_ _(AAAI’17)_. AAAI Press, San Francisco, California, USA, 4444–4451.
  * Suh and An (2022) Sangho Suh and Pengcheng An. 2022.  Leveraging Generative Conversational AI to Develop a Creative Learning Environment for Computational Thinking. In _27th International Conference on Intelligent User Interfaces_ (Helsinki, Finland) _(IUI ’22 Companion)_. Association for Computing Machinery, New York, NY, USA, 73–76.  https://doi.org/10.1145/3490100.3516473
  * Sun et al. (2022) Zhida Sun, Sitong Wang, Chengzhong Liu, and Xiaojuan Ma. 2022\.  Metaphoraction: Support Gesture-Based Interaction Design with Metaphorical Meanings.  _ACM Trans. Comput.-Hum. Interact._ 29, 5, Article 45 (oct 2022), 33 pages.  https://doi.org/10.1145/3511892
  * Van Heuven et al. (2014) Walter JB Van Heuven, Pawel Mandera, Emmanuel Keuleers, and Marc Brysbaert. 2014.  SUBTLEX-UK: A new and improved word frequency database for British English.  _Quarterly journal of experimental psychology_ 67, 6 (2014), 1176–1190.
  * Wan and Lu (2023) Qian Wan and Zhicong Lu. 2023.  GANCollage: A GAN-Driven Digital Mood Board to Facilitate Ideation in Creativity Support. In _Proceedings of the 2023 ACM Designing Interactive Systems Conference_ (Pittsburgh, PA, USA) _(DIS ’23)_. Association for Computing Machinery, New York, NY, USA, 136–146.  https://doi.org/10.1145/3563657.3596072
  * Wang et al. (2010) Hao-Chuan Wang, Dan Cosley, and Susan R. Fussell. 2010\.  Idea Expander: Supporting Group Brainstorming with Conversationally Triggered Visual Thinking Stimuli. In _Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work_ (Savannah, Georgia, USA) _(CSCW ’10)_. Association for Computing Machinery, New York, NY, USA, 103–106.  https://doi.org/10.1145/1718918.1718938
  * Wang et al. (2023) Yunlong Wang, Shuyuan Shen, and Brian Y Lim. 2023.  RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 22, 29 pages.  https://doi.org/10.1145/3544548.3581402
  * Wei et al. (2022) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022\.  Chain-of-thought prompting elicits reasoning in large language models.  _Advances in Neural Information Processing Systems_ 35 (2022), 24824–24837.
  * Wei-ning et al. (2006) Wang Wei-ning, Yu Ying-lin, and Jiang Sheng-ming. 2006\.  Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction. In _2006 IEEE International Conference on Systems, Man and Cybernetics_ , Vol. 4. IEEE, Taipei, Taiwan, 3534–3539.  https://doi.org/10.1109/ICSMC.2006.384667
  * Xiao et al. (2023) Shishi Xiao, Suizi Huang, Yue Lin, Yilin Ye, and Wei Zeng. 2023.  Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model.  arXiv:2304.14630 [cs.AI]
  * Yang et al. (2023) Leni Yang, Aoyu Wu, Wai Tong, Xian Xu, Zheng Wei, and Huamin Qu. 2023.  Understanding 3D Data Videos: From Screens to Virtual Reality. In _2023 IEEE 16th Pacific Visualization Symposium (PacificVis)_. IEEE, Seoul, Korea, 197–206.
  * Zhang et al. (2022) Chao Zhang, Cheng Yao, Jiayi Wu, Weijia Lin, Lijuan Liu, Ge Yan, and Fangtian Ying. 2022.  StoryDrawer: A Child–AI Collaborative Drawing System to Support Children’s Creative Visual Storytelling. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 311, 15 pages.  https://doi.org/10.1145/3491102.3501914
  * Zhou et al. (2023) Feng Zhou, Steven D Benford, Sarah Whatley, Kate Marsh, Ian Ashcroft, Tanja Erhart, Welly O’Brien, and Paul Tennent. 2023\.  Beyond Skin Deep: Generative Co-Design for Aesthetic Prosthetics. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 128, 19 pages.  https://doi.org/10.1145/3544548.3580803