JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

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Getting Insights from Twitter:
What People Want to Touch in Daily Life
Yusuke Ujitoko, Member, IEEE, Yuki Ban, Member, IEEE, and Takumi Yokosaka
Abstract—Understanding what people want to touch in daily life has been one of the central topics in the ﬁelds of haptic science,engineering, and marketing. Several studies have addressed the topic, however, their ﬁndings were highly dependent on theexperimental stimuli in the laboratory environment. In this study, we tried to gain insights into targets that people want to touch in daily
life by conducting a Twitter survey. We collected a considerable amount of Japanese Twitter posts containing references to the desire fortouch. To capture the motivation underlying these desires in relation to haptic properties, we used several queries that comprehensively
covered exploratory procedures. The results showed that targets that people wanted to touch depended on the exploratory proceduresin the queries used (e.g., “want to stroke” tended to target people and animals). We also found that these targets were desirable totouch not only for their haptic properties but also as a means of communicating with other people or living animals. Our ﬁndings would
be important for understanding human haptic function in the real world and for developing consumer haptic displays and applications.
Index Terms—Haptics, Desire for Touch, Social Media, Twitter, Exploratory ProcedureF

1

I NTRODUCTIONI

N daily life, people touch many kinds of targets around•

Yusuke Ujitoko is with the NTT Communication Science Laboratories,
Nippon Telegraph and Telephone Corporation, Atsugi, Japan. E-mail:yusuke.ujitoko.ws@hco.ntt.co.jp
Yuki Ban is with the Graduate School of Frontier Sciences, the Universityof Tokyo, Chiba, Japan. E-mail: ban@edu.k.u-tokyo.ac.jp
Takumi Yokosaka is with the NTT Communication Science Laboratories,
Nippon Telegraph and Telephone Corporation, Atsugi, Japan. E-mail:yokosaka.takumi@gmail.com

them. For example, people touch target surfaces to assess
their texture, make physical contact with familiar people oranimals for communication, or feel the condition of targets
through tools such as pliers. Among these various targets,there are those that people want to touch and those they donot. Understanding what people want to touch in daily life
is an important issue from the perspective of understanding
human behavior. Also, recognizing the desire for touch isuseful for the future development of consumer applications
using a haptic display.
Earlier studies have investigated what kind of look andfeel of targets motivated people to want to touch them [1],[2], [3], [4]. From the viewpoint of haptic saliency, Metzger et
al. [5] investigated what people preferentially touch within
an object surface and Lau et al. [6] investigated where
on a 3D object people would touch it. Although thesestudies successfully identiﬁed the shapes or textures thatinduce participants to touch, it still remains unknown towhat extent people want to touch such experimentally wellcontrolled stimuli when they do not have the experimenter’s
guidance in everyday life. For example, people sometimes
want to touch other’s body parts such as hands, heads, orshoulders that have largely different shapes and surface
properties depending on the communication situation. As
another example, they sometimes want to hit/tap a speciﬁc
product like a drum or a keyboard to get the tactile feedback••

Manuscript received April 17, 2021; revised July 31, 2021.

that largely depends on the material or shape design of
those targets. Therefore, in order to understand the desire
for touch in daily life, it is necessary to investigate it in
the real world. However, conducting a survey of people
reporting the desire to touch in daily life is laborious andtime-consuming, and is not suitable for collecting a goodrange of data.
This study focused on a Twitter survey for an investigation in Japan. In recent years, surveys using social media
have been receiving increased attention for the light they
shed on people’s daily activities and moods [7], [8], [9], [10].
Among social media, Twitter [11] has the largest numberof users in Japan. Investigation through Twitter has bothadvantages and disadvantages. Twitter provides a public
API and we can obtain a considerable quantity of usergenerated tweets. In contrast to the self-reporting in ofﬂine
experiments, posts on Twitter are not prompted by experimenters and thus there are less experimenter-demanded
effects on the posts. On the other hand, there could be noisy
text on Twitter since text can be posted by anyone or even by
automated posting software. Thus, in this paper, we aim to
get insights into what people want to touch by collecting
and analyzing data collected via Twitter, while carefully
handling noise within the data.
When getting insights into what people want to touch,an understanding of the reason for the desire to touch is also
important. From a series of psychophysical experiments [1],[2], [3], [4], it is suggested that the desire for touch has aclose relationship with speciﬁc haptic properties such assmoothness of texture [3]. The identiﬁcation of the role ofhaptic property in the desire would give us a clue as to
the reason for the desire. Having said that, it is difﬁcult to
identify the haptic properties that characterize a target ina tweet and how they attracted the attention of the user.
For example, if the word “box” is extracted as a target ofdesire, it is hard to estimate what properties – such as global
shape or surface texture – are the main factors attracting

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

the desire for touch. Here, exploratory procedures seemto be useful for alleviating the problem since it is known
that humans use corresponding exploratory procedures tojudge a speciﬁc haptic property [12], [13]. For example, it
is known that people push targets to judge their physicalcompliance. Based on this knowledge, the target that people
want to push might have some characteristics in respect of
its softness. In other words, based on the desire related
to a particular exploratory procedure, we can speculate
and discuss the haptic property that motivates people totouch. To aggregate the desire to touch some target via
a speciﬁc exploratory procedure, we conﬁgure the queries
for the Twitter API based on the categorization of the
exploratory procedure. We clarify which target is desiredfor each exploratory procedure in real-world situations bycomparing the distribution of target words extracted byeach query. Also, we clarify the desire that is fulﬁlled by atarget or particular haptic property by comparing the targetwords for desire and those for action for each exploratory
procedure.
The present study is the ﬁrst to investigate which targets
in the real world attract the desire for touch using Twitter.
Even though our data might be biased due to factors suchas the use of speciﬁc social media or skewed populationsin Twitter users in Japan, we believe that our results arevaluable for haptic science researchers and for developersof haptic displays or applications. For haptic science researchers, our results can contribute to clarifying how thebrain processes the desire for touch. For example, by comparing our results with the ﬁndings obtained in the former
laboratory experiments, researchers can gain an insight into
what missing factors in the laboratory experiment, such as
the multi-sensory information that real-world targets have,affect desire for touch. As a more practical example, researchers can prepare real-world stimuli encouraging desire
for touch to examine the brain function related to the desire.
For developers of haptic displays or applications, ourresults can contribute to designing a haptic display realizing
social touch or to deﬁning appealing targets for haptic entertainment applications. For example, developers can gain
insights on the functional requirements of a haptic display
realizing the touch experience for speciﬁc targets such as
cats or dogs. If the desire for touch is not conﬁrmed withspeciﬁc exploratory procedures by our analysis, it can beseen that such touch gesture is not required to be supported
to satisfy the desire to touch the targets. As another example,
developers can see what kind of target in applications
appeals most to users with a display that supports a speciﬁc
property or touch gesture.

2

R ELATED W ORKFirst, we introduce previous studies conducting content
analysis using Twitter. Then, we introduce the relevant
knowledge on exploratory procedures. Lastly, since ourinvestigation has a close relationship with affective anddiscriminative aspects of the desire for touch, we introduce
earlier investigations into the desire for touch from theperspective of these two aspects of touch.

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2.1 Content Analysis using Twitter
Content analysis is an approach to empirical research basedon pre-existing content. On Twitter, which is a microblogging tool that is growing in popularity worldwide, there
exists a large quantity of tweets in Japanese posted bymore than 50.9 million active users of Twitter [14] in Japan.
Recently, Twitter surveys have been widely used for a broad
range of objectives from stock prediction using sentiment
analysis [15] to estimation of drug consumption [16].
As a content analysis technique, it is known that wordfrequency lists provide an overview of the words or phrases
that occur in an analyzed text a certain number of times to
identify the interest of the text. In addition, triangulation of
the result with different queries [9], different analyses [17],and qualitative reviews of the sampled text [18] will help
to strengthen the validity of the analysis. Following thesemethods to improve the reliability of the results, we usedcomprehensive queries in relation to the exploratory procedures to collect data, qualitatively review the text, andtriangulate the results with multiple analyses. Also, since
the raw tweets are unstructured and noisy [19], we carefullyclean them before analysis.
2.2 Exploratory Procedures
The relationship between explorative hand/ﬁnger movement and the haptic properties of targets is systematicallydescribed in the form of exploratory procedures [12], [13].
Exploratory procedures are stereotypical patterns of manual
exploration observed when a human is asked to makejudgements about the haptic properties of a particular target. Each exploratory procedure during free exploration was
found to be optimal for accurately identifying the corresponding haptic properties. For example, it was reported
that “static contact”, which is when a skin surface is placed
against a target without moving, was usually performed forjudgment of the warmth or coolness of a target and this isbecause it is suitable for the judgment of temperature. Other
exploratory procedures include “pressure” (related to softness/hardness), “unsupported holding” (weight), “enclosure” (volume, shape), “lateral motion” (texture), and “contour following” (contour). In this study, multiple queriescorresponding to the exploratory procedures were used totriangulate the results.
2.3 Affective and Discriminative Aspects of Desire for
Touch
Touch has been described as having two different aspects:
discriminative and affective [20]. The discriminative aspectof touch helps us to discriminate the haptic property of atarget. Aβ afferents, which are myelinated and thick, convey
the discriminative aspect of touch to the brain. In addition to
the discriminative aspect, touch was also proven to provide
affective inputs to the human brain, which are key foremotion-related communication and social interactions. CT
afferents, which are unmyelinated and thin, contribute to
the affective aspects of touch [21]. For a comprehensive
understanding of the desire for touch, it is important toanalyze the desire in relation to these two aspects.
These two aspects of touch are receiving attention not
only within haptic science but also in the ﬁeld of marketing.

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Peck and Childers [22] have created the “Need for Touch”
scale, which is a scale that represents individual preferencesof the need for touch. Their scale is composed of two
subscales: the instrumental scale and the autotelic scale.
Instrumental NFT corresponds to the discriminative aspects
of desire for touch, originating from goal-directed motives
to play a role in making the judgment. Meanwhile, autotelic
NFT corresponds to the affective aspect of desire for touch
which corresponds to the hedonic feeling induced by touching a product. Since it was reported that the participants
having higher instrumental NFT, rather than autotelic NFT,
tended to rate targets as more inviting and pleasant infeeling [4], instrumental and autotelic aspects might havedifferent roles in the desire for touch. Thus, discussing our
results in terms of discriminative (instrumental) touch andaffective (autotelic) touch would be useful for understanding touch motivation in the realms of haptic science andhaptic marketing.

3

C OLLECTION AND P REPROCESSING

collect

queriesquery-wise
tweet texts

query-wisetargetrget wordsorresultFig. 1. The ﬂow of investigating what people want to touch comprises
collection, preprocessing, and analysis stages.

3.1

number of collected tweets
per query
exploratory
procedure

hapticpropertybase verb(in Japanese)

generalgeneralstatic contact
lateral motion

Collection

Tweets over 150 days (From September 12, 2020, to February
9, 2021), were collected using the Twitter API by feeding
queries. To avoid collecting duplicate tweets, an additionalparameter of the API was conﬁgured to exclude retweets(relays of tweets by another Twitter user) or replies to otherusers.
To collect tweets indicating the desire to touch related
to each exploratory procedure, we deﬁned base verbs
that comprehensively covered the exploratory procedures(shown in Table 1). In the leftmost column of Table 1, theexploratory procedures are described. Corresponding haptic
properties and base verbs are described in the next twocolumns. In total, we prepared 11 base verbs. Though theoriginal exploratory procedures [13] did not include a verb
corresponding to “tap” or “hit”, it is known that humans
recognize the hardness of a target by tapping or hitting thetarget surface and observing the damping natural vibration
or reactive impulsive force [23], [24], [25]. Thus, we adopted“tap/hit (tataku in Japanese)” as an additional base verb
capturing hardness. We also adopted the more general baseverb “touch (sawaru in Japanese)”, that can be considered tobe less dependent on speciﬁc haptic properties. Comparing

action queries

・want to ~(~tai)

desire queries

・past tense(~shita)touch(sawaru)

95137

174038temperature

statically contact(fureru)

33335

147128texturestroke(naderu)

22134

13125

lateral motion

texturerub(kosuru)

358

3192

unsupported
holdingweightlift(mochiageru)

1533

12788

unsupported
holdingweightsupport(sasaeru)

15480

36017

enclosureglobal shape,
volumegrasp(tsukamu)

12194

74690

enclosureglobal shapevolume

cover
(oou)

574

10943

pressuresoftness

push
(osu)

12892

175735

pressure

hardness

hit/tap(tataku)

4011

52664

global shape,
exact shapetrace(nazoru)

2283

7165

contourfollowing

analyzetwitter serverraw
w datapreprocess

The ﬂow of our investigation consisted of collection, preprocessing, and analysis stages as shown in Fig. 1. The
collection and preprocessing stages are described in thissection. In the next section 4, the analysis stage is described.

TABLE 1The base verbs corresponding to exploratory procedures. The queries
were combinations of a base verb with “want to” or the past tense of abase verb. The number of extracted tweets per query is given in eachcell.

target words between “touch (sawaru in Japanese)” and theother base verbs would be useful for determining characteristic target words for each base verb.
We deﬁned the queries by combining the base verbs
with the additional verb “want to” with which we aimed
at directly capturing the desire for touch. Hereinafter we
simply call these queries “desire queries”. Though we attempted to use other queries combining the base verbs withadditional verbs such as “like to” or “feel good to” withwhich we aimed at indirectly capturing the desire to touch,the extracted number of tweets was too small to analyze
and we did not use the texts extracted by those queries.
Also, we used queries using the past tense of the base verb
(e.g., “touched”) to capture the touch action. Hereinafter we
simply call these queries “action queries”.
In total, we prepared 22 queries (=11 base verbs × 2 (desire and action queries)). The numbers of tweets extracted
per query are shown in the cells in Table 1. The numberof tweets for desire queries corresponding to “want to rub”
and “want to cover” were lower than 1500, which equatedto 10 occurrences per day and the sample was too small
to analyze. Thus, we did not use the tweets extracted byqueries using the base verbs “rub” or “cover” for further
analysis.
3.2

Preprocessing

The ﬂow of the preprocessing stage is shown in Fig. 2.
3.2.1 Tweet exclusion
To avoid the double count of tweets posted by a bot (a bot
is a type of software that controls a Twitter account via the

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015
ququery-wisetwtweet texts

preprocess
tweet exclusion
query-wise tweet texts

phrase exclusion (e.g. hash tag, emoji)query-wise tweet texts

target word extraction
query-wise target words with tweet texts

noisy target word exclusion
query-wise target words with tweet texts

review of noise ratio on texts andexclusion of query with high noise ratio
query-wise target words with tweet texts

target word categorizationququery-wisetarget words
with category labelswiFig. 2. Flow of the preprocessing stage.

Twitter API. The bot software can autonomously perform
actions such as tweeting and retweeting) or by users who
tweet the same thing repeatedly, we excluded tweets withthe exact same content. To exclude ads and spam, tweetsthat contained URLs were excluded.
3.2.2 Phrase exclusion
Further, a per-phrase within tweet exclusion process was
employed. Non-Japanese phrases, hashtags (phrases writtenwith a # symbol used to index keywords or topics on
Twitter), emojis, and emoticons were excluded.
3.2.3 Target word extraction
After these processes were executed, target word extraction
was performed. This process depended on the NLP for
Japanese sentences. The Japanese language is a language
without word boundaries, and therefore, a morphologicalanalysis was performed using Juman++ [26]. To ﬁnd thewords that were dependent on the base verbs, a dependent
structure analysis was performed using the Kurohashi–
Nagao parser (KNP) [27]. A case structure analysis was thenperformed on the identiﬁed words to determine the target
word. Finally, to conﬁrm that the target was a noun, we
conﬁrmed whether the word exists in the word2vec [28]model provided by chiVe [29].
At this phase, the number of target words for the query“want to lift” was 56 and we decided not to use the baseverb “lift” since it was difﬁcult to analyze such a small
number of target words.
3.2.4 noisy target word exclusion
We conducted qualitative reviews [18] of the target words
and texts, and we noticed that there were noisy texts present
that did not show the desire to physically touch. For example, the tweets extracted by a query of “want to support”

4

included many texts that referred to the intention to help a
speciﬁc person.
To exclude such noisy texts, it would be best if automated exclusion to check texts were possible, but it was
difﬁcult to implement a system that could accurately judge
texts as noise or not automatically. In contrast, manual
inspection of all of the tweets would be accurate but too
time-consuming to apply. Thus, we adopted a third option;
we conﬁgured a noisy target word list per query that would
be excluded and excluded the tweets that contained suchnoisy target words. Note that if a tweet containing a target
word could not always be determined to be noisy, the target
word was not added to the noisy target word list. In otherwords, the target word in some cases could not be excluded
as noise. For example, “want to hit the keyboard” can mean
both “want to play the keyboard” and “want to feel the
feedback when hitting the keyboard” and it is difﬁcult todiscriminate. In such a case, we did not exclude the target
word. After deﬁning the noisy target word list, we excluded
tweets with target words that were included in the list.
3.2.5 review of noise ratio on texts and exclusion of querywith high noise ratio
After noisy target word exclusion, we reviewed the texts
to test how many noisy texts that did not represent thedesire for physical touch were included in the dataset. We
randomly sampled 500 tweets per query and counted thenoisy text ratio among them. As a result, the noise ratio
for the query related to the base verb “support” was toohigh (e.g., 78 % for “want to support”) and that ratio for
the queries corresponding to other exploratory procedures
were less than 1.6 % at most. Thus, we decided to use all
queries except for ones whose base verb was “support”. In
total, the target words extracted by 14 queries (=7 base verbs
× 2 (desire and action queries)) were used for analysis.
After all of these steps were completed, the target word
was automatically translated into English using the Google
Translate API. If the word was not appropriate for academic
reporting, we reworded it into a more suitable one.

4

A NALYSIS R ESULTS AND D ISCUSSION

4.1 Analysis on Desire at Target Level
We ﬁrst visualized the distribution of occurrence probabilityof target words for each desire query in Fig. 3. The ﬁgureshows how each different query captured the different
target words. Here, to make it clearer which target words
characterize each desire query, we have shown the top 10
target words extracted by each desire query in descendingorder of occurrence probability in Table 2. The target words
in Table 2 seem to be dependent on the desire query. For
example, “button” was the most frequently tweeted target
word among target words extracted by the desire query“want to push”, but “button” was not frequently extracted
by the other desire queries. We would like to conﬁrm
how signiﬁcant each target word was for each exploratory
procedure, but the challenge was how to deﬁne the control
condition. To statistically evaluate the signiﬁcance of thewords for each desire query, we calculated the differences
in a word’s occurrence probability between cases when theword was extracted by the focused desire query and cases

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

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TABLE 2
Target words in descending order of occurrence probability for each desire query. Blue target words denote that the words’ occurrence
probabilities in each EP (Exploratory Procedure) desire query were larger than those in the general desire query “want to touch”, which suggeststhat the target words characterize the EP desire query. Red target words denote that the words’ occurrence probabilities in each EP desire query
were smaller than those in the general desire query “want to touch”, which suggests that the target words characterize the general desire query.
target word in descending order of occurrence probability for each desire query

desire query

1

2

3

4

5

6

7

8

9

10

want to touchbreasthairbuttock

cat

abdomen cheek

dog

body

ear

hand

want to statically contact

youpeople

skin

cat

hand

warmthanimalhuman skinobjectlip

want to stroke

head

cat

dog

abdomen buttock

hair

me

back

child

cheek

want to pushbutton

stamp

cart

abdomen card

whorl

key

everyone

mole

chair

want to hit/tap

drum

buttock

keyboard

head

hand

shouldersomething

iron

them

cheek

want to grasp

waist

hand

buttock

tail

arm

hair

ankle

breasttongue leg

want to trace

line

abdominal eyebrow
musclemusclebackclavicle

blood vessel

crack

tooth

ditch

trace

Fig. 3. Distribution of occurrence probability for each desire query for target words ranked in the top 10 for either desire query. This shows how the
distribution differs depending on the desire query.

when the word was extracted by the general query “want totouch”. The comparison with the occurrence probabilities oftarget words in the general query, which is considered to beless sensitive to speciﬁc haptic properties, will allow us todetermine the characteristic words for the focused queries.
We calculated 10000 bootstrap samples [30] of the differences. If the Bonferroni-corrected 95% conﬁdence interval(CI) of the difference did not overlap to zero, we could
conclude that the difference was statistically signiﬁcant. In
Table 2, target words the CI of which was greater thanzero are colored in blue, which indicates the target word
occurred more frequently in the case of a focused desire
query than in the case of the “want to touch” query. In
contrast, target words the CI of which was less than zeroare colored in red, which indicates the target word occurred
less frequently in the case of a focused desire query than in
the case of the “want to touch” query. The statistical analysis
supported the observation above that each EP desire query
has distinctive target words, i.e., there are a lot of tweet
texts such as “want to statically contact you”, “want tostroke head”, “want to push button”, “want to hit (i.e.,beat) drum”, “want to grasp waist”, and “want to traceline”. Moreover, even between base verbs of “hit/tap” and“push”, both of which belong to the same exploratory procedure for capturing hardness/softness, the popular targets
obtained were different. This result does not support theassumption that people want to acquire different properties(e.g., softness or shape) of the limited popular target (e.g.
hand) via the different exploratory procedures. On the other
hand, some words triggered desires across some different
desire queries. For example, “cat” was extracted as one of
the top words by the desire queries whose base verbs were
“touch”, “statically contact”, and “stroke”. This means thatpeople want to conﬁrm at least both corresponding haptic
properties, i.e., the temperature and the texture, of the cat.
Fig. 4 shows the occurrence probabilities for the top 10
target words for each desire query. It indicates that a small
number of speciﬁc target targets form the majority of the desire, but the extent of the concentration of desire depended
on the queries. For example, the occurrence probability ofthe top target word in the case of queries of “want to push”
and “want to hit/tap” were more than 60 % of all desires.
It is interesting that both “want to push” and “want tohit/tap” are the exploratory procedures for conﬁrming the

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 20156softness/hardness of a target. In daily life, there may be alimited number of cases where the action to conﬁrm thesoftness/hardness is performed.

strokehead

headstaticallycontact

trace

touch
breast

grasp

arm

arm

abdominal
muscle

arm

arm

back
abdomen

waist

hit/tap

waist
buttock

trace

1

2
3
4
5
6
7
8
9
10
target word in descending order of occurrence probability for each queryFig. 4. Occurrence probability for target word in descending order.

Extracted target words include those related to bodyparts. Which body part people want to touch depends onthe desire queries as can be seen in Table 2 and Fig. 3.
For example, the waist is desired to be touched mainly via
grasping rather than via other exploratory procedures. We
visualized the geometric relationships between base verbs
based on the body parts by mapping them into a wholebody illustration (Fig. 5). Note that, though the target words
about body parts included not only the human body but
also other living things such as animals, we have selected
the human body as an example. In this ﬁgure, each baseverb was mapped to body parts where the rank of theoccurrence probability in the desire query using the baseverb was higher than that in the “want to touch” query.
For example, we mapped “stroke” to head since the rankof the occurrence probability of head was higher in thedesire query “want to stroke” than the rank in the desire
query “want to touch”. In addition, we mapped “touch”
when the rank of the occurrence probability of the bodypart was higher than that for other queries. Moreover, since“breast” and “ear” were among the top 10 in the desire
query “want to touch” but were not in other desire queries,
we mapped those body parts as desired targets of touch.
These results might indicate the relation-speciﬁc aspect of
the exploratory procedure. An earlier study showed thatthe hands and arms were relatively touchable for people
who are not close to the owner while the breast, abdomen,
and buttock were touchable only for people who are very
close to the owner [31]. Thus, touching (for breast andabdomen) and hitting (for buttock) might be behaviors
reﬂecting intimacy with the owner of the body parts (as longas the behaviors are performed for body parts). It would be
one interesting direction to investigate the relation-speciﬁc
exploratory procedure for body parts.
4.2

Analysis on Desire at Target Category Level

To conduct the analysis not only in terms of individual
target words but also in terms of the target category, wecategorized each target word under our deﬁned categorization.
Initially, we conducted a hierarchical clustering of thetarget words and attempted to use the resulting cluster asa categorization. We converted the word to vector using

hand

hand

hand

hand

touch
statically contact
strokepush
hit/tap
grasp
trace

Fig. 5. Mapping of base verb of desire query to human body parts.

word2vec model [28] and used the vectors as the input
information of a clustering algorithm. We conducted hierarchical clustering using Ward [32]’s method with squared
Euclidean distance as the distance measurement. The resultof the hierarchical clustering for targets extracted by eachquery is shown in Supplemental materials. Here, we foundthe clustering results seem to be less clear and intuitive.
(For example, “warmth” and “human skin” were classiﬁed
into the same cluster). This could be partly attributed to theword2vec models. The word2vec model assumes that words
occurring in the same contexts tend to have similar meanings. The fact that human skin and warmth often appear
in the same context suggests that warmth often appears inthe sense of temperature of human skin. However, warmthdoes not always mean human skin. As with this example,
clustering based on co-occurrence relationships seems to fall
into a less-accurate classiﬁcation.
Instead of using the result as is for categorization, we
referred to the result and manually deﬁned the following
six categories: (1) body part, (2) animal, (3) object, (4) person,(5) geometry, and (6) temperature. The example words areshown in Table 3. We framed the target words into thesesix categories and used them for further analysis. Since wedeﬁned the category bottom-up fashion, these categories are
not mutually exclusive and exhaustive. To make clear the
dependency between categories, we tried to divide theminto affective and discriminative touch [20]. The results areillustrated in Fig. 6. Since touch in relation to a “person”,“animal”, or “body part” would be related to communication, we classiﬁed touch in these categories under affective
touch. Since touch in relation to an “object” or “geometry”
would be related to the judgment of haptic feeling or tool
manipulation, we classiﬁed touch in these categories under
discriminative touch. “Temperature” would be related toboth judgment of a target’s temperature property and bodytemperature felt by physical contact, and thus, we judgedthat touch in respect of “temperature” has both aspects ofaffective and discriminative touch.
We computed and visualized the desire occurrence prob-

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7•

TABLE 3
Example words belonging to each category.
categories

example words

body part

hand, hair, buttock, abdomen, ear, tongue, arm, ankle

animalcat, dog, animal, bird, tiger, sheep, carp, cow, hamster

objectbutton, keyboard, stamp, card, stroller, stone, iron, clock

personyou, people, them, child, female, character, daughter

geometryline, ditch, crack, border, edge, region, shape, surface

temperature

warmth, heat, temperature, hot air, body temperature

person body part

object•geometry

temperature••when people want to feel temperature by static contact, they want to feel the temperature of someone orsomething;when people want to feel texture by stroking, theywant to feel the texture of an animal or person;
when people want to feel hardness by pushing, hitting, or tapping, they want to feel the hardness of atarget;To feel the contour of some geometry, people want totrace its line/contour.

Of course, there are cases where we cannot speculate thereason for touch based only on the exploratory procedures
related to haptic properties. For example, when people wantto grasp a hand, it is not always for the recognition of thehand’s shape, it may be for communication with someone.

animalaffective touch

discrminative touch

Fig. 6. Relationship between categories.

TABLE 4
Target categories characterizing each desire query. A checkmark(✓)
denotes that the occurrence probability of the category for the query
was signiﬁcantly higher than the probability of the target word in the“want to touch” query.
category
affective or
body animal object person geometry
temperature discriminative
part
touch?

ability of each category as shown in Fig. 7. The results
suggest that the desire queries were different not only ontarget word levels (Fig. 3) but also on category levels.
querywant to staticallycontactaffective

want to stroke

affective

want to pushdiscriminative

want to hit/tap

discriminative

want to grasp

affective

want to trace

discriminative■Note:

affective
touch

discrminativetouchsocial touch/ affective touch

trace

Fig. 7. Ratio of occurrence probabilities of target categories for eachdesire query.

We determined categories characterizing each desire
query by using a bootstrapping method for the differencesin occurrence probabilities of each category between focuseddesire queries and the “want to touch” query. By calculating 10000 bootstrap samples of the difference and the
Bonferroni-corrected 95% CI, we tested if the difference wassigniﬁcant. The check marks in Table 4 show the categories
where the Bonferroni-corrected CIs did not overlap withzero (i.e., we could judge whether each category characterized the query).
These results show that the target categories that triggered desires were different between desire queries andthat the categories were comprehensively covered by thedesire queries. Under the assumption that each exploratory
procedure is used to feel a speciﬁc haptic property (e.g.,
statically contact to feel temperature), we might be able tospeculate the following tendencies:

when “body part” , “animal” or “person” is checked, the base verb was characterized as affective touch.
when “object” or “geometryr “ is checked, the base verb was characterized as discriminative touch.

In addition, considering that the target category of “person”, “animal”, or “body part” are regarded as target ofaffective touch, and the target category of “object”, and “geometry” are regarded as the target of discriminative touch(see Fig. 6), we can characterize which affective touch or
discriminative touch was dominant in each desire EP query.
The touching, stroking, statically contacting, and graspingare exploratory procedures desired for affective touch. The
hitting/tapping and pushing are exploratory procedures
desired for discriminative touch. In contour-tracing, while
the tracing is characterized as discriminative as compared
to touching as shown in Table 4, the proportions of affective
touch and discriminative touch are comparable as shownin Fig. 7. In total, the ratio of affective touch is dominant.
It can be speculated that Twitter users have a tendency towant affective touch, which means that they are somewhat
autotelic. Thus, the result of our survey might be moresuitable to explain tendencies for those who are autotelic.
Since it is known that there are individual differences in thedesire for touch as clariﬁed in NFT research [22], it would
be important to investigate what non-autotelic people want
to touch and compare it to our results as future studies.
On the other hand, the desires for touch that were classiﬁed as discriminative touch might include affective nature.
Since, to some extent, people have knowledge about thehaptic properties of everyday targets (e.g., size-/ materialweight illusions [33], [34] and the memory softness ef-

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

fect [35]), people do not need to touch them every time
to gain this information. Nevertheless, our results showedthat Twitter users represent their desire for touch via the exploratory procedure (i.e., the optimal way to gain maximum
haptic information), which might show that people tweeted
their desire to enjoy the feeling of the targets classiﬁed
as discriminative touch. Also, there is no forced, speciﬁc
discriminative task (e.g., detecting a ripe one among fruits),
and thus the tweets might reﬂect the spontaneous desires of
Twitter users. Comparing extracted targets in this study tothose in future laboratory experiments in which participants
perform discriminative tasks would be important to make it
clearer whether all the tweets are more affective.

8

TABLE 5The difference in the desires and actions for target words for eachquery. Five target words are shown in descending order of differences.
Blue target words denote that probability of desire for the target wassigniﬁcantly higher than probability of touch action for the target. Blacktarget words denote that there was no difference between desire andaction.
target word in descending order of difference magnitude between desire and action

touchstaticallycontact

1

2

3

4

5

excessive desire

breasthair

abdomen cheekbuttocklack of desire

ball

face

eye

nose

head

excessive desire

you

cat

peoplehumanskinanimallack of desire

hair

hand

outsideair

watershoulderexcessive desire

cat

dog

buttock

abdomen melack of desire

head

cheek

hair

back

face

excessive desire

stamp

abdomen cart

whorl

mole

lack of desire

buttonkey

cardstrollerback

excessive desire

drum

buttock

something ironlack of desire

hand

knee

shoulder

head

back

excessive desire

waist

buttock

tongue

ankle

hair

lack of desire

arm

hand

shoulder

wrist

hem

excessive desire

abdominal muscle
muscleeyebrowblood
vesselclaviclelack of desire

lip

edge

line

contour

4.3 Comparison between Desire and Action

5

G ENERAL D ISCUSSIONS

5.1 Discussions from Viewpoint of Base Verb of Query
5.1.1 What People Want to Touch
Although we adopted the “want to touch” query as a control
condition, not the main factor, the results of the desire query
provide some insights for discussion.

strokequery

We compared the desire for touch to the action for touch
to assess how much the desire to touch was “fulﬁlled”.
We computed the difference of target words’ occurrence
probabilities between cases of desire queries (e.g., “want totouch”) and action queries (e.g., “touched”). We sorted thetarget words by the difference value obtained by subtracting
the occurrence probability of the desire query from theprobability of the action query. The top ﬁve positive andbottom ﬁve negative target words are shown in Table 5. For
example, in the case of the base verb “statically contact”,
the target word “you” took the positive maximum value.
In that case, the probability of desire query was superior to
the probability of action query and it implies that the desire
to touch someone called “you” was not easily fulﬁlled. On
the other hand, the target word “hair” took the negative
minimum value. In that case, the probability of desire query
was inferior to the probability of action query and it implies
that the desire for touching hair was easily fulﬁlled or eventhat there was a lack of desire.
We also conducted a bootstrapping method in a similar
way as described above, to determine which top or bottom
ﬁve target words had a difference in occurrence probability
between desires and actions. The target words the CI ofwhich did not overlap with zero are colored in blue, whichindicates the desire or action frequently occurred.
We found that the relationship between the order of mostdesired target shown in Table 2 and the order of the target
shown in Table 5 varies with base verbs. For example, inthe case of base verb “statically contact”, the most desired
target was “you” (see Table 2) and also the 1st-ranked word
was “you” shown in Table 5. The results might reﬂect thefact that many people want to touch familiar people, but thedesire is not always satisﬁed. In contrast, in the case of thebase verb “push”, the most desired target was “button” butpeople much more frequently reported that they actually
pushed buttons.

push

keyboard

hit/tap

grasp

trace

photo

Approximately 68.5 % of the occurring target words
were categorized to the body part category (see Fig. 7).
In accordance with the higher frequency of the body partcategory, we found some body part words such as breast,
hair, buttock, abdomen, cheek, body, ear, and hand were
the target of the desire to touch (see Fig. 2). We found thatthe soft body parts such as breasts or buttocks were ranked
higher than hard body parts such as bones or muscles. The
result of the higher rank of softer body parts is in line withthe results in a previous study [36] showing that the perceived magnitude of pleasantness increased monotonicallyas a function of increasing compliance of human skin-likerubber objects.
From the viewpoint of the difference between desire andactions shown in Table 5, the target words that people havean excessive desire to touch showed a similar tendency tothe target words for the mere desires shown in Table 2. In
contrast, Table 5 shows that the target words that people
have less desire to touch were head-related parts such as
the face, eyes, nose, and head. It can be speculated thatthese head-related parts might refer to those parts of theirown body since it is unlikely that people frequently touchthe eyes and nose of other people or animals. For example,
to relieve itchy eyes or nose, people frequently and easily
perform touch actions in daily life and the desire to touch

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

these body parts would be easy to fulﬁll.
5.1.2 What People Want to Statically Contact
We found that “person” and “temperature” categories frequently became the target of static contact desire queries(see Table 4). As example words in the “person” category,
we found “you” and “people” were ranked in 1st and 2ndplaces among target words (see Table 2). As an example
word of the “temperature” category, we found “warmth”
was ranked in 6th among target words. From the viewpointof comparison of desire with action, we can observe that not
only people but also animals trigger desires for static contact(see Table 5). Based on the knowledge that the exploratory
procedure of static contact corresponds to the temperature
as a haptic object property [13], there is a possibility that thetemperature people want to appreciate is usually the humanor animal’s body temperature.
5.1.3 What People Want to Stroke
From the categorical viewpoint, the animal and person
categories statistically characterized the desire query “wantto stroke” (see Table 4). Note that the occurrence probabilityof the body part category was still higher than that of theanimal or person (see Fig. 7).
We found that the highest-ranked target words were
head, cat, and dog (see Table 2). From the viewpoint of
haptic properties, considering that the exploratory procedure of stroking is known to be performed to judge thetexture [13], Twitter users might want to appreciate the hairrelated texture. These results can be explained by the combination of previous psychophysical results [4], [37]. Previous
studies showed that smoother rather than rougher [4] surfaces invite participants to touch. In addition, fur or wool
is perceived to be smooth [37]. The combination of thisknowledge shows that the skin covered with soft hair can
be felt smooth and invites touch. It is considered signiﬁcantin the sense that the results of psychophysical experimentsthat have been veriﬁed in the laboratory with carefully
controlled stimuli can be reproduced in the real world as
well.
From the viewpoint of the difference between desire
and actions shown in Table 5, the target words that people
have an excessive desire to stroke show a similar tendency
to the target words for the mere desires shown in Fig. 2.
Interestingly, most of the targets that were reported to be
stroked and were lack of desire were head-related targets
such as head, cheek, hair, or face. Compared to the abdomen
and buttocks, these head-related targets are exposed andit seem to be more acceptable to stroke them, even in a
relatively distal relationship.
5.1.4 What People Want to Push
From the categorical viewpoint, the “object” category was
the dominant desired category for push and there were lessdesires in relation to body parts (see Fig. 7 and Table 4).
This is different from other queries that included a certain
amount of desire in relation to body parts. Most of thedesired target words for push were buttons and stamps (seeTable 2 and Fig. 4) and the sum of those two words attained
90 %. In particular, the desire for buttons was more than
70 %, and thus we discuss mainly about buttons.

9

From the result of the difference between desires andactions, the action of pushing a button was the most performed of all actions of pushing something. This might
be due to the fact that there are various physical buttons
around people such as those in elevators or on TV Remote
Controllers, and the pushing of buttons is performed on adaily basis.
On the other hand, currently, physical buttons as userinterface devices are being replaced by touchscreens. For
example, mobile phones with physical buttons or keyboards
prevailed until the 2000s, but touchscreen-only devices were
popularized in the 2010s [38]. For another example, carmakers are now replacing buttons with touchscreens on
dashboard infotainment systems. 98.8 % of all new cars soldin the US have a touchscreen display [39]. In contrast to
the current undergoing replacement of physical buttons bytouchscreens, our results suggest that there is and will be adesire for the action of pressing physical buttons. There areresearch studies on presenting tactile button feedback whentouching a touchscreen [40], but this is still in the researchphase, and thus we can expect that people will still tend towant to push buttons more and more in the future.
We must be noted that the desire for pushing a button
does not always mean the desire for the tactile feeling of
the button. In some cases, people just expect the result
accompanied by pushing the button. The reason behind thedesire is difﬁcult to understand with the investigation into
Twitter posts and an ofﬂine experiment is needed.
5.1.5 What People Want to Hit/tap
From the categorical viewpoint, the “object” category triggered most of the desires for hit/tap (see Fig. 7 and Table 4) as in the case of those for “push”. The similarity
between hit/tap and push seems to be reasonable sinceboth push and tap/hit are exploratory procedures related
to hardness/softness. The dominance of a small number oftarget words was observed in Fig. 4 and it was also similar
to the “push” query.
Focusing on the target words, drum and buttock were
popular, and these two words accounted for more than 80%
of all desires (see Table 2 and Fig. 4). Some words indicating musical instruments such as drums or keyboards havebeen extracted and we recognize that hitting a percussion
instrument is one of the popular hitting use cases. However,in this case, the reason behind the situation of “hitting the
percussion” was not clearly distinguishable between people
wanting to feel the hardness of the instrument and thoseactually wanting to play the percussion instrument.
From the viewpoint of the difference between desire andactions shown in Table 5, the target words that people haveexcessive desire to hit/tap have a similar tendency to thetarget words for the mere desires shown in Table 2. In
contrast, the target words that people actually did hit/tap
and had less desire to do so (i.e., a lack of desire) werebody-part words such as hand, knee, shoulder, head, andback. The use case when people do not want to hit/tap bodyparts but they nevertheless do is considered to be caused bynegative factors. Scolding children would be one of suchcases.

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

5.1.6

What People Want to Grasp

From the categorical viewpoint, the body part is the dominant category for the desire to grasp (see Fig. 7 and Table 4).
Out of all the desire queries, the “grasp” desire query had
the highest ratio of body parts category words (see Fig. 7).
Paying attention to each target word, waist, hand, tail,arm, ankle, and tongue were the distinctive target words forthe grasping desire query (see Table 2). The results appear
plausible because it is easy to imagine how people grasp
these particular body parts as compared to other body parts
extracted by other desire queries such as abdomen or cheek.
Also, when we look at the illustration mapping the grasping
desire query to the whole body (Fig. 5), we can notice thatthese body parts are relatively located at the limbs, which
are more exposed and easy to grasp.
Focusing on the difference between desires and actions
for target words shown in Table 5, the body parts which
are somewhat sexual were ranked such as waist, buttock,and tongue. The top two target words that triggered a
relative lack of desire but were reported to be frequently
touched were arms or hands. It is known that the bodily
regions where one may touch different individuals in theirsocial network are relationship-speciﬁc [41]. According to an
earlier study [31], arms and hands rather than other bodyparts are allowed to be touched by even emotionally distantacquaintances and thus, these two might be ranked high.
5.1.7

What People Want to Trace

From the categorical viewpoint, the ratio of target words
attributed to the geometry category was higher (see Fig. 7
and Table 4). The words such as line, crack, or ditch (shownin Table 2) were not shown by the other desire queries.
This geometrical word occurrence appears plausible since
the aim of contour-tracing as an exploratory procedure is tocapture the contour of targets.
In addition to the geometric target words, we can observe the hard target words that are neither soft nor deformable such as abdominal muscle, muscle, clavicle, andtooth in Table 2. As compared to the soft targets extracted byother queries, the results show that hard targets characterize
the desire query of tracing. Focusing on the difference between desires and actions for target words shown in Table 5,
the muscle parts attracted people’s attention but people also
have less opportunity to access the muscles.
5.2 Insight into Haptic Displays or Application Development
Based on our results, we can gain insights into haptic application development that would help developers of haptic
displays or applications. Here, there are two points to note.
First, we focus on haptic applications for entertainment
purposes when people touch and feel something that theywant to touch and feel, while there are three application
categories: training, assistance, and entertainment [42]. Second, the result of this study is obtained from a Twitteruser population which is skewed (see next Section 5.3). Still,
our result would help developers in two ways: (1) deﬁning
targets in haptic applications and (2) deﬁning requirementsof haptic displays.

10First, our results would help developers to deﬁne the target for which their application provides a touch experience.
Our results in Table 2 or Table 4 show which targets would
appeal to users for each exploratory procedure or haptic
object property. For example, in the case of the “hit/tap”
exploratory procedure, some words such as drums or keyboards indicating musical instruments have been extracted
at top rank, which suggests that hitting a percussion instrument would be one of the appealing hitting/tapping
use cases. There is already some research addressing haptic
feedback when using percussion instruments [43], [44] andour result would support the motivation of these studies.
Such information would contribute to companies developing haptic applications based on a market-in approach [45]and also help researchers to conﬁgure demonstrations of
newly proposed haptic displays. We also found relatively
fewer tweets representing desires to touch targets that cannot be touched in the real world (e.g. ﬁre and magical orbs).
If we can deﬁne a query that can collect a lot of these tweets,
we will be able to investigate the desire to touch things
that do not exist and cannot be touched in the real world,
beyond laboratory stimuli and daily targets. This direction
is important for understanding what kind of content creates
touch desire and to create an appealing haptic application.
Second, our results would also contribute to the requirement deﬁnition of haptic displays that could capture
people’s desire to touch. We discuss a temperature display
as one of examples. According to the Table 2 and Table 5,
we conﬁrm that the people or animal category targets are
popular with the exploratory procedure of static contact.
Based on the knowledge that the exploratory procedure ofstatic contact corresponds to temperature as a haptic object
property [13], there is a possibility that the temperaturepeople want to appreciate would usually be a human’sor animal’s body temperature. Although currently one of
the research directions regarding thermal displays is to
speed up the response time of temperature change utilizing
techniques such as water cooling [46], [47], [48], in the cases
for detecting and presenting body temperature, dynamic
temperature changes may not be necessary.
Unfortunately, from the viewpoint of requirement deﬁnitions, we found some targets for which it would be difﬁcult
to realize a touch experience using current haptic displays tocomprehensively satisfy user’s desires. Our results suggestthat there are some targets that are popular across multiple
properties. For example, the hand was extracted by thedesire queries whose base verbs were “touch”, “staticallycontact”, “hit/tap”, and “grasp”. This means that people
want to appreciate the temperature, softness, and shapeof the hand. This suggests the difﬁculty in providing acomprehensive experience of touching a cat or hand withhaptic displays. Since each haptic property is delivered by adifferent type of actuator, which has its own volume, theintegration of haptic displays would cause spatial interference issues [49]. Moreover, sensing multiple exploratory
procedures makes display development difﬁcult.
5.3

Limitations

To the best of our knowledge, this study is the ﬁrst to
utilize Twitter data to discuss people’s desire to touch inJOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

their daily lives. The analysis provided some insights into
desires in relation to targets or target categories, which werepreviously unknown. This was enabled by the large amountof data on “what people want to touch,” posted publicly by
Twitter users. However, there are several limitations to thecurrent study.
This study only covers Twitter users; it does not coverthose who do not use Twitter and those who do not use
social networks at all. In addition, the age or gender distribution of Twitter users is skewed relative to the distributionof the real-world population (see Supplementary Table fordetail). Moreover, it is also difﬁcult to know the age andgender of speciﬁc tweet authors since that information was
not provided by the API. If a robust method of estimating a
Twitter user’s age and gender is developed in the future, wewill be able to further analyze the desire for touch in termsof age and gender.
Note that the touch desire analyzed in this study was
only the speciﬁc part of desire that Twitter users have intheir daily lives. The desire that the Twitter user wanted toshare with the public was posted to Twitter.
The method we used and the results obtained in thisstudy are assumed to be Japanese-speciﬁc. This is because
only tweets written in Japanese were collected. Furthermore,
the touch needs or the role of touch in different cultures
are different [50]; for example, in Italy, a hug and kiss oneach cheek are considered a common and acceptable form ofgreeting. In contrast, in Japan, a proper greeting comprises
a bow and the absence of any haptic contact [51], [52]. Thus,
the results would be different even when the same method
is applied to different cultures.
In addition, we analyzed data collected in a certain
period between 2020 and 2021. There is a possibility that thedata was affected by time factors such as seasonality. Also,
there is a possibility that the desire for touch may be biased
by restrictions on travel and by social distancing due to
COVID-19 situations. To clarify this, one future study topic
is to test whether the result would be changed or unchangedby the time period for data collection.

6

C ONCLUSION

This study aimed at investigating what people want totouch in daily life. We collected texts in Japanese fromTwitter because there is a considerable amount of posted
text representing user’s desires on that platform. We carefully denoised the text and triangulated the results withmultiple queries corresponding to exploratory proceduresor multiple analyses. The results suggest that the target ofdesire depends on the exploratory procedures on both target
level and category level. Some previously-obtained ﬁndings
suggesting a preference for soft targets were also observedin our results while other ﬁndings suggesting a preference
for metallic targets were not observed in our results. In
addition, our results could give insights into haptic display
or application development because they show which target
is desired for each exploratory procedure or haptic property.
To the best of our knowledge, this is the ﬁrst study that used
Twitter data for discussing touch desires and it suggests thepossibility of using Twitter for understanding human touch
behaviors.

11

ACKNOWLEDGMENTS
This research was partially supported by JSPS KAKENHI,
Grant Number 19K20315, 2019.

R EFERENCES[1]

H. Nagano, S. Okamoto, and Y. Yamada, “Visual and sensory
properties of textures that appeal to human touch,” In Proceedingsof International Journal of Affective Engineering, vol. 12, no. 3, pp.
375–384, 2013.
[2] ——, “Haptic invitation of textures: Perceptually prominent properties of materials determine human touch motions,” IEEE Transactions on Haptics, vol. 7, no. 3, pp. 345–355, 2014.
[3] R. Etzi, C. Spence, and A. Gallace, “Textures that we like to touch:An experimental study of aesthetic preferences for tactile stimuli,”
Consciousness and Cognition, vol. 29, pp. 178–188, 2014.
[4] R. L. Klatzky and J. Peck, “Please touch: Object properties that
invite touch,” IEEE Transactions on Haptics, vol. 5, no. 2, pp. 139–
147, 2012.
[5] A. Metzger, M. Toscani, A. Akbarinia, M. Valsecchi, and K. Drewing, “Deep neural network model of haptic saliency,” Scientiﬁcreports, vol. 11, no. 1, pp. 1–14, 2021.
[6] M. Lau, K. Dev, W. Shi, J. Dorsey, and H. Rushmeier, “Tactile meshsaliency,” ACM Transactions on Graphics, vol. 35, no. 4, pp. 1–11,
2016.
[7] E. Kouloumpis, T. Wilson, and J. Moore, “Twitter sentiment
analysis: The good the bad and the omg!” In Proceedings of theInternational AAAI Conference on Web and Social Media, vol. 5, no. 1,
July 2011.
[8] E. D’Andrea, P. Ducange, B. Lazzerini, and F. Marcelloni, “Realtime detection of trafﬁc from twitter stream analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 2269–
2283, 2015.
[9] S. . Park, C. . Ok, and B. . Chae, “Using twitter data for
cruise tourism marketing and research,” Journal of Travel & Tourism
Marketing, vol. 33, no. 6, pp. 885–898, 2016.
[10] S. A. Golder and M. W. Macy, “Diurnal and seasonal mood varywith work, sleep, and daylength across diverse cultures,” Science,
vol. 333, no. 6051, pp. 1878–1881, 2011.
[11] “Twitter,” https://twitter.com, (Accessed on 11/24/2020).
[12] S. J. Lederman and R. L. Klatzky, “Hand movements: A window
into haptic object recognition,” Cognitive Psychology, vol. 19, no. 3,
pp. 342–368, 1987.
[13] ——, “Haptic perception: a tutorial,” Attention, Perception, & Psychophysics, vol. 71, no. 7, pp. 1439–1459, 2009.
[14] “Leading countries based on number of twitter users as of january
2021,”
https://www.statista.com/statistics/242606/numberof-active-twitter-users-in-selected-countries/,(Accessed
on
2/1/2021).
[15] A.
MittalandA.
Goel,“Stock
prediction
using
twitter
sentiment
analysis,”Standford
University,
CS229
(2011
http://cs229.stanford.edu/proj2011/GoelMittalStockMarketPredictionUsingTwitterSentimentAnalysis.pdf), vol. 15,
2012.
[16] R. Daniulaityte, R. W. Nahhas, S. Wijeratne, R. G. Carlson, F. R.
Lamy, S. S. Martins, E. W. Boyer, G. A. Smith, and A. Sheth, “time for dabs : Analyzing twitter data on marijuana concentrates
across the us,” Drug and Alcohol Dependence, vol. 155, pp. 307–311,
2015.
[17] S. Stemler, “An overview of content analysis,” Practical Assessment,
Research, and Evaluation, vol. 7, no. 1, p. 17, 2000.
[18] A. Kim, H. Hansen, J. Murphy, A. Richards, J. Duke, and J. Allen,“Methodological considerations in analyzing twitter data,” Journalof the National Cancer Institute. Monographs, vol. 2013, pp. 140–6, 12
2013.
[19] D. Ghosh and R. Guha, “What are we tweeting about obesity?
mapping tweets with topic modeling and geographic information
system,” Cartography and Geographic Information Science, vol. 40,
no. 2, pp. 90–102, 2013.
[20] F. McGlone, J. Wessberg, and H. Olausson, “Discriminative andaffective touch: sensing and feeling,” Neuron, vol. 82, no. 4, pp.
737–755, 2014.
[21] M. Nordin, “Low-threshold mechanoreceptive and nociceptive
units with unmyelinated (c) ﬁbres in the human supraorbital
nerve.” The Journal of Physiology, vol. 426, no. 1, pp. 229–240, 1990.

JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015[22] J. Peck and T. L. Childers, “Individual differences in haptic information processing: The need for touch scale,” Journal ofConsumer Research, vol. 30, no. 3, pp. 430–442, 2003.
[23] A. M. Okamura, M. R. Cutkosky, and J. T. Dennerlein, “Realitybased models for vibration feedback in virtual environments,”
IEEE/ASME Transactions on Mechatronics, vol. 6, no. 3, pp. 245–252,
2001.
[24] K. J. Kuchenbecker and G. Niemeyer, “Improving telerobotic touch
via high-frequency acceleration matching,” In Proceedings 2006
IEEE International Conference on Robotics and Automation, pp. 3893–
3898, 2006.
[25] K. Higashi, S. Okamoto, and Y. Yamada, “Perceived hardness
through actual and virtual damped natural vibrations,” IEEE
Transactions on Haptics, vol. 11, no. 4, pp. 646–651, 2018.
[26] A. Tolmachev, D. Kawahara, and S. Kurohashi, “Juman++: A morphological analysis toolkit for scriptio continua,” In Proceedingsof the 2018 Conference on Empirical Methods in Natural Language
Processing: System Demonstrations, pp. 54–59, 2018.
[27] D. Kawahara and S. Kurohashi, “A fully-lexicalized probabilistic
model for japanese syntactic and case structure analysis,” In Proceedings of the Human Language Technology Conference of the NAACL,
Main Conference, pp. 176–183, 2006.
[28] Y. Goldberg and O. Levy, “word2vec explained: deriving mikolov
et al.’s negative-sampling word-embedding method,” arXiv
preprint arXiv:1402.3722, 2014.
[29] “chive: Japanese word embedding with sudachi & nwjc,”
https://github.com/WorksApplications/chiVe, (Accessed on
11/24/2020).
[30] B. Efron and R. J. Tibshirani, An introduction to the bootstrap. CRC
press, 1994.
[31] J. T. Suvilehto, E. Glerean, R. I. Dunbar, R. Hari, and L. Nummenmaa, “Topography of social touching depends on emotionalbonds between humans,” In Proceedings of the National Academy of
Sciences, vol. 112, no. 45, pp. 13 811–13 816, 2015.
[32] J. H. Ward Jr, “Hierarchical grouping to optimize an objectivefunction,” Journal of the American Statistical Association, vol. 58, no.
301, pp. 236–244, 1963.
[33] A. Charpentier, “Analyse experimentale de quelques elements
de la sensation de poids,” Archive de Physiologie normale etpathologiques, vol. 3, pp. 122–135, 1891.
[34] H. K. Wolfe, “Some effects of size on judgments of weight.”
Psychological Review, vol. 5, no. 1, p. 25, 1898.
[35] A. Metzger and K. Drewing, “Memory inﬂuences haptic perception of softness,” Scientiﬁc reports, vol. 9, no. 1, pp. 1–10, 2019.
[36] A. Pasqualotto, M. Ng, Z. Y. Tan, and R. Kitada, “Tactile perceptionof pleasantness in relation to perceived softness,” Scientiﬁc Reports,
vol. 10, no. 1, pp. 1–10, 2020.
[37] M. Cavdan, K. Doerschner, and K. Drewing, “Task and material
properties interactively affect softness explorations along different
dimensions,” IEEE Transactions on Haptics, 2021.
[38] M. Lindstrom, “You love your iphone. literally,” New York Times,
vol. 1, p. 21A, 2011.
[39] “Screen
stars:
Whichinfotainment
systemdeservesaleading
role
in
your
next
car?”
https://www.consumerreports.org/infotainmentsystems/screen-stars-in-car-infotainment-systems/,(Accessed
on 12/03/2021).
[40] M. Sinclair, M. Pahud, and H. Benko, “Touchmover: Actuated
3d touchscreen with haptic feedback,” In Proceedings of the 2013
ACM International Conference on Interactive Tabletops and Surfaces, p.
287–296, 2013.
[41] S. E. Jones and A. E. Yarbrough, “A naturalistic study of themeanings of touch,” Communications Monographs, vol. 52, no. 1,
pp. 19–56, 1985.
[42] A. G. Rodrı́guez Ramı́rez, F. J. Garcı́a Luna, O. O. Vergara Villegas, and M. Nandayapa, Applications of Haptic Systems in Virtual
Environments: A Brief Review. Cham: Springer International Publishing, 2018, pp. 349–377.
[43] T. Hachisu and H. Kajimoto, “Hachistack: Dual-layer photo touchsensing for haptic and auditory tapping interaction,” In Proceedings of the SIGCHI Conference on Human Factors in Computing
Systems, p. 1411–1420, 2013.
[44] T. Hachisu, G. Cirio, M. Marchal, A. Lécuyer, and H. Kajimoto,“Virtual chromatic percussions simulated by pseudo-haptic andvibrotactile feedback,” In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, pp. 1–5,
2011.

12

[45] C. Grönroos, “Deﬁning marketing: a market-oriented approach,”European Journal of Marketing, vol. 23, no. 1, pp. 52–60, 1989.
[46] M. Sakaguchi, K. Imai, and K. Hayakawa, “Development of highspeed thermal display using water ﬂow,” In Proceedings of Human
Interface and the Management of Information. Information and Knowledge Design and Evaluation, pp. 233–240, 2014.
[47] M. Gabardi, D. Chiaradia, D. Leonardis, M. Solazzi, and A. Frisoli,“A high performance thermal control for simulation of different
materials in a ﬁngertip haptic device,” In Proceedings of International Conference on Human Haptic Sensing and Touch EnabledComputer Applications, pp. 313–325, 2018.
[48] S. Gallo, L. Santos-Carreras, G. Rognini, M. Hara, A. Yamamoto,and T. Higuchi, “Towards multimodal haptics for teleoperation:
Design of a tactile thermal display,” In Proceedings of 2012 12th
IEEE International Workshop on Advanced Motion Control, pp. 1–5,
2012.
[49] D. Wang, K. Ohnishi, and W. Xu, “Multimodal haptic display forvirtual reality: A survey,” IEEE Transactions on Industrial Electronics,
vol. 67, no. 1, pp. 610–623, 2019.
[50] P. Duarte and S. C. e Silva, “Need-for-touch and online purchasepropensity: A comparative study of portuguese and chinese consumers,” Journal of Retailing and Consumer Services, vol. 55, pp. 102–
122, 2020.
[51] R. H. Finnegan, Communicating: The multiple modes of human interconnection. Psychology Press, 2002.
[52] E. McDaniel and P. A. Andersen, “International patterns of interpersonal tactile communication: A ﬁeld study,” Journal of Nonverbal
Behavior, vol. 22, no. 1, pp. 59–75, 1998.

Yusuke Ujitoko is a researcher at the NTT Communication Science Laboratories since 2020.
He used to be a member of the R&D Groupin Hitachi, Ltd. from 2016 to 2020. He received his Ph.D. from the University of ElectroCommunications, Japan, in 2020. He received
his BE degree in mechanical engineering and
MAE degree in interdisciplinary information studies from the University of Tokyo, Japan, in 2014
and 2016, respectively. His research interests
include applied haptic perception and interfaces.

Yuki Ban received his MS and PhD degrees
in information science and technology from the
University of Tokyo, Tokyo, Japan, in 2013 and2016, respectively. He was a researcher at Xcoo
Inc. research from 2016 to 2017. He is currently an assistant professor at the Department
of Frontier Sciences at the University of Tokyo.
His current research interests include modifying
spatial perception using visuo-haptic interaction.
He is a member of the IEEE.

Takumi Yokosaka received the PhD degree
in information processing from the Tokyo Institute of Technology, in 2018. He is employed in
the NTT Communication Science Laboratories,
Japan. His research interests include perception
and cognition shaped by body movement.