DEIM Forum 2023 5b-5-4

Text classiﬁcation for breast cancer patient identity from Weibo posts
Zhouqing ZHANG† , Kongmeng LIEW† , Wan-Jou SHE† , Shuntaro YADA† , Shoko WAKAMIYA† ,and Eiji ARAMAKI†† Nara Institute of Science and Technology
8916-5, Takayama-cho, Ikoma, Nara 630–0192, Japan
E-mail: †{zhang.zhouqing.yx6,liew.kongmeng,she.wanjou,s-yada,wakamiya,aramaki}@is.naist.jp
Abstract As one of the most common cancers among female patients, breast cancer patients ﬁnd social media
a handy platform to share their feelings after diagnosis, disease episodes, and experiences of coping with cancer.
Furthermore, some close others of the breast cancer patients also often utilize the platform to share their experiences
related to breast cancer. In this study, we aim to identify social media users who tend to initiate or engage in breast
cancer discussions. To do so, we applied a transformer-based language model from Chinese Weibo posts to identify
the author’s relationship with the breast cancer patient. We have identiﬁed six categories according to the types ofrelationship with the cancer patients: post user, family members, friends relatives, acquaintances, heard relation,and no patient. By classifying diﬀerent patient in the post, researchers can develop a more in-depth understanding
on individuals’ opinions and emotional investment about breast cancer discussion. For instance, when we researchon breast cancer patients themselves, which can allow us to know and meet patients’ needs.
Key words Text classiﬁcation, machine learning, natural language processing, breast cancer, social media
to express diﬀerent opinions by the powerful storytelling way

1 Introduction[4, 5]. Such materials are extremely valuable to focus on.

In the modern era, breast cancer is a globally prevalent

Nowadays, social media is an indispensable part in our

disease. With more than 2 million people diagnosed withdaily lives. Almost everyone is using at least one type ofbreast cancer around the world in 2020 (statistics releasessocial media platforms [6]. People share their life events fre-by World Health Organization (WHO)), breast cancer is aquently on social media. As a social tool, it can smoothly

familiar disease to almost all populations worldwide. Breast

interact and communicate with their friends and family incancer is a chronic disease with a high mortality rate, whichtime [7, 8]. Sina Weibo（注 2） is one of the most useful andposes a serious threat to human life [1]. For this reason, peo-

popular social platforms in China, it is a Chinese counter-ple often have a negative view about breast cancer, such aspart of Twitter [9]. The number of Weibo’s monthly active

sadness, fears, depression [2]. In recent decades, the num-users reached 511 million by 2020. In other words, Weibo is

ber of newly diagnosed patients continues to increase each

known by almost everyone in China, according to the statis-

year, despite continuous improvements in medical technol-

tics released by Sina Corp. [10]. People can discuss all kinds

ogy worldwide [1]. In China, breast cancer is a major diseaseof topics on Weibo, and this certainly including the topic ofthat plagues common people, more than 400,000 people werebreast cancer. With the larger number of users developing

diagnosed with breast cancer, and more than 100,000 people

Weibo, the large number of people discussing breast cancer-（注 1）. A large number of new

related topics on Weibo, correspondingly, the large amountconﬁrmed cases emerge every year; behind this, there areof data generated makes Weibo data a valuable corpus fornumerous stories intertwined with breast cancer [3]. Almost

research.

died from breast cancer in 2020

every female individual can be at risk of developing breast

In recent years, machine learning is a hot research branchcancer, and they can be our family members, friends, rel-in computer science. Researchers focusing on machine learn-

atives, neighbors, school fellows, or celebrities. As a result,ing have opened up a lot of outstanding work, no matter

breast cancer-related topics have become an inseparable partwhether it is in computer vision or natural language pro-in our everyday social lives. Discussion on breast cancer iscessing (NLP). In NLP, machine learning has shifted from

（注 1）：https://www.who.int/（注 2）：https://weibo.com

conventional neural networks to a transformer-based frame-

To our knowledge, however, there is not too much studies

work, resulting in cutting-edge pre-trained language models

to focus on Chinese Weibo. Meanwhile, although there is not

such as Bidirectional Encoder Representations from Trans-

been much such work same with us in prior research, this is aformers (BERT) [11], RoBERTa [12], and GPT-3 [13], whichreal widespread social problem for the topic of breast cancer,

have greatly improved the use of downstream tasks (e.g., sen-so we broke precedent. We would like to use transformer-

timent analysis, text classiﬁcation, named entity recognition,

based language model to do text classiﬁcation from Weibo

and text generation). This has provided a massive boost to

posts.

the development of NLP techniques [14].
To turn our attention to the topic of breast cancer, we aim
to study how Chinese netizens discuss breast cancer-related

3 Dataset
3. 1 Data Crawling

topics on social media, and we believe that there should ex-

Since Sina Weibo does not release an oﬃcial application

ist some reasons when people engage in breast cancer-related

programming interface (API) to the public, we made a web

discussion, for example, family members, friends, relatives,

crawler programming to request posts. Our web crawler is toas patients might be the reason why people want to speak

simulate a user visit to Weibo oﬃcial website with some ad-with breast cancer, or hearing some stories make people wantjustable parameters, and search for relevant posts. Throughto focus on. In this study, we use a pre-trained language

this approach, each web search request can get up to 50

model as a research tool. We choose Chinese Weibo as a

posts, while asking for a new search request, then new posts

corpus resource to explore.

will be available. Due to the strong anti-crawler mechanism,

This study aims to identify whether a post is talking

between every two search requests, an interval time must be

about breast cancer patient and what is the relationshipset; otherwise, the request will be recognized by the Weibo

between the mentioned breast cancer patient and the post

server as an illegal access and the programming will be forced

user. To achieve these goals, we set six categories accord-

to terminate. In our crawler programming, the adjustable

ing to the distance score between the breast cancer pa-

parameters include keywords, publish date, location, and in-tient mentioned in the post and the author of the post,

terval time of two search requests.

they are post user, family members, friends relatives,

In our data collection process, we conducted two searches

acquaintances, heard relation, and no patient, respec-with diﬀerent keyword pairs: (“乳腺癌 (breast cancer)” andtively.

“悲伤 (sadness)”) and (“乳腺癌 (breast cancer)” and “记录 (record)”) in Chinese characters, and the posting time

2 Related work

was set to January 1st, 2018 to December 31st, 2021 in

In recent past, since social media contains a lot of valuable

both data collection processes, the interval time was 15 sec-

corpus, which attracts lots of researchers to take advantage

onds, the location parameter was blank. Finally, for the two

（注 3）

（注 4）

have a wide range ofsearches with diﬀerent keywords, we obtained 160,182 posts

users, many researchers took Twitter and Reddit as corpus

and 144,125 posts, respectively, each posts includes user id,

resources to explore various social issues [15, 16]. Researchers

user name, user type, publish time, post text, location, num-incorporated NLP and machine learning techniques to focus

ber of comments, number of likes, and number of reposts.

of it. As Twitterand Reddit

on social media, which made out excellent results.

3. 2 Data Cleaning

In the earlier period, Orabi et al. [15] used conventional

In the data cleaning phase, we aim to extract posts con-neural network (CNN)-based machine learning model, likecerning breast cancer by individual users. For this phase, we

CNNs, SVMs to detect depression from Twitter. Sekulic etﬁrst combined the collected data with the two keyword pairs

al. [17] used CNN to detect a wide range of mental health(“乳腺癌 (breast cancer)” and “悲伤 (sadness)”) and (“乳腺issues. In recent years, machine learning has shifted from癌 (breast cancer)” and “记录 (record)”) together. To ensure

CNNs to a transformer-based framework, which made a lot ofthat the post text is related to the breast cancer patient, we

researchers started to use transformer-based model to study

removed duplicated data, advertising content. Then, to en-social media. Murarka et al. [7] used RoBERTa to study

sure that the posts are from personal users, we checked user

mental illnesses on social media. Ammer et al. [18] used

type and recognized that a post is from an oﬃcial account ortransformer-based model to study mental illness classiﬁca-

an individual account. According to this rule, oﬃcial posts,

tion on social media.

institutional posts, or posts from non-personal users were excluded. Finally, the remaining data are not only related to（注 3）：https://twitter.com/

breast cancer, but also from individual users, we obtained（注 4）：https://www.reddit.com/

10,322 posts in total.

3. 3 Annotation
We deﬁne a distance score from 0 to 5, which refers intimacy level between people. We set up six categories basedon the distance score from 0 (the most intimate level) to 5
(the least intimate level) between the breast cancer patientmentioned in the post and the post user (author) as follows.
post user When the breast cancer patient mentioned in
the post is the post user themselves, the breast cancer
patient and the post user are the same, so the distancescore is 0.
Figure 1 The distribution of the annotated posts

family members With distance score 1, it indicated thatthe patient mentioned in the post is a family member

speaker repeated the same annotation work. Eventually, we(e.g., wife, mother, grandmother, sister), who have acalculated these two data labeled by diﬀerent people, to thevery intimate relationship with the post user.

six categories. Cohen’s kappa value is 0.672288, indicating

friends relatives With distance score 2, it is consideredthat the patient mentioned in the post is a relative (e.g.,

substantial agreement between both annotators.

4 Methodology

cousins and aunts) or friends of the post user.
4. 1 Data Processing
acquaintances Distance score 3 is assigned when the men-

This study used Chinese Roberta（注 5） [20] as our classiﬁca-

tioned patient is an acquaintance of the post user (e.g.,tion baseline model. To improve the accuracy of multiclassa colleague and a neighbor) or just an acquaintance who

text classiﬁcation, we made two classiﬁers: a binary classi-

knows each other.

ﬁer and a multiclass classiﬁer. Since the pre-trained language

heard relation Distance score 4 was assigned when thepatient mentioned in the post is only a heard relationship with the post user, or a very distant relationship(e.g., someone who met shortly in a location, a celebrity,a character on a TV show, a public ﬁgure or someone
who heard about the patient from someone around).

model Chinese Roberta has a limited input character lengthof 512, 522 posts in our dataset are longer than 512, therefore, we did a step to extract text abstract to ensure the text
length is within 512. We took SnowNLP as a summarization
tool [21] to extract abstracts for the post longer than the intended length. The abstract extraction mechanism is basedon the weight of each sentence in each paragraph. By setting

no patient Distance score 5 is referred to as no patientmentioned in the post.
We randomly annotated about 30% of the data (here is 3,000
Weibo posts) based on the above classiﬁcation criteria, eachpiece of data was assigned a label from six categories. In
the process of labeling, ﬁrst step is to determine if there is apatient in post or not, which is a binary classiﬁcation. Then
we continued to work on if a patient exists in post, give alabel to the post according to the relationship between thementioned patient and the post author (the proposed classiﬁcation criteria), which was a multiclass classiﬁcation. Alldata labeling was done by one of the authors who was a
Chinese native speaker. See annotation results in Figure 1.
Samples for the annotations were shown in Table 1.
In order to verify that our annotations were factually labeled and free of personal subjective bias, we implemented
an agreement veriﬁcation by Cohen’s kappa [19]. 20% of thea number parameter, the corresponding number of sentences
is output accordingly, and so the text summary is achieved.
4. 2 Classiﬁer Training
Two classiﬁers were generated in this study. The ﬁrst one
was a binary classiﬁer for classifying Patient and np Patient.
The second one was a multiclass classiﬁer designed to groupeach post containing patient into one of the ﬁve categories (post user, family members, friends relatives,
acquaintances, and heard relation).
4. 2. 1 Binary Classiﬁer
To extract patient posts, we make a binary classiﬁer anddistinguish each post into patient class or no patient class.
The Chinese Roberta model was ﬁne-tuned using the annotated data. We merged post user, family members,
friends relatives, acquaintances, and heard relation
into patient. 80% (2,400 Weibo posts) of the labeled posts
were used to train the model and 20% (600 Weibo posts)

annotated data (600 Weibo posts) were randomly selected,
given the same classiﬁcation criteria, another native Chinese（注 5）：https://huggingface.co/hﬂ/chinese-roberta-wwm-ext

Table 1 Weibo post examples with label
ChineseEnglishLabel (Distance去让自己相信自己得了癌是一件漫长的事情，即使有两It was a long time to convince myself that I had cancer, even份确诊报告摆在我的面前, 当第二份报告我拿在手里，我though I had two reports in front of me. When I held the second

心里默念三遍阿弥陀佛，打开看到结果和第一次结果一report in my hand, I chanted Amitabha three times in my heart,样的时候，我心里咯噔一下，我被确诊了乳腺癌！我能and when I opened it and saw that the result was the same as

做的就是告诉自己，接受它，面对它，然后打败它！

the ﬁrst result, my heart thumped, I was diagnosed with breastscore)
post user (0)cancer! All I could do was tell myself to accept it, face it, andbeat it!
妈妈确诊乳腺癌的第三天，除了哭什么都做不了，不能替On the third day of my mom’s breast cancer diagnosis, I couldn’t

family members

她生病，不能替她分担痛苦，确诊之前一家人高高兴兴

do anything but cry, I could not share her sickness and pain, my(1)

的，我从来不奢求我的妈妈有多么的有钱，只想她平平安family was happy before the diagnosis, I never wanted my mom

安健健康康的，可为什么那么难呢，妈妈一生多灾多难， to be rich, I just wanted her to be safe and healthy, but why was
所有的痛苦都承受了，我不怪老天不公，只怪老天为什

it so hard? I don’t blame God for being unjust, I just blame God么不放过她，如果可以，我希望生病的是我自己，如果能

for not letting her go. If I could, I wish I was the one who was让妈妈健康，要我做什么都可以，只希望老天能善待我。 sick, if I could make my mom healthy, I could do anything, I justhope God would treat me well.
难得有一会的空闲时间，想想人的一生做点自己喜欢的It is rare to have a moment of free time to think about how hard it

friends relatives

事真的挺难。昨天惊闻一个好朋友的了乳腺癌小女儿才

is to do something you love in your life. Yesterday, I was shocked(2)两岁。好可怕！没有妈妈的孩子，想想就心疼。希望所

to hear that a good friend had breast cancer and her little daugh-

有的孩子都有妈妈爱，妈妈们身体健康。希望人类早日

ter was only two years old. How terrible! It hurts to think of a

攻克癌症！祝福我的好朋友早日康复！

child without a mother. I hope all children have their mothers’
love and mothers are in good health. I hope mankind will overcome cancer soon! Wish my good friend a speedy recovery!

今天突然得知以前的一个同事癌症到了最后的时期。三Today I suddenly learned that a former colleague has reached the年前检查出来了乳腺癌，切的时候发现扩散到了淋巴一ﬁnal stage of cancer. Three years ago, she was examined for breast

部分就一并切了，去年发现扩散到了骨头，这两天说是cancer, and when she was cut, she found that it had spread to theacquaintances (3)已经不认识人了。他的女儿才五年级，还有年迈的父母， lymphatic part and was cut together. Last year, she found that it
不过还好她不是独生子女。有时候我在想这样和离婚失had spread to the bones, and in the past two days, she said that

去父母一方，哪个对孩子的影响小一些？

she did not recognize people. His daughter is only in the ﬁfth
grade and has elderly parents, but fortunately she is not an only
child. Sometimes I wonder which has less impact on the child,this or losing one parent to divorce?

和同事闲聊，得知她同学经常生前男友的气，年纪轻轻就

I was chatting with a colleague and learned that her classmate was

得了乳腺癌早期。感叹现在的癌症越来越年轻化以后更often angry with her ex-boyfriend and got early stage breast can-

要好好爱自己的同时，暗自庆幸 2 年前的决定，一个男cer at a young age. She sighed that cancer is getting younger and的时常让你生气，连你的心情都不顾及，还指望他以后善

younger, and that she should love herself more, but she was glad

待你？一段关系，开心舒适最重要！这年头，还是保命that she made the decision 2 years ago. A relationship, happy andheard relation (4)最要紧！顺带嘲讽一下牛哥最近可爱肉见涨，自带喜感。 comfortable is the most important! These days, it’s important tosave your life! Incidentally, mock the cattle recently cute meat tosee the rise, bring their own sense of comedy.
结婚生小孩对我而言，真的是让我瞬间老了十岁的选择。 Getting married and having children was really a choice that in每每一次郁结，我都觉得乳腺癌离我又近了一步。那些stantly aged me by ten years for me. Every time I got depressed,

婆婆妈妈琐琐碎碎的生活，硬生生把我变成一个每天只I felt that breast cancer was one step closer to me. The mother-会抱怨和唠唠叨叨黄脸婆。呵呵。只要不生活在同一个

in-law’s trivial life has turned me into a yellow-faced woman who

屋檐下面对屎尿屁的生活，谁在外面还他妈不是个人见

only complains and nags every day. Oh. As long as you don’t live

人爱的小仙女呢。

under the same roof to face the shit life, who is not a fucking fairy

no patient (5)outside.

Table 2 Binary classiﬁer’s metrics report

4. 2. 2 Multiclass Classiﬁer

precision

recall

f1-score

supportno patient

0.90

0.90

0.90

204

patient

0.95

0.95

0.95

396

signing each post into one of the ﬁve categories based

The target classiﬁer focused on patient categorizing, as-macro avg

0.92

0.92

0.92

600

on the patient’s info:

weighted avg

0.93

0.93

0.93

600

friends relatives, acquaintances and heard relation.

post user, family members,

We removed no patient category from the annotated data.
were used to test the ﬁne-tuned model. The hyperparame-

1,515 Weibo posts were used to ﬁne-tune the Chinese

ters for the model training were as follows: batch size = 16,

Roberta model and 396 Weibo posts were used to test thelearning rate = 10

−5

, and training epochs = 5.

ﬁne-tuned model. The main parameters were the same as
for the binary classiﬁer.

tained the predicted label for all data. Figure 4 shows the
distribution of the predicted labels.
5. 2 Discussion
The prediction results were consistent with the manuallylabeled data in all categories’ distribution in Figure 1. Fromthem, we can easily know no patient category occupies theﬁrst place in all categories, with a proportion of about one
third. This indicated that about one third of the posts,
users did not mention the breast cancer patient but only the
keywords of breast cancer in their posts. In the remaining
Figure 2 Binary classiﬁer’s confusion matrix

two thirds of the posts, according to our classiﬁcation deﬁnition, the breast cancer patients most frequently mentioned

Table 3 Multiclass classiﬁer’s metrics reportin user’s posts were the one most distantly related to theprecision

recall

f1-score

supportacquaintances

0.76

0.67

0.71

75

family members

0.93

0.90

0.91

86

Besides, family members, acquaintances, post user,

friends relatives

0.74

0.84

0.79

51

and friends relatives categories occupy the second, third,

heard relation

0.83

0.83

0.83

102

fourth, ﬁfth, and sixth positions, respectively. Referring topost user

0.86

0.91

0.89

82

macro avg

0.82

0.83

0.83

396

weighted avg

0.82

0.83

0.83

396

post user, and it was assigned to heard relation category.

our classiﬁcation criteria, heard relation was deﬁned as thementioned patient in the post is only a heard relationshipwith the post user, like celebrity, a character in a TV show.
These easily resonate with people, and so that might be thereason heard relation ranked the ﬁrst among the ﬁve categories that contains the mentioned patient.
From the confusion matrix of the binary classiﬁcation in
Figure 2, we can see some cases were mistakenly classiﬁed
into a diﬀerent category. Examples of error cases are shownin Table 4. One common reason of the classiﬁcation error isthat the main character in a post is unclear and needs to beinferred through semantic understanding. For example, in(1), the post does not specify who the breast cancer patient

Figure 3 Multiclass classiﬁer’s confusion matrix

is, but after reading it, we can infer that the breast cancer
patient is the post user. In (2), the post mentions a wife’s
breast cancer, but does not say whose wife it is. After reading

5 Results and Discussion
5. 1 Results
To evaluate the accuracy of the binary classiﬁer, we used
600 Weibo posts as a test set. Table 2 shows the results ofthe binary classiﬁcation and Figure 2 shows the confusion
matrix of the binary classiﬁcation.
To evaluate the accuracy of the multiclass classiﬁer, weused 396 Weibo posts as a test set. Table 3 shows the results of the multiclass classiﬁcation and Figure 3 shows theconfusion matrix of the multiclass classiﬁcation.
In addition to the annotated data, there were still 7,322
unlabeled posts. We classiﬁed them by applying the twoclassiﬁers. Firstly, the binary classiﬁer was assigned to makethe binary classiﬁcation (patient and no patient). Thenamong the post containing patient, the multiclass classiﬁer
was used to predict a label for each post. Finally, we ob-

the post, from semantic understanding, we can infer there is
nobody, but generally refers to the wife of a certain person.
Under such situation, where semantic inference needs to be
combined, our classiﬁer is error-prone.
In Table 3 and Figure 3, we can ﬁnd that the highest classiﬁcation accuracy is family members among the 5 categories, which is beyond 90% in F1 score. The lowest classiﬁcation accuracy is acquaintances, which is around 70%
in F1 score. For the 5 categories, the classiﬁcation accuracy is beyond 80% in F1 score on average. Table 5 lists
some examples of error cases in multiclass classiﬁcation. In(3), the true label as post user was predicted as acquaintances, we found that there was a “colleague” appearing in
the post. Since it was not clearly stated that the breast cancer is “ME” (We inferred the breast cancer patient should
be post user from semantic understanding), and according toour classiﬁcation deﬁnition, “colleague” should be groupedTable 4 Examples of error cases in binary classiﬁcation
ID

True label

Predict label

Post

(1)

patient

no patient

人与人之间的边界感真的太重要了，至少对我来说。今天开完会一个同事顺路带我回去，聊起来我一个人住妈妈会不会担心的问题。结果没讲两句就跳到：你妈妈现在还催婚吗（意思是得了乳腺癌后）？
你有没有准备找男朋友？就很突然，我觉得自己够机智了，直接说我因为生病被人抛弃了，这不话
都在里面了吗？结果人家居然能够继续问：那对方现在结婚了吗？我真的当场懵逼，真想回一句“关你屁事”或者“关我屁事”，人要是为了我不结婚了，从一开始就不会抛弃我这不难懂吧？既然都说抛弃了我他结不结婚我还要去管？不累么？好好儿开心地活着不好么？非要膈应自己？哦，对了，这
次是个女的。 The sense of boundaries between people is really too important, at least forme. Today after the meeting a colleague stopped by to take me back and chatted about
whether my mom would be worried if I lived alone. I didn’t talk for two sentences before I
jumped to: Is your mom still pushing for marriage (meaning after having breast cancer)?
Are you planning to ﬁnd a boyfriend? I thought I was smart enough to say that I had
been abandoned because I was sick. The result was that people were able to continue toask: Is the other party now married? I really confused on the spot, I really want to returna “none of your business” or “none of my business”, if people do not get married for me,from the beginning will not abandon me this is not diﬃcult to understand it? Since they
say abandoned me he married or not I have to care? Do not tired? What’s wrong withliving happily? Must be a pain in the ass? Oh yes, this time it is a woman.

(2)

no patientpatient

有妻子乳腺癌术后化疗后 5 年复发转移，手机里 5 年的抽血、拍片的结果照片全在，用的什么化疗
方案，什么反应，更改的方案全部记得的丈夫。也有几几年确诊，左肺还是右肺都不知道，跟他打电话让来看复查结果也不来院的丈夫。 There are husbands whose wives’ breast cancer recurredand metastasized 5 years after chemotherapy, but they have all the photos of the 5 years’blood draws and ﬁlm results in their cell phones, and they remember what chemotherapy
regimen they used, what reaction they had, and what regimen they changed. There arealso husbands who don’t know whether their left or right lung was diagnosed several years
ago, and who don’t come to the hospital even though they called him to see the results of
the review.

into acquaintances category. So this might be the reason(3) was mistakenly classiﬁed into other category. In (4), thereasons for misclassiﬁcation are same as (3). Referring toour classiﬁcation criteria, the breast cancer patient had adistant relationship with the post author in (4) from semantic understanding, so it should be heard relation. However,there was “colleague” appearing at the beginning of the post,
so this might be the reason that was mistakenly classiﬁed
into acquaintances category. In (5), the breast cancer patient was post author regarded as post user category, but
in the post there were multiple family-related appellations

Figure 4 The distribution of the predicted posts

nouns, such father, baby, son, daughter-in-law, granddaughter, grandma, so this would be the reason that lead to misclassiﬁcation into family members category. In (6), thebreast cancer patient was the cousin of the post author, referring to our friends relatives category, but it was classiﬁed
into acquaintances category by our classiﬁer. However, we
could not explain the reason for this case from the point ofview of the classiﬁcation criteria.
Based on the above analysis, we believed that whether or
not the post clearly indicated who the breast cancer patient
was or if there was a breast cancer patient in the post would
be a factor in the classiﬁcation accuracy. In the post, multiple diﬀerent appellations also will aﬀect the classiﬁcation.

6 Case study
Among our classiﬁcation results, one category is postuser,which refers to the mentioned patient in the post is the postuser (author). That is, the breast cancer patients were talking about themselves, something related to their disease,such as treatment experiences, needs as patients, social relationships, and life changes. In this case study, we aimed
to understand the needs of Chinese breast cancer patients.
In addition, we compared the needs of Chinese patients andJapanese patients.
In Japan, the Shizuoka Cancer Center has made a cancer
problem classiﬁcation (we call the Shizuoka Cancer Classiﬁ-

Table 5 Examples of error cases in multiclass classiﬁcation
ID

True label

Predict label

Post

(3)

post user

acquaintances

人与人之间的边界感真的太重要了，至少对我来说。今天开完会一个同事顺路带我回去，聊起来我一个人住妈妈会不会担心的问题。结果没讲两句就跳到：你妈妈现在还催婚吗（意思是得了乳腺癌后）？你有没有准备找男朋友？就很突然，我觉得自己够机智了，直接说我因为生病被人抛弃了，这不话都在里面了吗？
结果人家居然能够继续问：那对方现在结婚了吗？我真的当场懵逼，真想回一句“关你屁事”或者“关我屁事”，人要是为了我不结婚了，从一开始就不会抛弃我这不难懂吧？既然都说抛弃了我他结不结婚我还
要去管？不累么？好好儿开心地活着不好么？非要膈应自己？哦，对了，这次是个女的。 The sense ofboundaries between people is really too important, at least for me. Today after the meeting acolleague stopped by to take me back and chatted about whether my mom would be worried
if I lived alone. I didn’t talk for two sentences before I jumped to: Is your mom still pushingfor marriage (meaning after having breast cancer)? Are you planning to ﬁnd a boyfriend? I
thought I was smart enough to say that I had been abandoned because I was sick. The result
was that people were able to continue to ask: Is the other party now married? I really confusedon the spot, I really want to return a “none of your business” or “none of my business”, if
people do not get married for me, from the beginning will not abandon me this is not diﬃcult
to understand it? Since they say abandoned me he married or not I have to care? Do not
tired? What’s wrong with living happily? Must be a pain in the ass? Oh yes, this time it is awoman.

(4)

heard relationacquaintances

和同事闲聊，得知她同学经常生前男友的气，年纪轻轻就得了乳腺癌早期。
。
。感叹现在的癌症越来越年轻化
以后更要好好爱自己的同时，暗自庆幸 2 年前的决定，一个男的时常让你生气，连你的心情都不顾及，还指望
他以后善待你？一段关系，开心舒适最重要！这年头，还是保命最要紧！顺带嘲讽一下牛哥最近可爱肉见涨，
自带喜感。 I was chatting with a colleague. I learned that her classmate was often angry withher ex-boyfriend and got early stage of breast cancer at a young age. She sighed that cancer isgetting younger and younger, and that she should love herself more, but she was glad that she
made the decision 2 years ago. A relationship, happy and comfortable is the most important!
These days, it’s important to save your life! Incidentally, mock the cattle recently cute meatto see the rise, bring their own sense of comedy.

(5)

post user

family members 难忘的十一天就是我的这个宝贝在医院陪护我 11 天，谁也信不着爸爸要换她让她去上班不行。手术那几天
整夜睡不好着，一会翻身，一会喝水，从来没有烦过，细心照顾着，营养餐天天换样，给我剪指甲，洗脚，真是无微不至，我虽然得乳腺癌是不兴的，但是幸运的是我养了个孝顺的好女儿，同时我还有个好儿子好儿媳妇，他们没有来陪我，是因为疫情期间不方便来，孙女十三岁了还得学习，我们来时太急了没告诉孙女，当视频知道奶奶生病了，就哭了说怎么不告诉我？唉想想这些我一定要坚强战胜病魔 The unforgettable elevendays is my baby in the hospital to accompany me 11 days, who can not believe that the father
to replace her to let her go to work can not. I had breast cancer, but luckily I have a gooddaughter who is ﬁlial, and I also have a good son and daughter-in-law, they did not come toaccompany me because it is not convenient to come during the epidemic, my granddaughter
is 13 years old and still has to study. When the video knew that grandma was sick, she criedand said, “Why didn’t you tell me? Thinking about all this I must be strong to overcome thedisease.

(6)

friends relatives

acquaintances刚唠嗑唠到重疾险，说我堂姐一个女生做策划的，一直熬夜压力很大但混得贼好。（年薪 200w 左右，然后被检查出乳腺癌中期。切了乳腺之后本来人快恢复了，结果又突然恶化医生诊断可能最多也就是十月
份了。我还感慨生命太无常了，我堂姐说就经常生闷气的人容易得吧，因为生闷气的话就会有很多脏东
西出不来，郁结在胸口久而久之就成了肿瘤，淦，所以说做人嘛最重要的是开心，这句话一点都没错。不爽了就说。难受了就哭。压力大就吃喝玩乐。别搞得最后自己身心都受损。 Just natter natter to thecritical illness insurance, said my cousin a girl doing planning, has been staying up all nightstressful but mixed well. （The company’s annual salary is about 200w, and then it was foundto have mid-stage breast cancer.) After the mammary gland was cut, the person was almost
recovered, and the result was suddenly deteriorated doctor’s diagnosis may be up to October.
I also lamented that life is too unpredictable, my cousin said that people who are often sulking
are prone to it, because if you are sulking, there will be a lot of dirty things that can’t comeout and become tumors in the chest over time, Kam, so it is important to be happy, this istrue. Not happy to say. Hard to cry. Stressed on eating, drinking and having fun. Don’t makethe last of their body and mind are damaged.

cation)（注 6）, which was established based on various consul-

categories. The Shizuoka Cancer Classiﬁcation is one of thetations from cancer patients. There are 16 broad categories

most complete classiﬁcation systems in Japan and the cate-as shown in Table 6, multi subcategories were set under eachgories can cover almost all cancer patients’ needs [22].

major category, each major category has up to fourth level

In [22], the Shizuoka Cancer Classiﬁcation was used to classify questions by breast cancer patients from Yahoo! JAPAN（注 6）：https://www.scchr.jp/cancerqa/start\_shizuoka.html

question and answer service, Yahoo! Chiebukuro. They suc-

Table 6 The Shizuoka Cancer Classiﬁcation categories
Code

Description

1

Outpatient2

Hospitalization/discharge/transfer

3Diagnosis/Treatment

4

Palliative care

5

Notice/Informed Consent/Second Opinion

6

Medical cooperation

7

Home care

8

Facilities/equipment/access

9

Relationship with medical staﬀ (current hospital)

10

Relationship with medical staﬀ (former hospital)

11

Symptoms/side eﬀects/aftereﬀects

12

Mental problems such as anxiety

13

Way of life, purpose in life, sense of values

14

Employment/ﬁnancial burden

15

Relationships with family and other people

16

Prevention of cancer, cancer screening, suspicion of cancer

cessfully classiﬁed 6,993 questions to fourth level categories,
with a correct rate of more than 70%.

Figure 5 Patient concerns’ distribution. (a) Japanese case [22]and (b) Chinese caseIn this case study, we classify the needs of Chinese breast
cancer patients into the the Shizuoka Cancer Classiﬁcation
categories. Then, we compare the results with the Japanese
ones in [22] as a reference for Japanese patients with breast
cancer. Speciﬁcally, we have randomly selected 100 Weibo
posts from our classiﬁed post user category. Taking the
Shizuoka Cancer Classiﬁcation categories as the classiﬁcation criteria, we manually annotated each post with one label. As the Shizuoka Cancer Classiﬁcation categories are too
detailed, we only considered 16 broad classiﬁcation criteria
as shown in Table 6.
This annotation was completed by one of the authors who
was a native Chinese speaker. To verify if this annotation
work was fair and unbiased, another native Chinese speaking
annotator repeated the same work based on the same standard. We adopted Cohen’s Kappa [19] to verify agreement,
resulting in 0.837581 (high agreement).
In order to keep the same pace with Chinese Weibo posts,
we converted all Japanese data’s predicted label into 16
broad categories as shown in Table 6. We visualized both

Code 11 (Symptoms/side eﬀects/aftereﬀects), and Code
15 (Relationships with family and other people) and account for 40.86%, 19.46%, 14.33%, 6.72%, and 4.42%,
respectively.

For Chinese breast cancer patients, thetop 5 categories are Code 3 (Diagnosis/Treatment), Code2 (Hospitalization/discharge/transfer), Code 10 (Relationship with medical staﬀ (former hospital)), Code 8 (Facilities/equipment/access), and Code 4 (Palliative care) andaccount for 30%, 21%, 15%, 14%, and 11%, respectively.
Even though Chinese posts were not large enough in thiscase study, we still could identify the tendency when breast
cancer patients talk about their disease. Chinese patientswere more interested in treatment, diagnosis, and treatmentrelated topics, whereas Japanese patients were more concerned with cancer prevention, emotional distress, and family relationships, that went beyond the treatment of breast
cancer itself.

7 Conclusion

distributions of Japanese patients’ concerns and Chinese patients’ concerns in Figure 5 based on the same categories
criteria in Table 6.
In Figure 5(b), 100 posts were distributed into 8 of the
16 categories.

Therefore, the bar chart of 8 other cate-gories account for 0. Despite all this, we can draw a conclusion from a broad comprehensive horizon between Japanese
breast cancer patients and Chinese breast cancer patients.
In terms of the distribution ratio, for the Japanese patients, the top 5 categories are Code 16 (Prevention of cancer, cancer screening, suspicion of cancer), Code 12 (Mental
problems such as anxiety), Code 3 (Diagnosis/Treatment),

This study focused on Weibo posts about breast cancerrelated topics. We used posts published by individual users
on Chinese Weibo as a corpus, to classify posts by individualcentered categories based on proximity to close relationships.
We used the latest NLP methods in machine learning to implement. Our basic binary classiﬁer showed high accuracy(more than 90% F1 score), and for the further multiclassclassiﬁcation, our classiﬁer also exceeded 80% in F1 score on
average.
Based on our methods and results, when it comes to further study posts on topics related to breast cancer, we canunderstand how the post author’s relationship distance with

[2] Nathan S Consedine, Carol Magai, Yulia S Krivoshekova,

the cancer patient can potentially inﬂuence their opinions

Lynn Ryzewicz, and Alfred I Neugut. Fear, anxiety,about and emotional responses to breast cancer itself. For

worry, and breast cancer screening behavior: a criticalexample, when the cancer patient is post user herself, thereview. Cancer Epidemiology Biomarkers & Preven-content of the post may be more about her own treatment,tion, 13(4):501–510, 2004.

relief, or personal concerns. When the cancer patient in thepost is his or her mother, wife, or other family members, thecontent of the post may be about a kind of sadness, grief, or
personal feelings or expectation. However, when the patientin the post is someone less close to the author, the content
of the post may be more of a rational statement or a simple[3] Xinyan Zhao, Xiaohui Wang, Zexin Ma, and Rong Ma.
Primacy eﬀect of emotions in social stories: User engagement behaviors with breast cancer narratives on
facebook. Computers in Human Behavior, 137:107405,
2022.

description of the story. As mentioned above, our research[4] Edel M Quinn, Mark A Corrigan, Seamus M McHugh,method provides a valid reference direction for further re-David Murphy, John O’Mullane, Arnold D Hill, andsearch on breast cancer-related topics in the future.

Henry Paul Redmond.

At the same time, there are some limitations in our study,
for example, the amount of data is not large enough, so even

Who’s talking about breast

cancer? analysis of daily breast cancer posts on theinternet. The Breast, 22(1):24–27, 2013.

the presence of small biases can have a signiﬁcant impact on
the ﬁnal prediction results. Data labeling is done manually,
so it is time-consuming and prone to human error. Since
the data are collected from public social networks, the data
structure is not uniform, which brings a lot of diﬃculties in
the cleaning and analyzing process. Also, because the data

[5] Tânia Brandão, Rita Tavares, Marc S Schulz, andPaula Mena Matos. Measuring emotion regulation andemotional expression in breast cancer patients: A systematic review. Clinical Psychology Review, 43:114–
127, 2016.

structure is very diﬀerent, too many diﬀerent attempts are[6] Patti M. Valkenburg. Social media use and well-being:needed to select the classiﬁer model, which makes the re-What we know and what we need to know. Current

search more diﬃcult.

Opinion in Psychology, 45:101294, 2022.

In the future research, we can try to unify the data structure. The distance scores we currently deﬁne (0-5) might[7] Ankit Murarka, Balaji Radhakrishnan, and Sushma

be too coarse, a more scientiﬁc and reasonable classiﬁcation

Ravichandran. Detection and classiﬁcation of mental

deﬁnition would be required. The data can be labeled andillnesses on social media using roberta. arXiv preprint

processed in a crowd-sourced manner to reduce human er-

arXiv:2011.11226, 2020.

ror. This will ensure that the data can be classiﬁed more

[8] S Anand and K Narayana. Earthquake reporting sys-

accurately, which is good for subsequent in-depth researchtem development by tweet analysis. Int. J. Emerg. Eng.

studies on breast cancer-related topics. We can get more

Res. Technol, 2:96–106, 2014.

speciﬁc valuable ﬁndings by focusing on each diﬀerent category. This may provide useful references for medical health,human care, social services, and other relevant ﬁelds.

[9] Xinyue Ye, Shengwen Li, Xining Yang, and Chenglin
Qin. Use of social media for the detection and analysis of infectious diseases in china. ISPRS International

Acknowledgements
This work was supported by JST-Mirai Program Grant
Number JPMJMI21J2 and JST CREST Grant Number JPMJCR22N1, Japan.

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Research

on credit evaluation model of online store based on