Sentiment Analysis of Breast Cancer Screening in the

United States using Twitter

Kai O. Wong

, Faith G. Davis

, Osmar R. Zaïane

and Yutaka Yasui

1,3

School of Public Health, University of Alberta, Alberta, Canada

Department of Computing Science, University of Alberta, Alberta, Canada

St. Jude Children’s Research Hospital, Memphis, U.S.A.

Keywords: Cancer Screening, Social Media, Data Visualization, Sentiment Analysis, Spatial Analysis, Twitter.

Abstract: Whether or not U.S. women follow the recommended breast cancer screening guidelines is related to the

perceived benefits and harms of the procedure. Twitter is a rich source of subjective information containing

individuals’ sentiment towards public health interventions/technologies. Using our modified version of Hutto

and Gilbert (2014) sentiment classifier, we described the temporal, geospatial, and thematic patterns of public

sentiment towards breast cancer screening with 8 months of tweets (n=64,524) in the U.S. To examine how

sentiment was related to screening uptake behaviour, we investigated and identified significant associations

between breast cancer screening sentiment (via Twitter) and breast cancer screening uptake (via BRFSS) at

the state level.

1 INTRODUCTION

Breast cancer is the most prevalent cancer among

women in the United States (U.S.) (American Cancer

Society, 2015). Regular breast cancer screening is

important in detecting early stages of breast tumors.

Screening mammogram, clinical breast exam (CBE)

performed by health professionals, breast self-exam,

and breast magnetic resonance imaging (MRI) are

examples of breast cancer screening tests. A

systematic review concluded that among women with

average risk (i.e., no personal or family history of

breast tumor/lesion, or genetic mutations such as

those in BRCA1 and BRCA2 genes), mammogram

screening was associated with 20% reduction in

breast cancer mortality (Myers et al., 2015). The

American College of Obstetricians and

Gynecologists (2011) guidelines recommended U.S.

women aged 40-74 with average risk to attend a

screening mammogram and CBE annually, while the

U.S. Preventive Services Task Force (2016) added

that the net benefits of breast cancer screening for

women aged 40 to 49 is less conclusive than that for

women aged 50 to 74 (Centers for Disease Control

and Prevention, 2016b). Women aged 75 and above

with average risk should consult with physician to

decide whether or not to continue receiving a

mammogram.

Not all U.S. women adhere to the recommended

breast cancer screening guidelines. The uptake of

breast cancer screening vary across residence location

(Mai et al., 2009), social class (Borugian et al., 2011),

and ethnicity (Mahamoud, 2014). Whether or not to

seek breast cancer screening often depended on one’s

perception regarding the quality of care, competency

of health professionals, discomfort level during the

procedure, and length of time waiting for the

procedure and test results (Cruz-Castillo et al., 2014).

Women not attending regular breast cancer screening

listed their main reasons as being busy, unaware of

breast cancer risk, fearful of receiving a true cancer

diagnosis or a false diagnosis, and deterred by the

pain and discomfort from the procedure

(HealthTalkOnline, 2013). Many of these reasons can

be explained by the health belief model (HBM) (Janz

and Becker, 1984) which states that individuals’

readiness and commitment to adopt or continue a

healthy behaviour are built on four perception-based

constructs: perceptions of susceptibility, severity,

benefits, and barriers. Individuals’ subjective

perception about breast cancer screening, including

influence of face-to-face physician recommendation

and perceived effectiveness and safety of breast

cancer screening (Fulton et al., 1991, Wang et al.,

2014, Austin et al., 2002), plays a crucial role in

determining if a woman would participate in the

procedure. Yet real-time and unfiltered perception

data on medical procedures are often unavailable in

Wong, K., Davis, F., Zaïane, O. and Yasui, Y.

Sentiment Analysis of Breast Cancer Screening in the United States using Twitter.

DOI: 10.5220/0006047102650274

In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 265-274

ISBN: 978-989-758-203-5

265

public health surveillance, administrative, and other

health-related databases (Bryson et al., 2016).

Twitter is a rich data source of perception data.

Twitter is used by hundreds of millions of active

users, continuously broadcasting their uncensored

opinions, experiences, thoughts, and feelings in a

form of a tweet, a short text message of 140 characters

or less (PewResearchCenter, 2015, Zhao and Rosson,

2009). A considerable portion of tweets is health-

related (Dredze, 2012, Paul and Dredze, 2011) and

has contributed to various health monitoring

applications such as public awareness of influenza

(Smith et al., 2015), worldwide influenza incidence

(Paul et al., 2015), self-reported mental illnesses

(Coppersmith et al., 2015), medical complaints

(Nakhasi et al., 2012), and safety monitoring for

hospital patients (Passarella et al., 2012). As for

cancer communities, Twitter serves as a popular

digital platform to bring together different groups of

key stakeholders. Medical professionals use Twitter

to disseminate scientific findings and connect with

patients (Vance et al., 2009). Cancer patients use it to

share experience, gain support, and educate one

another (Lapointe et al., 2014, Sugawara et al., 2012).

The general public uses it to advocate and raise

funding (Thackeray et al., 2013). Currently, no study

was found to examine Twitter’s potential in gauging

public perception on preventive public health

interventions such as breast cancer screening.

Sentiment analysis is a sub-domain of natural

language processing that extracts subjective

information from a text and assigns a sentiment score

or a sentiment polarity classification (i.e., neutral,

positive, and negative) (Pang and Lee, 2008).

Sentiment analysis helps determine the attitude or

perception of a writer with respect to a specific topic

in a systematic and quantifiable manner. We propose

a sentiment analysis that not only demonstrates the

visualization of sentiment patterns using breast

cancer screening tweets in the U.S. (descriptive

analysis), but also explores the relationship between

breast cancer screening sentiment from Twitter and

actual breast cancer screening uptake behaviour

derived from an external data source from the U.S.

government (hypothesis-based analysis).

2 METHODS

2.1 Tweet Processing

Twitter allowed public access to 1% random subset

of tweets via Twitter REST Application

Programming Interface (API) (Kumar et al., 2013).

Via Twitter API, tweets related to breast cancer

screening published from 17

September 2014 to 10

May 2015 were collected using the following filtering

terms:

"mammogram", "mammography", "breast imaging",

"breast screening", “breast mri”, “breast

ultrasound”, "breast self-exam", "breast

examination", "breast exam", and their

corresponding hashtags (i.e., “#breastimaging” and

“#breastexam”)

Extracted information from each breast cancer

screening tweet included user name, time of tweet,

published tweet content, and two types of geographic

information including user-described location and

user-enabled global positioning system (GPS)

location in longitude and latitude (Twitter, 2014).

The content of each tweet was processed by

removing any retweet tag (“RT”), hashtag symbol

(“#”), user-mention tag (“@”), and Uniform Resource

Location (URL) links. Not all Twitter users have

described location information or enabled the GPS

option. If both location inputs were available, the

more precise GPS location was used; otherwise the

user-described location was used. When available,

the user-described location was converted into GPS

coordinates using Python module Geocoder (by

accessing MapQuest) (MapQuest, 2014). The

location information was then standardized by

reverse-geocoding the coordinates into corresponding

country, state, county, and city.

2.2 VADER Sentiment Classifier

There are a number of existing automated sentiment

classifiers (Hutto and Gilbert, 2014), such as

Linguistic Inquiry and Word Count (LIWC), General

Inquirer (GI), Affective Norms for English Words

(ANEW), SentiWordNet (SWN), SenticNet (SCN),

Word-Sense Disambiguation (WSD), and Hu-Liu-

2004. However, these sentiment classifiers were not

developed specifically for microblogging platforms

such as Twitter. Tweets generally employed unique

communication patterns (i.e., hashtag, user-mention,

all-capitalization, acronyms, emoticons, slangs, and

repeated punctuations) to better express emotions and

fit into the microblogging culture. Hutto and Gilbert

(2014) developed and made publically available a

sentiment classifier, called Valence Aware

Dictionary for sEntiment Reasoning (VADER)

classifier, specifically tailored to microblogging

platforms such as Twitter. The sentiment lexicon of

VADER classifier was based on well-established and

human-validated sentiment lexicons (i.e., from

KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval

266

LIWC, GI, and ANEW) and extended by adding

common microblogging vernaculars (i.e., acronyms,

slangs, and emoticons). In addition, grammatical and

syntactical aspects of text (i.e., use of repeated

punctuation such as “!!!!” and all-cap such as

“EXTREMELY GOOD day”) were incorporated by

systematically adjusting the baseline sentiment value

using a rule-based model (Hutto and Gilbert, 2014).

To classify the sentiment of a text, the VADER

classifier examines the sentiment polarity and

intensity of each word of the text against its lexicon,

and then outputs four VADER sentiment scores:

neutral, positive, negative, and composite scores. The

neutral, positive, and negative scores correspond to

the proportion of text containing a particular

sentiment polarity. For example, a 1.0 positive

sentiment score indicates that every word in a text

contains positive sentiment while 0.0 positive score

indicates there is no positive word, and likewise for

neutral and negative sentiment scores. The composite

score is computed by summing the sentiment

intensity score of each word from the text that has a

match with the VADER lexicon, adjusted with

grammatical and syntactical rules, and then

normalized to be between -1 (most negative) and +1

(most positive). The composite score can be used as a

single uni-dimensional measure of sentiment. Hutto

and Gilbert (2014) concluded the VADER classifier

considerably outperformed all seven established

sentiment classifiers (i.e., LIWC, GI, and ANEW).

The VADER classifier achieved a 0.99 precision,

0.94 recall, and 0.96 F1 score, which were

comparable to human accuracy.

2.3 Modifications of VADER

Although VADER was validated on general tweets by

Hutto and Gilbert (2014), its performance to classify

sentiment of tweets related to public health

intervention, specifically breast cancer screening,

required further validation. Such validation was

conducted in our study by drawing a random subset

of 250 tweets from the original breast cancer

screening tweets pool. The composite score was

categorized into neutral (-0.3 to +0.3), positive (>

+0.3 to +1.0), and negative (-1.0 to < -0.3). The

sentiment polarity (neutral, positive, and negative) of

each of the 250 tweets was determined by a blind-

rater K.W. as the gold standard. A poor accuracy

(<40.0%) was observed from the VADER

classification initially and the primary reason was

identified.

In the original VADER lexical dictionary, the

lexicon “cancer” contained a highly negative

sentiment value (-3.4). This resulted in VADER

universally assigned highly negative composite

sentiment score to virtually all tweets since they were

related to breast cancer by default. Similarly, other

words including “die”, “died”, and “death”

containing highly negative default sentiment values

(i.e., -2.9, -2.6, and -2.9, respectively) were identified,

yet these lexicons often appeared in our collected

tweets as part of the conversations on breast cancer

statistics without any default positive or negative

connotation. The effect on sentiment classification

accuracy was examined by removing these four

lexicons from the original lexical dictionary, resulting

in more favourable accuracy (77.2%). The remaining

classification discrepancy between VADER and the

human rater was derived from more advanced

sentiment classification challenges such as sarcasm,

sentiment ambiguity, and mixed sentiments that were

difficult for even human raters, and thus unlikely to

be addressed by further minor modifications in the

VADER classifier. The aforementioned modified

version of the VADER classifier was used to compute

sentiment scores of breast cancer screening tweets.

2.4 Descriptive Sentiment Analysis

Temporal, geospatial, and thematic patterns of

sentiment from breast cancer screening tweets were

examined as descriptive sentiment analyses. For

temporal patterns, the daily volume of breast cancer

screening tweets and daily average of composite

sentiment scores were plotted in a line graph.

For geospatial patterns, tweets with available

geographic information were used to generate

cartographic and hot-spot maps based on composite

sentiment scores. Hot-spot analysis identifies spatial

clusters with significantly high or low sentiment

values, using the Getis-Ord Gi* statistics (ArcGIS,

2015):



















 

































 

















 



where,

















































Gi* statistics is calculated at each location point i that

has a feature (sentiment) value. The x

is the sentiment

value for feature j, w

i,j

is the spatial weight between

features i and j, and n is the total number of features.

Inverse square distance is used such that closer

features are weighted more heavily than features that

Sentiment Analysis of Breast Cancer Screening in the United States using Twitter

267

are further away. That is, w

i,j

is equal to M/(d

where M is a constant and d

is the distance between

features i and j. Conceptually, Gi* statistics compares

the sum of feature values within a neighbouring

region around location i against the expected sum of

feature values derived from global average

(numerator), and then standardized with the variance

(denominator). The Gi* statistics returns a z-score for

each location i. Significant hot-spots contain highly

positive z-score value and small p-value, indicating

location i is surrounded by high sentiment value

neighbours, while significant cold-spots contain

highly negative z-score and small p-value, indicating

location i is surrounded by low sentiment value

neighbours.

For thematic patterns, an example word-cloud

was generated which consisted the most frequent

words amongst all and only negative tweets

(excluding positive and neutral tweets). A

comprehensive list of common but non-informative

words such as “the”, “it”, and “what” were omitted

from the word-cloud creation. The font size of each

word shown in a word-cloud corresponded to the

frequency of that word (i.e., the larger the word, the

more frequently it appears). Example themes were

extracted qualitatively as a demonstration.

2.5 Hypothesis-based Sentiment

Analysis

To evaluate possible association between breast

cancer screening sentiment and actual breast cancer

screening uptake at an ecological level, a hypothesis-

based sentiment analysis was conducted. While

information on breast cancer screening sentiment was

provided by Twitter, information on breast cancer

screening uptake was obtained from a separate dataset

collected by the Centers for Disease Control and

Prevention (CDC) called the Behavioral Risk Factor

Surveillance System (BRFSS) survey (Centers for

Disease Control and Prevention, 2016a). The BRFSS

is one of the largest recurring national health surveys

that collects data via phone interviews on U.S.

residents regarding health-related risk behaviours,

chronic health conditions, and use of preventive

services. During the study period, the latest available

BRFSS survey was the BRFSS 2014 (calendar year).

From the BRFSS 2014, interested individual-level

variables were extracted and recoded as 1)

mammogram received within the last two years

(Mamm, 1 – yes and 0 – no), 2) CBE received within

the last two years (CBE, 1 – yes and 0 – no), 3)

highest education achieved (Edu, 1 – have at least

some college education, 0 – do not have any college

education), 4) general health (GenHlth, 1 – good, very

good, or excellent, 0 – fair or poor), and 5) race (Race,

1 – non-Hispanic white only, 0 – all others). Women

aged less than 40 years old, women with missing key

variables (i.e., mammogram and CBE), and men were

removed from the analysis. Explanatory and outcome

variables were aggregated by states, where individual

sentiment values were grouped as averages by state

(i.e., 



, 



, 



, and 



), and

individual BRFSS variable values were aggregated as

percentage of “1” for each state (i.e., %Mamm as

percent women reported having a mammogram

within two years, and likewise for %CBE, %Edu,

%GenHlth, and %Race). We hypothesized that U.S.

states with more positive sentiment score values

towards breast cancer screening (via tweets) are more

likely to have higher overall uptake of breast cancer

screening (via BRFSS). This hypothesis was

examined qualitatively and quantitatively.

Qualitatively, the cartographic maps of state-level

breast cancer screening sentiment and breast cancer

screening uptake patterns were compared.

Quantitatively, since the values of the dependent

variables (%Mamm and %CBE) fall between 0 and 1,

beta regression model was used to statistically test the

relationship between sentiment scores and

mammogram/CBE uptake. States with less than 100

tweet count (including Hawaii, Vermont, and

Montana) were excluded from the analysis.

In multivariable beta regression, the outcome

variable was either %Mamm or %CBE, and the

explanatory variable of interest was one of the four

average VADER sentiment scores (i.e., 







, 



, and 



) plus other

covariates including %Edu, %GenHlth, and %Race to

adjust for potential confounding. Beta regression

assumes y

(i.e., %Mamm or %CBE), for k=1,2,…,

state

(number of individual U.S. states), to be

distributed in a beta distribution whose probability

density function is given as:











































  











where Γ is the gamma function, and 0<y<1, 0<u<1,

and z>1. The u is the mean and z is the precision

parameter. The systematic component of beta

regression is:





























 





















 









 









where E(y

)=u

is the expected value of y

, or mean

, in each state. It is linearly linked to the explanatory

variables via the logit link function, g

(u)=log(u/(1-

u)). The random component of beta regression states

that y

is distributed in a beta distribution with its

KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval

268

mean specified as above and z as a constant. The

estimation of β and z was done by maximum

likelihood estimation.

3 RESULTS

3.1 Descriptive Analysis

There were 3,544,328 breast cancer-related (both

breast cancer screening and non-breast cancer

screening) tweets collected in the data collection

period. A total of 61,524 tweets were found to be

specifically related to breast cancer screening in the

U.S., and 54,664 of these tweets contained specific

geographic information allowing for spatial analysis.

The baseline daily breast cancer screening tweet

volume fluctuated between 100 and 200, with an

explosive volume started in the beginning of October

(also Breast cancer awareness month) and then

gradually declined back to baseline (Fig 1). For the

remaining portions of this paper, “sentiment score”

refers to “composite sentiment score” unless

specified otherwise. There were 29,034 neutral (-0.3

≤ sentiment score ≤ 0.3), 21,561 positive (sentiment

score > 0.3), and 4,069 negative (sentiment score < -

0.3) tweets. The daily average sentiment score was

above the zero line during almost the entire period,

indicating that the overall sentiment towards breast

cancer screening was neutral-to-positive (Fig 1).

Figure 2 depicts the location and sentiment

polarity classification of each breast cancer screening

tweets. A larger volume of tweets was published in

the eastern states, which coincided with states with

higher population densities (MapOfUSA, 2007). The

states with the highest volumes of breast cancer

screening tweets were California, Texas, New Jersey,

Ohio, and Illinois in decreasing order (9,640 ≤ n

≤

2,639). The states with the lowest volumes of breast

cancer screening tweets were Vermont, Montana,

Hawaii, Wyoming, and South Dakota in increasing

order (71 ≤ n

≤ 145).

Figure 3 shows hot-spot analysis using individual

composite sentiment scores, regions in red (99%

confidence), orange (95% confidence), and light pink

(90% confidence) were statistically significant

clusters of low sentiment value, and they were named

the cold-spots. Regions in dark green (99%

confidence), medium green (95% confidence), and

light green (90% confidence) were significant

clusters of high sentiment values, and they were

named the hot-spots. While cold-spots appeared to be

occurring throughout the country with higher

concentration on the eastern side of the country, hot-

spots rarely appeared on the western side of the

country (with exceptions occurring in the south-

western coast of the country).

Three quintile maps as average sentiment score,

percent of recent mammogram, and percent of recent

CBE by states are shown in Figure 4. The top quintile

map ranked states by their average composite

sentiment score (with red being the lowest and green

being the highest) based on tweets, with three states

having less than 100 tweet counts removed. The

bottom left quintile map depicts the percent of women

aged 40 and above who had received a mammogram

within the last two years, and the bottom right quintile

map depicts the percent of women aged 40 and above

who had received a CBE within the last two years,

according to BRFSS 2014. Qualitatively, the bottom

Figure 1: Temporal trends of breast screening tweet volume and sentiment in the U.S. (n

=54,664).

Sentiment Analysis of Breast Cancer Screening in the United States using Twitter

269

Figure 2: Sentiment of breast screening tweets in the U.S. (n

=54,664).

Figure 3: Hot Spot map based on sentiment score value in the U.S. (n

=54,664).

two maps show similar and strong horizontal

gradient, lowest to highest quintiles from west to east

across the country. While not as identical and

prominent compared to the bottom maps, the

sentiment-based quintile map does show an

increasing gradient from western to eastern part of the

country.

The word-cloud using only negative breast cancer

screening tweets is shown in Figure 5. Some of the

key words were circled manually and grouped

together thematically by a human inspector (K.W.)

subjectively. For example, “discomfort”, “scary”,

“hate”, “hurt”, “forced”, and “pain” together

suggested many people might feel negatively about

breast cancer screening due to the perceived or

experienced physical and psychological discomfort

with the procedure. On the other hand, “Obamacare”,

“coverage”, “excluded”, “cost”, “insurance”, and

“access” together might suggest many people with

negative sentiment about breast cancer screening

viewed inaccessibility and financial obstacles as

deterrence of obtaining a breast cancer screening.

3.2 Hypothesis-based Analysis

Ecological association between each of the four

average sentiment scores on breast cancer screening

and outcome variables (i.e., %Mamm and %CBE)

KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval

270

Figure 4: Quintile maps of average sentiment score of breast screening tweets (n

=54,416, top), percent women aged ≥40

years with recent mammogram (n

BRFSS

=217,503, bottom left), and percent women aged ≥40 years with recent CBE

BRFSS

=217,503, bottom right).

were explored using multivariable beta regressions

(Table 1). States with less than 100 tweets were

removed, including Hawaii, Vermont, and Montana.

At the state level, 



is the average composite

sentiment score, 



is the average neutral

sentiment score, 



is the average positive

sentiment score, 



is the average negative

sentiment score, %Mamm is the percent women (≥40

years) received mammogram within two years,

Figure 5: Word-cloud using only negative breast cancer

screening tweets in the U.S. (n

=4,069).

and %CBE is the percent women (≥40 years) who

have received CBE within two years. Significant

positive association (p<0.05) was observed between





and recent CBE uptake, and significant

negative associations (p<0.05) were observed

between 



and recent mammogram uptake and

recent CBE uptake. For example, 10% increase in

average negative sentiment score was associated with

0.57 and 0.75 decrease in log odds of recent-

mammogram-uptake being “yes” and recent-CBE-

uptake being “yes”, respectively, adjusted for

education, general health, and race.

Table 1: Regression coefficients from multivariable beta

regression examining average sentiment scores and

outcome variables of recent mammogram and CBE uptakes

by states (n

state

=48).

















%Mamm

-0.01 (-1.89

to 1.78)*

2.69 (-0.17

to 5.56)

-2.46 (-

7.06 to

2.13)

-5.65 (-

10.84 to -

0.47)

%CBE

0.65 (-1.20

to 2.50)

3.27 (0.47

to 6.08)

-2.45 (-

7.00 to

2.10)

-7.53 (-

12.47 to -

2.58)

*β-coefficient (95% C.I.) adjusted for education, general health,

and race.

Sentiment Analysis of Breast Cancer Screening in the United States using Twitter

271

4 DISCUSSION

This study demonstrated how Twitter might serve as

a potentially useful tool in fulfilling public health

needs that require data on public perception. Twitter

provides a rich source of real-time, instantaneous, and

uncensored public perception data, which may be

utilized to monitor public sentiment towards health

interventions/technologies. The descriptive sentiment

analysis illustrated how Twitter depicts temporal,

geospatial, and thematic patterns of sentiment.

Temporally, the quantity and average sentiment

typically fluctuated within a baseline range, which

can help detect instances with abnormal level of tweet

volume and/or sentiment score value. Cartographic

and hot-spot maps visualized general geographical

trends and specific clusters based on sentiment

values, respectively. A vast number of negative

sentiments in a location towards breast cancer

screening might indicate an underlying public

misconception, unaddressed concerns, ineffective

health promotion, or lack of accessible infrastructure.

Thematically, qualitative interpretation of a word-

cloud revealed potentially important thematic

elements that might lead to better understanding of

the root causes of the observed sentiment in the whole

country or specific regions.

In the hypothesis-based sentiment analysis,

significant associations were found between some of

the average sentiment scores (via Twitter) and actual

mammogram and CBE uptake behaviours (via

BRFSS 2014) at the state level. Average negative

sentiment scores were negatively associated with

mammogram and CBE uptakes, as expected.

However, positive association was not observed

between average composite and positive sentiment

scores and breast cancer screening uptakes. This

might be due to several methodological and data-

limitation challenges: for example, data in Twitter

and BRFSS did not overlap over the exact time

period; subjects in these data sources did not

represent the same individuals (i.e., Twitter users

might not be representative to the target general

population); relationship existed at the ecological

(state) level could be different from those of the

individual level; uptake behaviours influenced by

factors other than sentiment could be at play; certain

states only had a small numbers of tweets; and

positive tweets published by commercial or non-

commercial organizations rather than individuals

might not link to individuals’ uptake patterns. Some

of these Twitter data limitations were also mentioned

by other studies including (Paul and Dredze, 2011),

(Mitra et al., 2016), and (Brooks, 2014). Nonetheless,

our finding suggested the existence of meaningful

associations that negative sentiment tweets on breast

cancer screening might be particularly useful in

identifying or predicting regions with lower breast

cancer screening uptake.

We suggest future studies to develop strategies to

minimize background noise such as tweets published

by organizations instead of individuals, and examine

more fine-grained categorization of sentiment that

also captures a person’s feelings and moods such as

anger, worry, disgust, fear, happiness, surprise, and

sadness (Pulman, 2014). Future studies may also

explore and validate a systematic approach to add

public health- and/or cancer-specific lexicons into the

existing VADER’s sentiment lexical dictionary to

further improve its context-specific utility in public

health and/or cancer research.

5 CONCLUSIONS

Based on the health belief model, one’s perception

about a health intervention/technology could

influence one’s ultimate action in adopting it. Twitter

sentiment data may fill an important gap by providing

health researchers and other stakeholders real-time

and unfiltered data essential to gauge public

perception on health interventions/technologies. The

knowledge of such public perception might help

predict subsequent utilization in the population. This

study not only demonstrated the use of Twitter to

visualize rich breast cancer screening sentiment

information, but also linked the sentiment derived

from Twitter to actual breast cancer screening uptake

patterns from BRFSS 2014. This suggests that

knowledge about public perception of health

intervention/technology might help predict future

public utilization, which holds important values in

public health policy development, community

planning, and resource allocation.

With better understanding and distillation of

useful tweets from the background noise, Twitter

could potentially be used as a uniquely important

public health surveillance tool to monitor public

perception. Spatial clusters with highly negative

sentiment should be monitored closely over time and

the reasons for their negative sentiment might be

extracted using thematic tools such as word-cloud.

Specific programs or policies can be tailored in

attempt to alleviate the specific negative sentiment,

which may subsequently improve public acceptance

and utilization of a target health intervention/

technology.

KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval

272

REFERENCES

American Cancer Society. (2015). Cancer facts and figures

2015. Atlanta: American Cancer Society.

ArcGIS. (2015). How optimized hot spot analysis works.

Environmental Systems Research Institute, Inc. URL

http://desktop.arcgis.com/en/desktop/latest/tools/spati

al-statistics-toolbox/how-optimized-hot-spot-analysis-

works.htm.

Austin L., Ahmad F., McNally M., Stewart D. (2002).

Breast and cervical cancer screening in Hispanic

women: a literature review using the health belief

model. Women's Health Issues, 12, 122-128.

Borugian M., Spinelli J., Abanto Z., Xu C., Wilkins R.

(2011). Breast cancer incidence and neighbourhood

income. Health Reports. Statistics Canada.

Brooks B. (2014). Using Twitter data to identify geographic

clustering of anti-vaccination sentiments. Master of

Public Health, University of Washington.

Bryson E., Schafer E., Salizzoni E., Cosgrove A., Favaro

D., Dawson R. (2016). Is perception reality? Identifying

community health needs when perceptions of health do

not align with public health and clinical data. SM

Journal of Community Medicine, 2, 1013.

Centers for Disease Control and Prevention. (2016a).

Behavioral risk factor surveillance system. Atlanta,

GA: CDC. URL http://www.cdc.gov/brfss/.

Centers for Disease Control and Prevention. (2016b).

Breast cancer screening guidelines for women. Atlanta,

GA: Centers for Disease Control and Prevention. URL

http://www.cdc.gov/cancer/breast/pdf/BreastCancerSc

reeningGuidelines.pdf.

Coppersmith G., Dredze M., Harman C., Hollingshead K.

(2015). From ADHD to SAD: analyzing the language

of mental health on Twitter through self-reported

diagnoses. NAACL Workshop on Computational

Linguistics and Clinical Psychology.

Cruz-Castillo A., Hernández-Valero M., Hovick S.,

Campuzano-González M., Karam-Calderón M.,

Bustamante-Montes L. (2014). A study on the

knowledge, perception, and use of breast cancer

screening methods and quality of care among women

from central Mexico. Journal of Cancer Education.

Dredze M. (2012). How social media will change public

health. IEEE Intelligent Systems, 27, 81-84.

Fulton J., Buechner J., Scott H., DeBuono B., Feldman J.,

Smith R., Kovenock D. (1991). A study guided by the

health belief model of the predictors of breast cancer

screening of women ages 40 and older. Public Health

Reports, 106, 410-420.

HealthTalkOnline. (2013). Reasons for not attending breast

screening. URL http://www.healthtalk.org/peoples-

experiences/cancer/breast-screening/reasons-not-

attending-breast-screening.

Hutto C., Gilbert E. (2014). VADER: a parsimonious rule-

based model for sentiment analysis of social media text.

Association for the Advancement of Artificial

Intelligence.

Janz N., Becker M. (1984). The health belief model: a

decade later. Health Education Quarterly, 11, 1-47.

Kumar S., Morstatter F., Liu H. (2013). Twitter data

analytics, Springer.

Lapointe L., Ramaprasad J., Vedel I. (2014). Creating

health awareness: a social media enabled collaboration.

Health and Technology.

Mahamoud A. (2014). Breast cancer screening in racialized

women: implications for health equity. Advancing

Urban Health. Wellesley Institute.

Mai V., Sullivan T., Chiarelli A. (2009). Breast cancer

screening program in Canada: successes and

challenges. Salud Publica Mex, 51, S228-S235.

MapOfUSA. (2007). US population density map. URL

http://www.mapofusa.net/us-population-density-

map.htm.

MapQuest. (2014). Geocoding API. URL

https://developer.mapquest.com/products/geocoding.

Mitra T., Counts S., Pennebaker J. (2016). Understanding

anti-vaccination attitudes in social media. Tenth

International AAAI Conference on Web and Social

Media. AAAI.

Myers E., Moorman P., Gierisch J., Havrilesky L., Grimm

L., Ghate S., Davidson B., Mongtomery R., Crowley

M., McCrory D., Kendrick A., Sanders G. (2015).

Benefits and harms of breast cancer screening: a

systematic review. JAMA, 314, 1615-1634.

Nakhasi A., Passarella R., Bell S., Paul M., Dredze M.,

Pronovost P. (2012). Malpractice and malcontent:

analyzing medical complaints in Twitter. AAAI Fall

Symposium on Information Retrieval and Knowledge

Discovery in Biomedical Text.

Pang B., Lee L. (2008). 4.1.2 Subjectivity detection and

opinion identification. Opinion mining and sentiment

analysis. Now Publishers Inc.

Passarella R., Nakhasi A., Bell S., Paul M., Pronovost P.,

Dredze M. (2012). Twitter as a source for learning

about patient safety events. Annual Symposium of the

American Medical Informatics Association (AMIA).

Paul M., Dredze M. (2011). You are what you tweet:

analyzing Twitter for public health. International

Conference on Weblogs and Social Media (ICWSM).

Paul M., Dredze M., Broniatowski D., Generous N. (2015).

Worldwide influenza surveillance through Twitter.

AAAI Workshop on the World Wide Web and Public

Health Intelligence.

PewResearchCenter. (2015). Social media update 2014.

Pew Research Center. URL http://

www.pewinternet.org/2015/01/09/social-media-

update-2014/.

Pulman S. (2014). Multi-dimensional sentiment analysis.

Oxford: Dept. of Computer Science, Oxford University.

URL http://www.lt-

innovate.org/sites/default/files/lt_accelerate_files/13.3

0%20Stephen_Pulman_UNIV_OXFORD.pdf.

Smith M., Broniatowski D., Paul M., Dredze M. (2015).

Tracking public awareness of influenza through

Twitter. 3rd International Conference on Digital

Disease Detection (DDD).

Sugawara Y., Narimatsu H., Hozawa A., Shao L., Otani K.,

Fukao A. (2012). Cancer patients on Twitter: a novel

Sentiment Analysis of Breast Cancer Screening in the United States using Twitter

273

patient community on social media. BMC Research

Notes, 5, 699.

Thackeray R., Burton S., Giraud-Carrier C., Rollins S.,

Draper C. (2013). Using Twitter for breast cancer

prevention: an analysis of breast cancer awareness

month. BMC Cancer, 13, 508.

Twitter. (2014). Geo guidelines. Twitter. URL

https://dev.twitter.com/overview/terms/geo-developer-

guidelines.

Vance K., Howe W., Dellavalle R. (2009). Social internet

sites as a source of public health information.

Dermatologic Clinics, 27, 133-136.

Wang W., Hsu S., Wang J., Huang L., Hsu W. (2014).

Survey of breast cancer mammography screening

behaviors in Eastern Taiwan based on a health belief

model. Kaohsiung Journal of Medical Sciences, 30,

422-427.

Zhao D., Rosson M. (2009). How and why people Twitter:

the role that micro-blogging plays in informal

communication at work. 243-252.

KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval

274