=Paper=
{{Paper
|id=Vol-1996/paper4
|storemode=property
|title=Characteristics of Zika Behavior Discourse on Twitter
|pdfUrl=https://ceur-ws.org/Vol-1996/paper4.pdf
|volume=Vol-1996
|authors=Ashlynn R. Daughton,Dasha Pruss,Brad Arnot,Danielle Albers Szafir,Michael J. Paul
|dblpUrl=https://dblp.org/rec/conf/amia/DaughtonPASP17
}}
==Characteristics of Zika Behavior Discourse on Twitter==
https://ceur-ws.org/Vol-1996/paper4.pdf
Characteristics of Zika Behavior Discourse on Twitter
Ashlynn R. Daughton, MPH1,2 , Dasha Pruss1 , Brad Arnot1 , Danielle Albers Szafir, Ph.D.1 ,
Michael J. Paul, Ph.D.1
1
University of Colorado, Boulder, Boulder, CO; 2 Los Alamos National Laboratory, Los
Alamos, NM
Abstract
Zika is an important emerging illness that has been linked to neurological syndromes in adult patients, and birth
defects in patients infected in-utero. Here, we use Twitter to explore the discourse of individuals tweeting about Zika.
We labeled a sample of 500 English tweets to identify common themes, used keywords to track two themes, reproduction
and travel, and identify spatial patterns in tweets based on the language of the tweet. We also observed tweets made in
the first person that might be indicative of reflections of personal behavior on Twitter. Future work will delve further
into first person tweets and continue to examine spatial and temporal trends in the themes temporally observed.
Introduction
Mosquito borne arboviruses (most notably dengue, chikungunya, and, now, Zika) cause massive outbreaks worldwide.
In 2015 Zika emerged in South America, and caused a large outbreak throughout many countries in South, Central and
North America.1–3 Although the majority of Zika cases are asymptomatic4 or cause mild disease, evidence emerged
that there was an association between infection and neurological syndromes (e.g., Guillian-Barré5 or birth defects
in fetuses infected in-utero.6 ) Specifically, women who were infected with Zika during pregnancy had fetuses with
much higher rates of microcephaly, a condition where the head circumference of the fetus is very small (below the
second percentile for gestation7 ), and has accompanying brain defects.6 The intersection between this outbreak and
the impacts on human behavior are of substantial public health relevance. Social media data provide one way to gain
insight into human behavior with respect to well publicized public health events.
Social media data now has a rich history of use understanding health-related human behavior. Previous studies have
used Twitter as a general way to understand patterns in human behavior with respect to possible risk factors for disease.
Paul and Dredze used Twitter data to identify types of ailments discussed on Twitter.8 Others identified tweets related
to human behaviors designed to control infection. For example, Signorini et al. found that an important minority of
persons talking about H1N1 on Twitter, also talked about possible control measures (hand hygiene and mask wearing).9
As Zika emerged, researchers again used Twitter to find relevant patterns. McGough et al. used a combination of
official incidence reports and Internet data streams (including Twitter) to build Zika forecasts for several Central and
South America countries.10 Stefanidis et al. used data from the first 3 months of the outbreak to look at spatial clusters
in discussions of Zika on Twitter.11 They also found tweets referencing pregnancy and abortion.11
This study builds upon previous work by looking at trends in discourse. We present preliminary evidence that examines
temporal, spatial and keyword trends in a larger Zika Twitter corpus, and analyzes the demographics of Twitter users
that talked about Zika. These analyses provide a discussion of Zika tweet content at a finer resolution, and broader
spatial context than previous work.
Methods
Data collection
Tweets were collected from Gnip, based on the keywords “zika”, “zica” (a common Portuguese spelling of the virus1 ,
or ‘zikv’ (a common abbreviation of ‘Zika Virus’). Tweets were collected from March 1, 2015 until October 31, 2016.
This resulted in just under 15.5 million tweets, 7 million of which are in English. For this initial work, we only coded
English tweets. We identified the likely gender of person tweeting using Demographer12 , and identified the likely
location of the tweets using Carmen.13
1 https://en.wiktionary.org/wiki/zica
Data labeling
This initial work has focused on the relationship between Zika and behavioral decisions (e.g., those relating to repro-
duction, travel, mosquito interventions etc.), as well as to important global events (e.g., 2016 Olympics). We identified
a list of keywords related to these behaviors (see Table 1). We then filtered the dataset for tweets that included the
keywords. Where applicable we included different plural forms (-ies rather than -s endings). For words that might
match longer words or strings (e.g., ‘birth’ would match ‘birthday’), we included \b, a Python regular expression
marker for a word boundary.
Table 1: Behavior keywords. Keywords were identified by reading 500 hundred random English tweets and identi-
fying words that were commonly associated with behaviors. Keywords were translated into Spanish and Portuguese
using Google translate (translations not shown for brevity).
Keywords
pregnancy pregnancies microcephaly microcephalies brain babies baby \bbirth\b
defect pregnant condom \bsti\b std sex\b travel female
abortion syndrome guillain barre repellent spray olympic fetal
carnival autoimmune born pope catholic ultrasound transmission transmit
abnormality abnormalities reconsider cancel protect paralyze paralyzing church
conceive spring break baby-moon paralysis terminate
Table 2: Label frequency and example tweets. Tweets were included in frequency if both annotators used the label.
Code & Definition Example (Paraphrased to preserve privacy) Freq Cohen’s
(%) κ
Travel discussion changing plans, sharing information CDC says pregnant women should consider delay- 34 0.47
about travel advisories ing travel (6.8%)
Sex and/or safe sex behaviors: contraceptive methods Men, if you’re partner is pregnant, abstain or use 13 0.64
or comments about sexual transmission. condoms to prevent zika (2.6%)
Delaying pregnancy: advisories to delay pregnancy, or Delay pregnancy if you live in a place with zika 17 0.65
use methods to prevent pregnancy. (5%)
Reproductive rights: legal and personal opinions Americans weigh in on late-term abortions in 14 0.41
about abortion or birth control use. cases of zika-linked defects (3.4%)
Birth defects: birth defects associated with Zika Baby born with zika-related birth defect 56 0.65
(11.2%)
Paralyzing syndromes: Guillian-Barré or other para- Guillain-Barré cases associated with #Zika infec- 17 1.0
lyzing syndromes tion rise (3.4%)
Intervention strategies ways to interrupt transmission Country to spray for mosquitos to prevent zika 37 0.56
(e.g., mosquito spraying) (7.4%)
Olympics or large group gatherings information Olympic athlete decides to not compete over zika 68 0.78
about very large events (e.g., Olympic games, Carnival fears (13.6%)
etc.)
Misleading jokes, references to conspiracy theories that Brazilian government admits microcephaly not 8 0.38
are factually inaccurate caused by zika (1.6%)
The resulting subset included 3,572,320 tweets. Of these, we randomly sampled 500 for coding. Labels were decided
iteratively as a group, with the final labeling schema available in Table 2. Tweets were coded independently by two
team members. Multiple labels per tweet were allowed. Labels were assigned based solely on the content of the tweet;
we did not attempt to look at the context of any URLs linked, or find additional context from that user’s other tweets.
Inter-rater reliability is presented in Table 2 (see Cohen’s κ).
Behavior threads
Lastly, we performed initial explorations of two specific types of tweets. Because our initial labeled corpus of 500 is
too small to build a robust classifier system, we identified a few keywords that related (1) reproduction/ birth control
(keywords: ‘birth control’ ‘abortion’) and (2) travel (‘travel’ ‘cancel’ ‘plane\b’ ’airline’). Keywords were used in
lieu of more sophisticated methods as a crude approximation of topics of public health relevance to begin to identify
temporal trends in topics. These topics were chosen because they relate to the two most common types of advisories
observed - recommendations about limiting travel and delaying pregnancy.
Results
Demographics and label frequencies
Within the behavior Twitter corpus, we have a somewhat higher proportion of male tweeters (61%) compared to female
tweeters (37%) (the remaining 2% could not be identified). This is in contrast to surveys that suggest that men and
women use Twitter at roughly the same frequencies.14 We were able to predict a user’s location for 61% of tweets.
The majority came from the United States (20%). Venezuela, United Kingdom, Brazil, Canada, and India contributed
1-3% each. 179 additional countries had at least 1 tweet present in the dataset. Additional spatial analyses are available
in Figure 2.
Labels, descriptions and example tweets are listed in Table 2. The most common label in our random sample was
‘birth defects’, followed by discussions of the Olympics, travel, ‘intervention strategies’, safe sex behaviors and ‘re-
productive rights’.
We also noticed a rare, but interesting handful of tweets that were written in first person. These are an interesting
subset because they provide insight into individual’s opinions on policy decisions, or reactions to guidelines on health
behaviors, beyond what might be inferred by noticing what kinds of informational things an individual retweets or
links to. A paraphrased example is ‘Canceling our baby-moon trip due to Zika concerns’.
Trends in behavior themes
Figure 1 shows the fraction of behavior tweets each day that have keywords matching reproduction or travel keywords
(see section Behavior threads).
Figure 1: Initial behavior trends for the three categories of words. Blue tweets are those that talk about reproduction
and birth control and orange are tweets about travel. Annotations come from articles in the popular news media.15–17
Dates are formatted yyyy-mm-dd.
These keywords are not meant to be exhaustive representations of discourse, but are useful to identify general patterns
to explore more in future work. Several spikes within the dataset seem to correspond to policy discourse in the US
and have been added as annotations onto Figure 1. Overall, travel tweets are much more common than those about
reproduction. Given the intensely personal and political nature of abortion and reproductive rights, this is unsurprising.
However, further investigation into the content of these tweets is warranted.
Spatial trends
Figure 2 shows the number of geotagged tweets by language in each location by month between August 2015 and June
2016. Unsurprisingly, the volume of tweets in each language strongly correlates with the primary language spoken
there. In general, tweets were rare in the first few months of the outbreak, and began increasing primarily among
Portuguese speakers November and December 2015. In January and February 2016 the number of tweets increased
primarily among Spanish and Portuguese speakers in Central and South America, possibly because the outbreak was
extremely active at that time. In contrast, there is an increase in English tweets throughout the spring and summer of
2016, possibly in accordance with increased media attention to the outbreak.
Figure 2: Spatial context, August 2015 - June 2016. The area of the bubble is directly proportional to the number of
tweets. Tweets are divided by language - ‘en’ corresponds to English, ‘pt’ to Portuguese, ‘es’ to Spanish.
Conclusion
This work describes common themes in Zika-related tweets, as well as temporal and spatial trends in those tweets.
Future work will employ other techniques, like topic modeling and named entity recognition to identify topics dis-
cussed within each theme identified. Additional focus will be on first person tweets because they provide insights into
people’s individual decisions to change behavior. Although first person tweets were rare, we found tweets describing
the decision to change travel plans based on advisories from public health organizations. Analysis of these tweets
will allow us to understand how public health agencies can better use Twitter to communicate important public health
information.
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