# Desaparecidxs: characterizing the population of missing children using
Twitter

Carolina Coimbra Vieira  Max Planck Institute for Demographic
ResearchRostockGemany<EMAIL_ADDRESS>,  Diego Alburez-Gutierrez
Max Planck Institute for Demographic ResearchRostockGemany
<EMAIL_ADDRESS>,  Marília R. Nepomuceno  Max Planck Institute
for Demographic ResearchRostockGemany<EMAIL_ADDRESS>and  Tom Theile
Max Planck Institute for Demographic ResearchRostockGemany
<EMAIL_ADDRESS>
(2022; 2022)

###### Abstract.

Missing children, i.e., children reported to a relevant authority as having
“disappeared,” constitute an important but often overlooked population. From a
research perspective, missing children constitute a hard-to-reach population
about which little is known. This is a particular problem in regions of the
Global South that lack robust or centralized data collection systems. In this
study, we analyze the composition of the population of missing children in
Guatemala, a country with high levels of violence. We contrast the official
aggregated-level data from the Guatemalan National Police during the 2018-2020
period with real-time individual-level data on missing children from the
official Twitter account of the Alerta Alba-Keneth, a governmental warning
system tasked with disseminating information about missing children. Using the
Twitter data, we characterize the population of missing children in Guatemala
by single-year age, sex, and place of disappearance. Our results show that
women are more likely to be reported as missing, particularly those aged
13-17. We discuss the findings in light of the known links between missing
people, violence, and human trafficking. Finally, the study highlights the
potential of web data to contribute to society by improving our understanding
of this and similar hard-to-reach populations.

population, missing children, disappearance, social media data, Twitter

††copyright: acmcopyright††journalyear: 2022††journalyear: 2022††copyright:
rightsretained††conference: 14th ACM Web Science Conference 2022; June 26–29,
2022; Barcelona, Spain††booktitle: 14th ACM Web Science Conference 2022
(WebSci ’22), June 26–29, 2022, Barcelona, Spain††doi:
10.1145/3501247.3531589††isbn: 978-1-4503-9191-7/22/06††ccs: Social and
professional topics User characteristics††ccs: Networks Social media networks

##  1\. Introduction

Quantitative researchers often encounter ‘missing’ values in their data, for
instance, derived from measurement error or non-response. ‘Missingness’ in
real life, however, is an altogether different phenomenon. Missing people are
individuals, whose status as dead or alive is unknown, reported as missing by
relatives and friends. The Wall Street Journal reported in 2012 that: “It is
estimated that some 8 million children go missing around the world each
year”111https://www.wsj.com/articles/SB10001424052702304707604577424451609727644.
However, missing people have received surprisingly little attention in the
literature. This may be partly due to the fact that governments do not always
collect and release up-to-date data on missing people (Citroni, 2014) and
disappearances are relatively rare in high-income
countries222http://amberalert.eu/statistics/.

Missing people are a particular concern in enclaves of the Global South in
which populations are exposed to high levels of economic uncertainty, poverty,
violence, and conflict (McIlwaine and Moser, 2001; Soares Filho, 2011;
Meneghel and Hirakata, 2011; García and Aburto, 2019). We focus on the case of
Guatemala, which emerged from a bloody 36-year civil war in 1996 (CEH, 1999).
Over the last decades, the country has experienced growing levels of violence
related to gang activity and drug trafficking (Cruz et al., 2020) that
alongside high levels of poverty, have contributed to large-scale population
displacement and migration (Jonas and Rodríguez, 2015).

Aggregated records from the National Civilian Police are one of the few data
on missing people in Guatemala. According to these data, almost 40,000
individuals went missing in the country between 2003 and 2020, half of whom
were under 18 years of age. Between 2018 and 2020, the period we focus on in
this study, 4,000 individuals went missing in Guatemala, 65% of whom were
children (0-17 years old). The police data, however, provide neither a full
nor an updated picture of the population of missing people. The reasons why a
person disappears may also be associated with age, gender, race, socioeconomic
level, and contextual factors. However, little is still known about the
characteristics of the missing people.

In this work, we adopt a methodology for collecting Twitter data in a
systematic way, including image processing techniques to extract text from
images, to track the population of missing children in Guatemala in real-time.
We collected individual-level data of more than seven hundred missing children
from the official Twitter account of the _Alerta Alba-Keneth_ , a governmental
warning system tasked with disseminating information about missing children in
Guatemala (Rodas Andrade, 2021).

We combine the Twitter data with official data sources to provide the first
systematic description of the population of missing children in Guatemala
during the 2018-2020 period. This work addresses the following research
question: What is the composition of the population of missing children in
Guatemala by age, sex, and geography, and how has this varied over time?

To the best of our knowledge, this is the first work studying the demographic
composition of missing children using Twitter data. We focus on Guatemala, but
the methodology we adopt in this work can be replicated for other Twitter
accounts sharing detailed information about missing people. Moreover, this
work highlights the advantages of combining social media data combined with
official government data to study phenomena of societal relevance.

The rest of the paper is organized as follows. In Section 2, we describe some
related work. In Section 3, we detail the methodology used to collect and pre-
process the data. The results are presented in Section 4. In Section 5, we
present the discussion of our results and offer additional comments about the
applicability, limitations, and future work on using social media data to
study missing children. Finally, our conclusions are presented in Section 6.

##  2\. Related Work

Existing studies have documented how individuals affected by the disappearance
of an acquaintance use social networks to crowdsource support. Despite the
fact that there is no guarantee that reporting missing people online will lead
to their discovery, social networks provide much needed emotional support to
friends and relatives of the missing people (Hattingh and Matthee, 2016).
Police departments around the world increasingly use social media to engage
the surrounding communities using social media platforms (Dai et al., 2017).

Exploratory studies have examined the use of social media tools by police
departments, with a particular focus on Twitter (Crump, 2011; Ferguson and
Soave, 2021; Solymosi et al., 2021). Most of these studies have centered on
understanding how to increase the public’s engagement with the information on
missing people shared online. In (Ferguson and Soave, 2021), the authors
analyzed 373 missing person tweets posted over two years (2017–2019) from 15
Canadian police services on Twitter to estimate which features are likely to
increase public engagement (retweets, likes, and comments) with these tweets.
Similarly, (Solymosi et al., 2021) analyzed 1008 Tweets made by Greater
Manchester Police between the period of 2011 and 2018 in order to investigate
what features of the tweet, the Twitter account, and the missing person are
associated with levels of retweeting. In both studies, the authors found
several features to be significantly associated with higher engagement, such
as the use of images and hashtags. These strategies increased community
outreach and participation, as well as the likelihood of efficiently and
successfully solving the missing person cases. A standardized structure for
sharing details of missing people on Twitter may enhance the usefulness of
social media in this respect (Ferguson and Soave, 2021).

In this work, we collect information about missing children from the official
Twitter account of Alerta Alba-Keneth, a governmental warning system focused
on missing children in Guatemala. The main goal of this work is to
characterize the population of missing children, instead of predicting the
best features responsible for a high engagement with tweets. Moreover, we do
not expect variations between engagement due to different features used to
tweet (e.g., the use of different hashtags) since the Alerta Alba-Keneth
Twitter account is highly structured and all the tweets and images shared have
standardized components.

Finally, by focusing on the characterization of the missing children in
Guatemala, we expect to provide a demographic overview of the missing child,
minimizing the race- and gender-related media bias (e.g., African American
missing children and female missing children being significantly
underrepresented in television news coverage) as reported by other authors
(Min and Feaster, 2010). To the best of our knowledge, this is the first work
studying the demographic composition of missing children using Twitter data.

##  3\. Data

###  3.1. Guatemalan National Police data

Missing people in Guatemala can be directly reported to three government
offices: the National Civilian Police (Policía Nacional Civil), the Prosecutor
General’s Office (Ministerio Público), or the Attorney General’s Office
(Procuraduría General de la Nación). Each of these offices produces its own
statistics, which means that reports of disappearances are often duplicated
across sources. In our experience, accessing these data is very difficult
given the unwillingness of the relevant authorities to share it.

We obtained data on missing people in Guatemala via multiple Freedom of
Information Requests to the National Civilian Police. We made similar requests
to a number of other government agencies that collect reports of missing
people, but this information was refused. The data from the National Civilian
Police cover the 2003-2021 period, but our analyses are limited to the
2018-2020 period. The National Police data only includes disappearances
reported to this institution directly.

According to the National Police, the population under 18 years of age
represents more than half of the known cases of missing people in the country.
These data include the number of missing people reported to the police by
broad age groups (0-17; 18-35; 36-55; 56+) and month of occurrence. However,
the police authorities did not share individual-level data or aggregated
information on the composition of the population by age, sex, or ethnicity.
Other details about the circumstances of the events (e.g., place of
disappearance) were also unavailable.

###  3.2. Twitter data

Twitter is a popular microblogging platform that allows users to express
themselves and record their thoughts in 280 characters at maximum. Twitter is
also a social network since it is possible to follow users to be updated about
their tweets (which may contain text, photos, GIFs, videos, and links). Among
its more than 200 million daily active
users333https://s22.q4cdn.com/826641620/files/doc_financials/2021/q2/Q2’21_InvestorFactSheet.pdf
are news outlets, academic institutions, and government agencies who use the
platform to share official information with a wide audience. We are
particularly interested in the latter’s use of Twitter to share information on
missing people. The data provided by Twitter has been used extensively by
researchers in social science (McCormick et al., 2017; Tsoi et al., 2018).

We are interested in leveraging the use of Twitter by a Guatemalan government
agency to gather detailed data on missing children, i.e. those under 18 years
old according to Guatemalan law. We focus on the Alerta Alba-Keneth444Official
website available here: https://www.albakeneth.gob.gt/, an inter-governmental
agency tasked with the search, location, and immediate protection of
disappeared or abducted children and adolescents. Alerta Alba-Keneth was
established in 2010 to coordinate the efforts of multiple government agencies
for locating missing children, including the National Civilian Police, the
Prosecutor General’s Office, and the Attorney General’s Office. It collects
reports on missing children made to any of these agencies in addition to those
made directly to Alerta Alba-Keneth through their website and hotline, making
it the most comprehensive source of data on missing children in the country
(Rodas Andrade, 2021).

The Alerta Alba-Keneth Twitter account, set up in 2015, has high visibility
with almost 20 thousand followers. The @alba_keneth profile tweets images with
information about newly missing children on a daily basis, following the
structure of the image shown in Figure 1.

We used the Twitter API Academic Research product
track555https://developer.twitter.com/en/products/twitter-api/academic-
research to download all the tweets and their embedded images of the Alerta
Alba-Keneth account since 2015. Regular tweets about missing children in a
consistent format started in 2018. Every tweet consists of a short text and an
image. The text contains information about the name, age, date, and place of
disappearance. The image contains not only a portrait photo of the missing
child, but also a ”profile” with more information in text form, including sex,
appearance or individual characteristics (e.g., eyes color, hair color, and
height) and contact information.

We collected a total of 13,696 tweets containing images. Considering the fact
that some images are tweeted more than one time, by comparing the
hash666https://docs.python.org/3/library/hashlib.html of each image, all
duplicated images have been removed. We processed all these 11,130 unique
images to extract information related to the demographic attributes of the
sample. We used Optical Character Recognition (OCR) via the Python package
PyTesseract to extract information on the children’s demographic attributes
(date and place of disappearance, age, and sex) from images. This information
was not always available in the tweet text and when it was included, it was in
an unstructured form. Thus it was easier to extract this information from the
structured images. At the end of this process, we collected structured
information about 7,800 unique missing children. Of 7,800 unique missing
children, 6,875 correspond to tweets from 2018 or later and as such included
in our study.

In order to extract the coordinates of the place of disappearance, we used the
address reported in the “place of disappearance” field as input for the two
geocoding services of Bing777https://docs.microsoft.com/en-us/bingmaps/rest-
services/ and ArcGIS888https://geocode.arcgis.com/arcgis/. We calculated the
Haversine distance between the two results for an extra description of the
accuracy of the address geolocation.

Figure 1. Example of a tweet from the Alerta Alba-Keneth Twitter account
(@alba_keneth).

###  3.3. Data protection

All data used in this study have been made publicly available on Twitter by
the Alerta Alba-Keneth account. Nevertheless, data protection was a major
concern when collecting and processing Twitter data. We recognize that the
data are sensitive because they concern minors going through a difficult
situation that also affects their families. For this reason, we kept the data
in an encrypted drive and, even though we conducted individual-level analysis,
this article includes only aggregated results. Moreover, in the text, we
eliminated all personally-identifying information so that no child or relative
could be uniquely identified.

##  4\. Results

In this section, we explore time and demographic trends of missing children
reports in Guatemala for the 2018-2020 period. The main results come from
Twitter data from the Alerta Alba-Keneth account. Whenever possible, we
compare these results to “official” data obtained from the National Police. It
is worth emphasizing that although both sources refer to the same phenomenon –
missing children reports – we do not expect them to match perfectly. This is
due to the fact that both institutions have independent, though complementary,
data collection systems, as explained above. Nevertheless, we expect that both
data sources will reflect similar trends over time, even if absolute numbers
differ.

As a first step, we contrast the number of missing children reported by the
Twitter data to the official police data over the period of study. Figure 2
shows the number of children reported as missing according to both data
sources. The image shows a general correspondence in the general trends of
child disappearances over time. Although both series show similar fluctuations
over time, the number of disappearances is higher in the Twitter data for most
of 2019 and lower for 2020. Note that the latter does not necessarily reflect
a falling number of disappearances – the decline may be related to the
Covid-19 pandemic (Martinez-Folgar et al., 2021). According to a recent report
by the Ombudsman of Guatemala (Rodas Andrade, 2021), strict lockdowns, more
restricted opening hours, and reduced access to public transportation may all
have contributed to limiting the number of missing children reports received
by Alerta Alba-Keneth in 2020.

The measure presented in the paragraph above can be regarded as a rough
measure of incidence. That is, the monthly number of disappearances is a proxy
of how common it is for children to go missing. An advantage of the
individual-level Twitter data is that it allows us to desegregate the
population of missing children by age and sex. The police data do not include
information on the distribution of missing children for different combinations
of age and sex. This is crucial in order to identify the populations at the
highest risk of disappearances and is particularly useful given the known
links between missing children and human trafficking (Rodas Andrade, 2021).

Figure 2. Number of missing children by month: a comparative overview of data
from the Guatemalan National Police and Alerta Alba-Keneth Twitter data. Note
that data for May-December 2019 were not provided by the National Police.

Figure 3 shows the distribution of the missing minors by three age sub-groups
intended to represent young children (0-4), children (4-12), and adolescents
(13-17). The top panels show the absolute number of monthly reports of missing
children and the bottom panels show the age composition of the population of
missing children every month. The first thing to note is that the age
distributions remain relatively constant over time (bottom panels) even as the
absolute number of reported cases fluctuates visibly over time. Indeed, this
is true even for the 2020 period, in which the number of missing children
reports was heavily affected by the Covid-19 pandemic (Rodas Andrade, 2021).
The figure also shows that adolescents constitute the largest group of missing
children in the period we study. The proportions of young children and
children are both considerably lower and similar to each other.

The most striking finding of Figure 3 is the disparities by sex. Focusing on
the bottom panels, we see that there are important differences between the age
distribution of missing male and female children. Concretely, missing girls
tend to be considerably older than missing boys–around 75% of missing girls
were over 13 years old whereas only about 60% of missing boys were
adolescents. Female adolescents are the largest sub-population of missing
children and were almost twice as likely to be reported as missing as boys the
same age.

Figure 3. Age and sex distribution of the number of missing children by month
of reported disappearance (2018 - 2020) according to the Alerta Alba-Keneth
Twitter data.

Figure 4 summarizes the stock of missing children in the 2018-2020 period by
age and sex. The image confirms the patterns discussed above and shows the
large disparities in the number of missing male and female children. The
number of women aged between 10 and 17 years was 4,200, more than twice as
high as the number of men reported as missing in the same age group (1,800).
As expected, the ratio between the number of adolescent women and men is
largest for older ages. There are 2.7 times more women aged 15-17 reported as
missing than men of the same age. This large gap between sexes is absent for
younger age groups (e.g., 870 girls younger than 10 years were reported as
missing, compared to 850 boys).

Figure 4. Stock of missing children: cumulative number of disappearances (2018
- 2020) according to the Alerta Alba-Keneth Twitter data. Figure 5. Geographic
distribution of the number of reported missing children (2018 - 2020)
according to the Alerta Alba-Keneth Twitter data. Location data were extracted
from the “Place of disappearance” field of the tweet.

Next, we consider the geographic distribution of the disappearance events. We
obtained this crucial piece of information from the Twitter data since
equivalent individual-level information is not shared by the National Civilian
Police. Figure 5 shows the location of the disappearance of missing children
reported by the Alerta Alba-Keneth Twitter account between 2018 and 2020. On
the map, each individual disappearance is represented as a dot. Note that dots
may not correspond to the exact locations since we applied a small degree of
random noise to the location data (less than 0.001°) to avoid overplotting in
areas with a high concentration of missing reports.

We highlight four important spatial patterns from this figure. First, missing
children reports are concentrated in urban centers, more prominently in the
capital city, which is home to about 20% of the country’s population. Second,
we find a smaller concentration of missing reports in the country’s Highlands,
located in the Central and Western parts of the country. This is the region
where most of the indigenous population, which correspondents to approximately
half of the country’s total, is concentrated. It is also the region with the
highest rates of poverty in the country, according to data from the National
Institute of Statistics999https://www.ine.gob.gt/ine/vitales/. Third, we find
a higher concentration of missing cases in the south of the country. This area
is mostly populated by non-indigenous groups and is generally characterized by
higher levels of violence. Fourth, we document a high incidence of
disappearance events close to the country’s boundaries. This is true for the
eastern border with Mexico, the northwestern border with Belize, and the
southeastern border with Honduras and El Salvador. In addition to this, we
find a high number of disappearances in the country’s only port on the
Atlantic Ocean (Puerto Barrios, in the west of the country). These are all
regions characterized by high levels of insecurity and in which bands of human
traffickers are known to operate. These are worrying patterns that deserve
more attention.

We do not have access to the number of children that are found after having
been reported as missing from the Alerta Alba-Keneth. Nevertheless, there are
reasons to believe that the number of children that are found after having
gone missing is very small. According to police data obtained from this study,
the number of children that are found constitutes around 5% of the cases
reported every year, on average. In addition, parents may not notify the
authorities of a child’s “re-appearance” for a number of reasons, including
fear of losing custody of the child (Evelyn Espinoza, personal communication
19 September 2019).

##  5\. Discussion

The population of missing children is a relevant social issue in many
societies, particularly in those where the levels of violence, drug, and human
trafficking are high. Due to the lack of detailed information on this
population, our study leveraged Twitter data in order to characterize missing
children by age, sex, and place of disappearance. Here, we presented results
from Guatemala, however, the methodology for collecting data from tweets can
be replicated for other countries.

Our results showed a remarkable difference in the number of disappearances by
sex and age. Female adolescents are the most likely group to go missing. The
number of missing girls was about two times higher than that of boys, and
around 75% of missing girls were between 13 and 17 years old. We provide the
first set of estimates on the specific sex and age of individuals more likely
to be reported as missing in Guatemala by leveraging real-time and individual-
level data from a social media platform. By highlighting the striking number
of missing girls aged 13-17, our results suggest that sex trafficking is a
potential mechanism behind disappearances in Guatemala, which corroborates
with the known link between disappearances and human trafficking (Rodas
Andrade, 2021). In Guatemala, 45% of the detected victims of trafficking in
2019 are girls. Of the female victims, 70% are between 14 and 17 years old (on
Drugs and Crime, 2021).

However, due to the high levels of violence, particularly among men which,
according to the National Police in Guatemala, correspond to 86% of the
victims of homicide in 2020101010https://infosegura.org/seccion/guatemala/,
one might see our results by sex as surprising. If violence was the main
mechanism behind missing children, boys would have gone missing more than
girls. Our results showed the opposite. A possible explanation for this might
be the age range we analyzed here. Violent deaths are less concentrated among
children (10-17 years old) when compared to individuals aged
18-30111111https://infosegura.org/2022/02/04/homicidios-guatemala-2021/. Thus,
our results suggest that violence might not be the main mechanism behind
disappearances in Guatemala.

This study also showed the spatial patterns of disappearance. Our findings
revealed a higher number of reported cases in urban centers. The regions with
higher levels of poverty and where most are indigenous had lower numbers of
missing children. This result may reflect a potential limitation of our study:
the level of reporting cases may vary across the country. A lower presence of
State institutions in the poorest areas may reduce the number of reported
missing cases.

Our study has a number of limitations. First, the data we present, both from
Twitter and from official sources, are reliant on missing reports. We are
unable to observe cases of missing children that were not reported. Second, we
are not able to establish a comparison between the datasets on an individual-
level, once only the Twitter data are available on an individual-level. In
this case, we cannot identify the subset of disappeared children that appear
in both datasets, or those cases that were not reported to the police, for
example, but appear in the Twitter data. Third, it is possible that the Alerta
Alba-Keneth Twitter account may not have tweeted all the missing person cases
reported to them. Fourth, our automated geocoding is subject to a small degree
of error arising from ambiguous place names (e.g., incorrect spelling). In
future work, we plan to use regular expressions to identify other cases of
disappearances discussed on Twitter, apart from those reported to the
authorities or tweeted by Alerta Alba-Keneth.

##  6\. Conclusion

In this paper, we showed how digital data can be of great help to better
understand social problems. Our study provided a detailed picture of missing
children in Guatemala. We showed that the female adolescents are the most
likely group to go missing, suggesting that sex trafficking is a potential
mechanism behind disappearances in Guatemala given the high concentration of
human traffic among girls between 14 and 17 years old. We propose that, in
order to reduce the incidence of disappearances in Guatemala, authorities must
improve protection for young girls. Moreover, greater efforts are needed to
collect data on missing children in low-income settings, where missing
children are a particularly prevalent and detrimental social problem.

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