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EXPERIMENTING WITH
BIG DATA AND ARTIFICIAL
INTELLIGENCE TO SUPPORT
PEACE AND SECURITY
SOMALIA
THANKS TO THE GENEROUS
CONTRIBUTION OF:
Government
of Belgium
Government of SwedenGovernment
of The Netherlands
Government
of Denmark
United Nations Development
Operations Coordination Office
United Nations
Peacebuilding
Support Office
TABLE OF CONTENTS
Acknowledgements 2
Executive Summary 3
Analysing people’s voices to achieve peace and security 5
Testing new methods of analysis 7
Experimenting with social media mining in Somalia 7
Data mining process 8
Representativeness and biases 9
The influencers analysis 9
The analysis of fake news 11
The analysis of trending topics 14
Experimenting with radio content analysis in Uganda 15
Data mining process 15
Representativeness and biases 17
Detecting rumors and misconceptions 18
Detecting social tensions 19
Detecting reports that can cause social alarm 20
Developing a new generation of analysis tools: QataLog 22
Introducing ethics to analyse people’s voices 25
Bibliography 27
1
ACKNOWLEDGEMENTS
This publication would not have been possible without the leadership, commitment and
investment of many colleagues from Pulse Lab Kampala and the UN Global Pulse network.
The report was authored by Paula Hidalgo-Sanchis, PhD, Manager of Pulse Lab Kampala.
UN Global Pulse would like to thank the generous contributions from the Governments of
Sweden, Denmark, The Kingdom of the Netherlands, The Kingdom of Belgium, the UN
Peace Building Support Office (PBSO) and the UN Development Group (UNDOCO) that
funded this work.
HOW TO CITE THIS DOCUMENT:
UN Global Pulse (2018). Experimenting with Big Data and Artificial Intelligence to
Support Peace and Security.
2
“…Online social networking lets us find like-minded
people around the world, enlarging free speech and human
creativity. But it also amplifies hate speech, contributes to
ethnic and political polarization, and facilitates terrorist
recruitment… .
United Nations Secretary-General’s
Strategy on New Technologies.
”
United Nations Development Programme (2017).
1
3
EXECUTIVE SUMMARY
Sustainable development is built on the foundations of a peaceful, just and inclusive
society and institutions
1
. New types of war, conflict and violence challenge traditional
ways of doing analysis to support peace and security efforts. Small data and methods of
analysis used with small data cannot respond to these new challenges. We need to
strengthen our analytical capacities, use new types of data and develop new methods.
UN Global Pulse has been working - through its lab in Kampala - with partners using big
data to support progress on SDG16: Promote Just, Peaceful, and Inclusive
Societies. We explored the utility of analysing data from social media and public radio
broadcasts to extract insights to feed early warning systems and inform peace and
security processes. The experimentation process led to producing technology tools to
analyse people’s voices. The application of the technology tools resulted in the
development of new methods of analysis.
This report outlines the experimentation process, methods of analysis, results and lessons
learned from two test cases. The first test case used data mined from social media,
namely posts from public Facebook pages and groups, to analyse how influencers and
fake news might be shaping discussions among online users in Somalia and to identify
trending topics relevant to SDG 16. The second test case analysed public discussions on
radio broadcasts to detect instances of rumors and misconceptions, social tensions
and testimonials that cause social alarm in Uganda.
The report also details the functionalities of a new tool, named QataLog, developed by UN
Global Pulse to help extract, analyse and visualise data from public social media and radio
shows. The tool can be used in different scenarios, including to inform humanitarian
and peace efforts.
New technologies and new digital applications, if used responsibly, hold tremendous
potential to help us achieve the 2030 Agenda for Sustainable Development. More and
more compelling examples illustrate the value of technology to improve early warning
systems and inform policy and programmatic response. Yet, as adoption of big data and
artificial intelligence (AI) increases and technology evolves, so do the potential risks and
issues that need to be resolved.
The final section of the report outlines the data privacy, data protection and ethics
principles that guide the work with big data and AI at UN Global Pulse, from
conceptualization to development of technology and implementation.
4
ANALYSING PEOPLE’S
VOICES TO ACHIEVE PEACE
AND SECURITY
The 2030 Agenda states that ‘there can be no sustainable development without peace and
no peace without sustainable development ’
2
. The definition of SDG16 breaks new ground
in articulating and emphasizing the purpose and outcomes of good governance in
development. Sustainable development is built on the foundations of a peaceful, just and
inclusive society and institutions
3
.
“…Preserving peace has become more complicated because of an increase in violence
no longer perpetrated exclusively by national security forces and conventional armed
oppositions but also by an increasingly wider and assertive range of hybrid actors…their
impact is so significant that the violence resulting from these unconventional players
exceeds that of many ongoing civil wars and must be added to the role of revolutionary
mass movements such as the popular uprisings in the Arab region… .
Conflict Analysis Handbook. A Field and Headquarter Guide to Conflict Assessment
4
.
New types of war, conflict and violence challenge traditional ways of doing analysis to
support peace and security efforts. Small data and traditional methods of analysis
commonly used with small data are no longer sufficient to respond to these new
challenges. We need to strengthen our analytical capacities, use new types of data and
develop new methods that can provide more granular, real-time and comprehensive
information.
Quantitative and qualitative traditional means for gauging public sentiment, such as
surveys, focus group discussions and informant’s interviews, often require a great amount
of resources and time and as a consequence, people’s sentiments might have long
changed by the time that insights are actioned by policy makers. In addition, security
constraints in conflict or post conflict areas limit the movement of analysts that stay
confined in secured locations with reduced capacity to interact with the local population.
Also, analysts frequently encounter language barriers in these contexts and have limited
capacity to correct the biases introduced by interpreters and translators. All these result
in data gaps.
Transforming our world: the 2030 Agenda for Sustainable Development.
2
United Nations Development Programme (2017).
3
F. Oliva and L. Charbonnier (2016).
4
”
5
Effective prevention and conflict mitigation measures rely on timely information to identify
trends as they emerge and to monitor contexts as they evolve. Public social media and
radio discussions can fill some of these data gaps and supplement official reporting when
data quality is insufficient
5
.
UN Global Pulse worked with partners to understand whether data from social media,
namely Facebook, and public radio shows (radio remains the most widespread means of
communication for most communities across Africa) can provide insights on SDG16.
The following sections present the results of the research process and the lessons learned
in the definition of new methods of analysis developed with experimentation in 2 test
cases: mining social media in Somalia and analysing data from radio broadcasts in
Uganda.
Transparency, Accountability and Participation for 2030 Agenda (2017).
5
6
TESTING NEW METHODS OF ANALYSIS
EXPERIMENTING WITH SOCIAL MEDIA MINING
IN SOMALIA
Social interactions have changed due to the adoption of technology with people worldwide
being constantly inter-connected. Media platforms allow actors to reach out, influence and
mobilize parts of society at scale in unprecedented ways. A variety of opinion-makers
6
have
emerged and with them, new patterns of social mobilization. In recent years, analysts have
witnessed the significant role of social media to drive social movements – during the Arab
Spring uprisings and with increasing online recruitment efforts from various
extremist groups.
In the private sector, companies use a myriad of social media mining techniques to provide
business intelligence
7
and increase revenue. Specialized companies offer a variety of data
mining services to track brand perception, campaign performance and detect market
trends. Also, an increasing number of companies are providing analysis of real time
information on social media and breaking news alerts to inform financial markets.
DATAMINR
SOCIAL MENTION
HOAXY
CRIMSON HEXAGON
SPROUTSOCIAL
MELTWATER
PREDATA
F. Oliva and L. Charbonnier (2016).
6
United Nations Global Pulse (2013).
7
Figure 1. Data mining of social media: Overview of companies, services and data sources.
By Pulse Lab Kampala.
7
Data sourcesCompany
INSTAGRAM WEB PAGESLINKEDIN YOUTUBE FACEBOOK TWITTER
Using techniques similar to those developed by private sector companies, UN Global Pulse
experimented analysing social media data from public Facebook groups to understand
people’s perceptions of various topics related to peace and security issues in Somalia. The
experimentation process resulted in the analysis of: influencers, fake news and trending
topics.
8
Data mining process
The following combination of human and machine-learning processes were implemented:
Development of the software to stream
information from public Facebook groups
using Facebook's Graph Application
Programming Interface (API).
Machine analysis of 2,300 public
Facebook groups for analysis conducted
with software based on the group or
location name and high number of
participants.
Manual analysis of additional 200
Facebook groups that were not identified
with the software. Groups were selected
basedon name, location and a high
number of participants.
Development of a taxonomy of keywords
related to the topic of analysis to filter 3
categories of data identified: a) public
posts, b) comments and c) reactions to
comments.
Definition of a query of analysis based on
the defined taxonomy and defined
timeframe of analysis.
Filtering of comments based on the query.
Aggregation of results preserving the
anonymity of individuals posting in public
forums.
Categorization of filtered posts by gender
(based on group name) and other defined
categories.
Representativeness and biases
There are an estimated 1.2 million users of Facebook in Somalia (around 8% of the total
population
8
). Facebook users are literate and it is expected that the majority of users
are male, young and live in urban areas. The analysis results reflect only on the
interactions on public social media groups of this portion of the population.
An analysis of the biases of the data was conducted to inform the methods of analysis and
the interpretation of the results. The following biases were identified:
It is expected that the Facebook groups are created by people living in Somalia and by
Somali diaspora, but it is not possible to distinguish them.
It is expected that individuals or groups create more than one Facebook group or an
unknown number of fake groups.
Translators might introduce biases during the translation from Somali to English of
public posts.
Commonly, Facebook posts include emoticons and these are lost during the translation
process.
The machine and manual targeting processes of Facebook groups might leave out an
unknown number of groups.
The analysis conducted did not distinguish between public posts posted by one or
different users.
The influencers analysis
“…The group (Al-Shabaab’s) uses Facebook, Twitter, YouTube and websites like
alfurqan.net and somalimemo.net, as well as Radio Andalus to spread its rhetoriclocally
... .
Countering Al-Shabaab Propaganda and recruitment mechanisms in South Central
Somalia
9
.
InternetWorldStats (https://www.internetworldstats.com/africa.htm).
8
The radicalization of young people is a source of deep concern for countries around the
world, especially regarding the risk of being recruited into terrorist groups. Violent
extremism in Africa threatens prospects of achieving the SDGs
10
. Social media is a vehicle
used by power groups to spread ideologies online and create opinions.
”
United Nations Assistance Mission in Somalia (2017).
9
United Nations Development Programme Regional Bureau for Africa (2017).
10
9
Borama News Net...
24,763 comments
89,280 followers
Barxada Videos
18,595 comments
129,536 followers
Barcelona fc ...
15,511 comments
44,057 followers
Universal Soma...
14,091 comments
756,454 followers
10
45,305 comments
384,770 followers
Somali world
A social media influencer is a user who reaches a large audience and has a certain
credibility for that audience and therefore, has the potential to influence or persuade it.
According to research, terrorist groups have systematically used social media platforms to
spread propaganda and recruit young people in Africa.
The team explored how data from social media can help identify groups that might be
influencing the opinions of other online users. To test the hypothesis, the project used
the topic of corruption.
To identify influencers relevant to the topic, the project team first identified all the groups
that had posted anything related to the subject matter. Then, the groups were
ranked according to the following variables: a) number of posts, b) number of
comments posted and c) the number of shares of content posted and the number of
followers. As the number of followers is not available through the API, the team excluded
this variable.
Results
A total of 114 out of the 2,500 public Facebook groups analysed (from January to
December 2017) included content related to corruption.
The top 5 influencers under each variable were news agencies, with only one exception,
the Barcelona fc somalian fans.
Number of comments
Fake news can cause social alarm, generate opinions, contradict governmental channels of
communication or manipulate people’s perceptions of events. The use of fake news at
scale can influence electoral results, gear social tensions or mobilize social rage towards a
common target with violent acts.
The analysis of fake news
Number of posts
SONNA
1,396 posts
55,582 followers
Caasimada Online
1,369 posts
423,094 followers
Radio Dalsan
1,333 posts
122,719 followers
Goobjoog
1,323 posts
237,128 followers
Somali world
295,029 shares
384,770 followers Universal Soma...
263,574 shares
756,454 followers
Borama News ...
192,783 shares
89,280 followers
Telefishinka Qar...
174,617 shares
357,212 followers Dalsoor
158,097 shares
280,836 followers
Figure 2. Ranking of public Facebook group influencers on the topic of corruption. By Pulse Lab Kampala.
1,539 posts
274,453 followers
Radio Mogadishu
Number of shares
11
Patterns in fake Facebook
posts
All posts mentioned senior level govern-
ment officials in the Government of Soma-
lia such as the Prime Minister and line min-
isters.
All posts were posted up to 20 times from
3-4 different accounts within 1-2 minutes.
Most posts contained the words sir (secret)
and culus (big).
All posts were almost 1 page long while
non-fake posts are normally brief (a few
lines maximum).
Examples of fake news
Translation from Somali: “Today’s News 28
November 2017 Big Secret! Who can hide
the truth today? Today’s Questions: Why did
President Mr. Mohamed Abdullahi
Farmaajo nominate individuals to the
highest office, whose corrupt practices
have been exposed and documented by the
UN Somalia and Eritrea Monitoring Group.”
Patterns in fake Facebook
groups
Out of the 100 Facebook groups with more
number of posts, 15 groups were fake.
Most used the name of real media outlets
(for example Radio Mogadishu, Villa
Somalia or SONNA) and popular public
figures such as politicians, journalist,
bloggers or activists.
The number of followers of some fake
groups is significant in comparison to the
number of followers of the genuine groups,
see example in the next page.
Translation from Somali: “Emirate military
barrack (military training academy use to
managed by UAE) of Gen. Gordan in Hodan
District of Mogadishu has been looted
today”.
12
Identifying fake news in real time can support efforts to counter the groups that are
disseminating them. In this test case, the project team detected fake news and then iden-
tified patterns that can guide the machine-automatic identification of both fake news and
the groups that promote them.
Results
The analysis was conducted on the 2,500 Facebook groups for content posted from
January 2017 to May 2018.
While there is no official Facebook group
of the former President of Somalia,
Sheikh Sharif Sheikh Ahmed, the team
found 5 fake groups with his name:
NOTE: The official Facebook group of
the President of Somalia had
344,394 likes and 356,693 followers
in May 2018.
Sharif Sheikh Ahmed Sharif
69,402 likes
72,069 followers
Sheikh Ahmed Official Page
90,602 likes
91,745 followers
Sheikh Sharif Sheikh
Ahmed Ahmed
7,471 likes
7,427 followers
Sheikh Sharif
291 likes
295 followers
President Sheikh
Sharif Sheikh
5,571 likes
5,559 followers
Examples of fake Facebook groups
The team found 15 Facebook groups with
the name of the President of Somalia,
Mohamed Abdullahi Farmaajo, these are
the top 5 in terms of number of followers:
Maxamed
Cabdullaahi Farmaajo
59,141 likes
59,628 followers
Farmaajo
30,118 likes
30,285 followers
Madaxweyne
27,182 likes
27,454 followers
Maxamed C/laahiFarmaajo
3,079 likes
3,133 followers
Mohamed abdullahi
mohamed farmaajo
15,988 likes
16,375 followers
Image 1. Example of genuine and fake Facebook groups. The genuine group has a verification mark
next to the group name. By Pulse Lab Kampala.
13
“…If we are to counter terrorists’ manipulative messages, we must engage with young
people on their terms... .
Statement of António Guterres, UN Secretary-General
11
.
The analysis of trending topics
Statement delivered at the UN forum “Investing in Youth to Counter Terrorism” (12 April 2018).
11
InternetWorldStats (https://www.internetworldstats.com).
12
”
14
Facebook is the most popular social media platform around the world. The number of
subscribers in Africa reached 177 million
12
in 2017 and increases exponentially every
month because of the increase in mobile phone penetration. A trending topic on social
media is a subject that experiences a surge in popularity for a limited duration of time.
Private sector conducts analysis of trending topics to understand what holds consumer
interest.
The analysis of trending topics among social media users can guide policies to support
peace and security efforts. The team developed software to conduct this process
automatically.
The software detects terms that are most repeated in a defined timeframe (last 48 hours).
To identify them, the software uses an algorithm based on the information retrieval
technique, term frequency–inverse document frequency (TF-IDF), to calculate the weight
of each word in a text corpus (in this case all public Facebook group discussions within the
specified timeframe). The weight is calculated based on 2 dimensions: term frequency
(TF) that measures the frequency of a word in the text and the inverse document frequency
(IDF) that measures how significant that word is in the text. The software excludes stop
words that are used to connect words like the, and or but in English and identifies the top
20 most frequent words.
Examples of how the software works
The software identifies trending terms in the last 48 hours, for example: the trending terms
in all the Facebook groups selected (2,500) for the 48 hours prior to November 14, 2018
(5:30pm) were: alpha, at the fair, ereteriya (name of country), corners,
communication, master, wine, Radio Mogadishu, republic and theatre
13
.
The software identifies terms most associated to keywords selected, for example: we
selected dagaal as the keyword (that means war) and we did a query to select all posts
from the target Facebook groups (2,500) containing the term dagaal in 2017.
The query results showed 4,225 retrieved posts. Then, the software identified the most
frequent terms associated with the word daagaal in these posts. These terms were:
mosque, shed, courage, longer, agree, virtual, shere (name of place), financial, he is
sick, moods, opposition, is enough, conference, boom, book, horjeesaty (name), no
money and proud
14
.
Translated to English from Somali.
13
Translated to English from Somali.
14
World Radio Day 2017.
15
Radio was used to incide the genocide in Rwanda. Reference: BBC News article: The impact of hate media in Rwanda
(http://news.bbc.co.uk/2/hi/africa/3257748.stm).
16
15
EXPERIMENTING WITH RADIO CONTENT
ANALYSIS IN UGANDA
“…Radio gives voices to women and men everywhere... ”
Irina Bokova, Director-General of UNESCO at the World Radio Day 13, February 2017.
The 2030 Agenda agreed by 193 countries to address challenges of today’s world has a
central commitment: leaving no-one behind. With technology, unprecedented large
volumes of data are now available in real-time to ensure that people’s voices are heard,
even those who are normally categorized as disconnected from digital devices.
According to United Nations Education Scientific and Cultural Organization (UNESCO)
15
,
radio is the most reliable and affordable medium of accessing and sharing information in
Africa. Radio shows are a channel to influence behavioral change on topics like hygiene,
violence against women or AIDS. At the same time, as recent history in Rwanda
16
showed,
radio is also used to create opinion and mobilize large population groups.
Three years ago, UN Global Pulse started an experimental programme in Uganda to
analyse people’s voices from radio broadcasts. The research revealed that analyzing radio
data captures testimonials, people’s sentiments and reports that are not gathered from
other sources
17
. This can inform early warning systems to prevent violence, conflict and
social tensions from escalating in support of peace and security efforts.
Qualitative information is as valuable as quantitative information in an early warning
systems as their ultimate goal is to provide alerts. Unsolicited testimonials expressed on
public radio are a valuable source of information showing various dynamics in societies,
which can help inform early warning systems.
The test case explored the detection of: rumors and misconceptions, social tensions and
testimonials that cause social alarm.
Data mining process
UN Global Pulse developed a toolkit that uses AI technology to analyse large amounts of
information from public radio broadcasts. There are hundreds of hours of content, in the
form of raw data, that the toolkit streams everyday. By using convolutional neural
networking technology to create Automated Speech Recognition (ASR) toolkits for African
languages, these large and unstructured datasets become smaller and more manageable.
United Nations Global Pulse (2017).
17
16
The toolkit includes the following components:
Software to filter content out. Out of 24-hour radio programmes, around 50% consists of
music. The software identifies and filters out clips with more than 70% of music
content. A second filter is then applied to select audio files from radio programmes
relevant for analysis that were targeted manually by an analyst as for example, radio
shows with people phoning in.
Speech recognition software. The software transforms speech to text automatically for
Luganda and Acholi (vernacular languages of Uganda). Pronunciation dictionaries were
created for those languages showing the commonest sequences of sounds people make
as they say each word. With the dictionaries and transcribed recordings, acoustic
models were built. The acoustic models reflect all the sounds people utter as they talk
about topics of interest. The team worked on language modelling, which is an analysis
of how different words in the target languages are related. For example, if the word
flood is uttered, the system determines the probability of the next word, e.g. destroy.
This needs to be done to help the system identify keywords of interest based on the
context of the words being said in the same sentence
18
.
Keyword spotter software Spock. A new system for speech recognition was developed
with deep learning methods to identify keywords. The system requires less transcribed
speech recordings as the software learns words in the new language instead of the
entire dictionary. The advantage of this software is that the deployment for new
languages is much faster than the previous version. The disadvantage is that the
accuracy is lower as the system can no longer search for a set of keywords, nor rely on
the context to ensure the right word has been identified.
Goldie software. The software distributes the computing resources so that they are used
for the most relevant radio content targeted in the ranking of radio content. An
interface allows analysts to identify, tag and translate audio clips.
John Quinn, Artificial Intelligence advisor at UN Global Pulse.
18
To facilitate the data mining process, the team at the Lab mapped out the programmes of
92 radio stations. It is estimated that there are 292
19
operational FM radio stations in
Uganda, which means that the team analysed the content of one third of them. The team
organized them in the following categories:
Uganda Communications Commission (UCC).
19
Gold list includes programmes with grassroots discussions and public participating by
phoning the radio studio.
Silver list includes programmes with radio studio discussions with grassroots,
local/national/ regional officials and public participating by phoning the studio.
Bronze list includes programmes of news reports.
Black list includes programmes on sports, celebrity gossip, strictly musical shows or
similar.
17
The team estimated that every day, an average of 20,000 to 25,000
20
people voice their
opinions and share reports on radio shows in Uganda. It is presumed that people who
participate in radio shows are outspoken members of society, who can afford airtime for a
local call, and that the majority of these people are men.
An analysis of the biases of the data was conducted to inform the methods of analysis and
the interpretation of the results. The following biases were identified in the data:
Estimation by Pulse Lab Kampala/UN Global Pulse based on analysis of radio content in a sample size of 92 radio stations (2017).
20
Representativeness and biases
The accuracy of the transcription software is not 100%. As a result, relevant content
might be missed out.
Intermittent or weak radio signal might reduce the accuracy of the transcription
software.
Radio call-in shows might be sponsored by international assistance, NGOs, or
government, which might introduce a bias in the selection of public opinions to be
aired.
While radio presenters moderate public discussions, it is expected that their personal
agendas might influence the public discourse.
The translation process into English might introduce interpretation of the public
discourse.
To reduce the amount of data to be processed and increase the speed and accuracy of the
final results, radio programmes in the Bronze and Black lists were excluded from analysis
using machine filtering.
“…now South Sudan, there is a war and
you see that many refuge seekers come
here in Uganda. And when every person
walks, he walks with his diseases and
problems…and you see that puts us on
alert. Now we also see, that there are
some districts which have not
immunized… now reports show us that
we have almost 200,000 children who
are not immunized. Now, if those
children stay there when they have not
been immunized they became
dangerous to others. It can be the
source of the disease spreading to other
areas…”.
A listener explained (West Nile, October
2017) to the audience: “…It may not
have been refugees, but we are already
seeing stock out of ARVs, of recent we
also saw stock out of anti TB drugs…
we are even so scared that whether the
government will be able to meet the
cost or buy more of these ARVs for the
refugees…”.
A community leader (Northern Region,
September 2017) explained on radio:
“…now you notice that there is
increased murder in Uganda especially
of women... So, all of the refugees
have run into Uganda. Uganda has
opened all its borders just for anyone
to come in. For this reason, we do not
know the people that we are living with
in our surroundings. If you look at this
tribe X (name of a South Sudanese
tribe), their population is way beyond
already in Uganda. At this moment,
this is a security threat to all of us...”.
A listener (Central region, September
2017) explained: “…I was actually
tempted to think that the source of the
anthrax outbreak in West Nile was due
to the animals that entered our country
(to refugee settlements) without being
screened…”.
A government official (Central Region,
September 2017) said:
18
Results
To foster peaceful, just and inclusive societies which are free from fear and violence, we
need to better identify changes in the behavior of population groups that might pose a
threat to others in time. The proposed method is to use qualitative reports from public
radio discussions to feed early warning systems in near real time. The type of content
identified as valuable to feed these systems was categorized under the following
categories: rumors and misconceptions, social tensions and testimonials that can cause
social alarm.
Detecting rumors and misconceptions
Many challenges for sustainable peace are the result of collective reactions powered by
rumors and misconceptions. Social tensions can rise as a result of rumors that spark them,
and early identification is essential to be able to effectively address them. These are some
examples:
Social tensions usually have long-term historical, cultural or religious roots and build up
over time. Sudden changes in contexts affecting societies might spark these tensions and
also create new ones that will again leave a print in the collective memory. Identifying
emerging social tensions and the circumstances that spark them is critical to respond
promptly.
Detecting social tensions
A community leader (Northern Uganda,
September 2017) expressed: “…now if
we are to ask ourselves, what could be
the reason for insecurity within the
country? You are going to find out that
Uganda has become a regional hub for
thugs and all the thieves within the
region…”.
Two radio presenters (m) (Northern
Uganda, September 2017) discussed:
“…(m1) when refugees are coming, we
should be assured that there could be
other risks that result from their coming
as refugees. (m1): Yes, we have for
examples of diseases. (m2): Not only
that, you know that these refugees come
with their culture and this culture is not
in Uganda here and you are aware that
in South Sudan, these people have guns
and the whole community too. (m1) So,
they are familiar with the gun. (m2) Yes,
for that reason we fear that they will
enter with their guns into Uganda…”.
A community leader (Central Region,
July 2016) explained: “… over 300
Ugandans have been able to run from
the country of South Sudan after an
internal war erupt again inside that
country… the leaders in the parts of
Moyo also expressed worry because of
the illegal guns that are continually
being brought into their region and the
all country as well…it led to the
residents especially the Madi to come
out and complain to the security,
showing that there are guns that enter
in their regions in unlawfully ways…”.
A radio presenter (West Nile,
December 2017) explained:
“…leaders in Arua district are
appealing to the various institutions
like the church schools and the
individuals to partner with the
government to restore the environment
that has been destroyed by the
presence of refugee in the district. The
refugees who have settled in West Nile
and northern region negatively
contributed to the destruction of trees
and road network...”.
19
Detecting reports that can cause social alarm
Individual testimonies can cause social alarm when they touch upon underlying structural
issues affecting societies in a negative way. Collective reactions to alarming testimonials
might occur in a peaceful way or with violence.
A radio presenter informed (West Nile,
October 2017): “…number of refugees
now in Imvepi refugee camp is around
133,000. X( name) who is the field
officer for Arua said that the reason why
this is happening is because more
names than people are being written
(registered)…”.
A listener (p) and a community leader
(Lo) (West Nile, October 2017)
explained: “… (p) - In the past, level
one registration was done at collection
points, which promoted a malpractice of
one person registering many times
levying burden on the Aid
Agencies…(Lo): We are trying to help
each other with Office of the Prime
Minister and government to make sure
the systems are well established to
address double registration here…”.
A listener (West Nile, November 2017)
shared with the audience: “…she
delivered…and she recognized the sex
of that baby, a baby girl…how comes
that you people bring a dead boy? I
need my daughter. Then from there
shortly that doctor explained to her
that now if you want me to go and look
for that baby, give me 20,000
(Ugandan Shillings), so that I am in
position to go and look for your baby.
The woman said she is a refugee-she
can't afford 20,000 (Ugandan
Shillings)-where will she get that
money from? She started to cry…”.
An official explained (West Nile,
November 2017): “…3 men, calling
themselves (XYZ names) had come to
recruit clandestinely the youth by
promising them jobs, money, all
that…. the man arrested confessed to
recruiting youth to participate in rebel
activities in South Sudan… Two of the
youths gave in their statement,
confessing that truly they escaped
from that training ground…”.
20
DEVELOPING A NEW GENERATION OF
ANALYSIS TOOLS: QATALOG
UN Global Pulse developed a data mining tool that UN personnel and partners can use
to inform development, humanitarian and peace efforts, called QataLog. The tool allows
users to extract, analyse, and visualize data from social media and radio broadcasts. The
interface is built using D3, a JavaScript visualization library and database
management tool. QataLog stands for:
Q
A
T
A
Query
Assign
Tag
Analyze
Functionalities
Users define the parameters for the extraction of public content
for analysis. Elements of the query include: timeframe of
analysis and keywords. To facilitate the selection of keywords on
social media, trending terms are identified automatically. Also,
terms mostly associated with the keywords selected are
automatically identified.
Users can work in groups and assign tasks or content to be
analysed by team members.
Users define their own tags and use them to organize the
content in categories to facilitate the analysis.
Users access raw data that has the parameters selected. To
facilitate analysis, the following functionalities are available: a)
through an integration with Google Translate, rough text
translations are automatically generated for social media
content; b) automatic geotagging of social media posts
assigning a location tag to posts containing place names is done
automatically; c) raw data extracted for analysis can be
transferred to a spreadsheet and d) basic visualization of
analysis results (volume and trends over time) is available.
Limitations
Data mining applied to low-resource languages presents a challenge and affects the
accuracy of the analysis tool. In some cases, false matches are identified during the
mining process. For example, refugee in Acholi is translated as luring ayela, the literal
meaning of which is runners of problems. As a result, discussions about athletes and
problems in general are selected with the English keyword refugee. Another example is the
term HIV/AIDS that is translated in Lugbara as two jonyo, that is literally translated as
slimming disease
21
. As a result, conversations about disease or slimming in general are
selected automatically. To correct this bias, manual analysis is required to identify false
matches.
John Quinn, Artificial Intelligence advisor at UN Global Pulse.
21
22
Benefits analysis
QataLog allows users to conduct analysis of big data from social media and radio content
at a large scale to inform humanitarian and peace efforts, among others. The exciting
aspect is that it captures people’s voices from places where traditionally very sporadic
and unreliable data is collected at a lower cost than other means of doing analysis
BENEFITS OF QATALOG FOR RADIO AND SOCIAL MEDIA
TOOL NO TOOL
TARGETING RADIO CONTENT
ACCESS TO RADIO BROADCAST
FILTERING OUT MUSIC CONTENT
MULTIPLE RADIO STATION ANALYSIS
REAL TIME TRANSCRIPTION OF CONTENT
An analyst manually listens to
radio stations to extract
relevant content on a given
topic
It is not possible to filter out
music on the radio
An analyst automatically
targets all relevant content
on a given topic from
unlimited stations
Music on the radio is
automatically filtered out
An analyst can analyse a
maximum of 1 radio station in
real-time
An analyst can analyse more
than 10 radio stations in
real-time
Transcription is limited because
the analyst has to rely on
note-taking and memorisation
Easier, faster and more
reliable transcriptions
Radio broadcasts can be
accessed from remote
locations
Analysts have to physically be
in the same location as the radio
station
10 1
AUTO
23
TOOL NO TOOL
ANALYSIS OF FACEBOOK GROUPS
A limit of 20 to 30 groups
are analysed daily
An unlimited number of groups
are analysed daily
GEOLOCATION OF POST
A limit of 20 to 30 posts are
geolocated daily
An unlimited number of posts
are geolocated daily
ANALYSIS OF TRENDING TOPICS
TRANSLATION FROM SOMALI TO ENGLISH
FACEBOOK COMMENTS
Open up GoogleTranslate from
the browser, copy and paste
the source text for translation
One single click is required to
translate the text
Each Facebook comment must
be individually copied and
pasted into a spreadsheet
One click is needed to
download an unlimited number
of Facebook comments to a
spreadsheet
ANALYSIS OF TRENDS
STATISTICS ON SELECTED MESSAGES
TARGETING COMMENTS
Feeds with reoccuring words
within the last 48 hours
suggests trending topics
500 messages are selected and
tagged per day
Automatically counts total
tagged messages per day
Automatic targeting of
posts and comments
containing keywords
Posts are selected manually.
Reoccurring hashtags, likes
and replies suggests trending
topics
50 messages are selected and
tagged manually per day
Manually counts up to 50
tagged messages per day
Manual search of keywords
using the facebook search
function & comments reviewed
one at a time
24
United Nations Development Group (2017).
22
25
INTRODUCING ETHICS TO ANALYSE
PEOPLE’S VOICES
New technologies and new methods of analysis pose challenges to policy, legal
frameworks and ethics. The UN Secretary-General's Strategy on New Technologies calls
for overcoming these challenges and reconciling interest on ethics to use frontier
technologies.
Although privacy norms have been long established to protect personal data from misuse
and ensure individual privacy in the digital world, ethics has become an additional tool in
AI applications used to protect fundamental human rights.
A recent example in which ethics was included in the United Nations (UN) policy is the
Guidance Note on Big Data for the achievement of the 2030 Agenda
22
adopted by the UN
Development Group. The note, which UN Global Pulse helped develop, contains a set of
principles to ensure that data ethics is included as part of standard operating procedures
for data governance.
UN Global Pulse also builds ethical considerations into its data practices by conducting a
risks, harms, and benefits assessment, which helps identify anticipated or actual ethical
and human rights issues that may occur during a data innovation project. The assessment
considers the proportionality of potential benefits compared to risks of harm from data use.
If the risks outweigh the benefits, the project does not proceed.
To conduct the analysis of public Facebook and radio content, the team applied the
following data privacy principles:
Objective of the analysis
A common concern from stakeholders is the potential misuse of data and tools. UN Global
Pulse applies a Purpose of Use principle to all its projects: we access, analyse or otherwise
use data for the purposes consistent with the United Nations mandate and in furtherance
of the Sustainable Development Goals.
To protect from risks and harms that can occur to individuals and groups, UN Global Pulse
applies the principle of Data Security: we ensure reasonable and appropriate technical and
organizational safeguards are in place to prevent unauthorized disclosure or breach of
data.
Informed consent
A common question from partners is whether the analysis conducted on Facebook is done
on personal pages or if people calling into talk shows have given consent for their opin-
ions to be analysed.
When users create a group on Facebook, they can choose between 3 privacy
settings: public, closed or secret. Public groups are visible to everyone and people
who join that group agree to the chosen privacy setting. In the case of public radio
talk shows, these conversations are public. UN Global Pulse applies the Right to Use
principle: we access, analyze or otherwise use data that has been obtained by lawful and
fair means, including, where appropriate, with the knowledge or consent of the individual
whose data is used.
Anonymity and re-identification
In some cases, the identity of people expressing opinions on public social media or
radio could be identified via voice or names mentioned. To ensure that this does not
happen, the data we use is anonymised to the extent possible. UN Global Pulse
also applies the principle of Individual Privacy: we do not access, analyse or otherwise
use the content of private communications without the knowledge or proper consent of
the individual. We do not knowingly or purposefully access, analyse, or otherwise use
personal data, which was shared by an individual with a reasonable expectation of
privacy without the knowledge or consent of the individual. We do not attempt to
knowingly and purposefully re-identify de-identified data and we make all reasonable
efforts to prevent any unlawful and unjustified re-identification.
26
Equality and Anti-Discrimination Ombud. (2015). The equality and anti-discrimination ombud’s report:
Hate speech and hate crime. Government of Norway.
Heinzelman J., Brown R., Meier P. (2011). Mobile technology, crowdsourcing and peace mapping: New
theory and applications for conflict management. In: Poblet M. (eds) Mobile technologies for conflict
management. Law, governance and technology Series, vol 2. Springer, Dordrecht.
Oliva F. and Charbonnier L. (2016). Conflict analysis handbook. A field and headquarter guide to conflict
assessment. United Nations system Staff Colleague.
Salem, F. (2017). The Arab social media report 2017: social media and the Internet of things: Towards
data-driven policy making in the Arab world (Vol. 7). Dubai: MBR School of Government.
Seyle C. (2018). Global Alliance for reporting progress on promoting peaceful, just and inclusive societies.
The role of the private sector in support of reporting under SDG16. OEF Research.
Starbird K., Arif A., Wilson T., Van Koevering K., Yefimova K. and Scarnecchia D. U (2018). Ecosystem or
echo-system? Exploring content sharing across alternative media domains. Conference on computer –
supported cooperative work and social computing (CSCW). Faculty of Washington.
The Kingdom of Belgium (2016). Strategic policy note: Digital for Development’ (D4D) for the Belgian
Development Cooperation. The Kingdom of Belgium.
Transparency, Accountability and Participation for 2030 Agenda (TAP Network). Principal author Rodrigues
Ch. (2017). Goal 16 Advocacy Toolkit. A practical guide for stakeholders for national-level advocacy around
jeaceful, Just and inclusive societies. TAP Network.
United Nations Global Pulse (2013). Big data for development: a premier. United Nations.
United Nations Global Pulse (2017). Using machine learning to analyse radio content in Uganda.
Opportunities for sustainable development and humanitarian action. United Nations.
United Nations Development Group (2017). Data privacy, ethics and protection– Guidance note on big data
for achievement of the SDGs.
United Nations Development Programme (2017). Drivers, incentives and the tipping point for recruitment.
Journey to extremism in Africa. United Nations.
United Nations Development Programme (2017). Monitoring to implement peaceful, just and inclusive
societies. Journey to extremism in Africa. United Nations.
United Nations Mission to Somalia (2018). Countering Al-Shabaab propaganda and recruitment
mechanisms in South Central Somalia. United Nations.
United Nations (2018). United Nations Secretary-General’s strategy on new technologies.
Verhuslst S., Young A. (2017). The Potential of social media intelligence to improve people’s lives: social
media data for good. The GovLab.
https://www.unglobalpulse.org/kampala pulselabkampala@unglobalpulse.org 2018
27
BIBLIOGRAPHY
SOMALIA

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Experimenting with Big Data and AI to Support Peace and Security

  • 1. EXPERIMENTING WITH BIG DATA AND ARTIFICIAL INTELLIGENCE TO SUPPORT PEACE AND SECURITY SOMALIA
  • 2. THANKS TO THE GENEROUS CONTRIBUTION OF: Government of Belgium Government of SwedenGovernment of The Netherlands Government of Denmark United Nations Development Operations Coordination Office United Nations Peacebuilding Support Office
  • 3. TABLE OF CONTENTS Acknowledgements 2 Executive Summary 3 Analysing people’s voices to achieve peace and security 5 Testing new methods of analysis 7 Experimenting with social media mining in Somalia 7 Data mining process 8 Representativeness and biases 9 The influencers analysis 9 The analysis of fake news 11 The analysis of trending topics 14 Experimenting with radio content analysis in Uganda 15 Data mining process 15 Representativeness and biases 17 Detecting rumors and misconceptions 18 Detecting social tensions 19 Detecting reports that can cause social alarm 20 Developing a new generation of analysis tools: QataLog 22 Introducing ethics to analyse people’s voices 25 Bibliography 27 1
  • 4. ACKNOWLEDGEMENTS This publication would not have been possible without the leadership, commitment and investment of many colleagues from Pulse Lab Kampala and the UN Global Pulse network. The report was authored by Paula Hidalgo-Sanchis, PhD, Manager of Pulse Lab Kampala. UN Global Pulse would like to thank the generous contributions from the Governments of Sweden, Denmark, The Kingdom of the Netherlands, The Kingdom of Belgium, the UN Peace Building Support Office (PBSO) and the UN Development Group (UNDOCO) that funded this work. HOW TO CITE THIS DOCUMENT: UN Global Pulse (2018). Experimenting with Big Data and Artificial Intelligence to Support Peace and Security. 2
  • 5. “…Online social networking lets us find like-minded people around the world, enlarging free speech and human creativity. But it also amplifies hate speech, contributes to ethnic and political polarization, and facilitates terrorist recruitment… . United Nations Secretary-General’s Strategy on New Technologies. ” United Nations Development Programme (2017). 1 3 EXECUTIVE SUMMARY Sustainable development is built on the foundations of a peaceful, just and inclusive society and institutions 1 . New types of war, conflict and violence challenge traditional ways of doing analysis to support peace and security efforts. Small data and methods of analysis used with small data cannot respond to these new challenges. We need to strengthen our analytical capacities, use new types of data and develop new methods. UN Global Pulse has been working - through its lab in Kampala - with partners using big data to support progress on SDG16: Promote Just, Peaceful, and Inclusive Societies. We explored the utility of analysing data from social media and public radio broadcasts to extract insights to feed early warning systems and inform peace and security processes. The experimentation process led to producing technology tools to analyse people’s voices. The application of the technology tools resulted in the development of new methods of analysis. This report outlines the experimentation process, methods of analysis, results and lessons learned from two test cases. The first test case used data mined from social media, namely posts from public Facebook pages and groups, to analyse how influencers and fake news might be shaping discussions among online users in Somalia and to identify trending topics relevant to SDG 16. The second test case analysed public discussions on radio broadcasts to detect instances of rumors and misconceptions, social tensions and testimonials that cause social alarm in Uganda. The report also details the functionalities of a new tool, named QataLog, developed by UN Global Pulse to help extract, analyse and visualise data from public social media and radio shows. The tool can be used in different scenarios, including to inform humanitarian and peace efforts.
  • 6. New technologies and new digital applications, if used responsibly, hold tremendous potential to help us achieve the 2030 Agenda for Sustainable Development. More and more compelling examples illustrate the value of technology to improve early warning systems and inform policy and programmatic response. Yet, as adoption of big data and artificial intelligence (AI) increases and technology evolves, so do the potential risks and issues that need to be resolved. The final section of the report outlines the data privacy, data protection and ethics principles that guide the work with big data and AI at UN Global Pulse, from conceptualization to development of technology and implementation. 4
  • 7. ANALYSING PEOPLE’S VOICES TO ACHIEVE PEACE AND SECURITY The 2030 Agenda states that ‘there can be no sustainable development without peace and no peace without sustainable development ’ 2 . The definition of SDG16 breaks new ground in articulating and emphasizing the purpose and outcomes of good governance in development. Sustainable development is built on the foundations of a peaceful, just and inclusive society and institutions 3 . “…Preserving peace has become more complicated because of an increase in violence no longer perpetrated exclusively by national security forces and conventional armed oppositions but also by an increasingly wider and assertive range of hybrid actors…their impact is so significant that the violence resulting from these unconventional players exceeds that of many ongoing civil wars and must be added to the role of revolutionary mass movements such as the popular uprisings in the Arab region… . Conflict Analysis Handbook. A Field and Headquarter Guide to Conflict Assessment 4 . New types of war, conflict and violence challenge traditional ways of doing analysis to support peace and security efforts. Small data and traditional methods of analysis commonly used with small data are no longer sufficient to respond to these new challenges. We need to strengthen our analytical capacities, use new types of data and develop new methods that can provide more granular, real-time and comprehensive information. Quantitative and qualitative traditional means for gauging public sentiment, such as surveys, focus group discussions and informant’s interviews, often require a great amount of resources and time and as a consequence, people’s sentiments might have long changed by the time that insights are actioned by policy makers. In addition, security constraints in conflict or post conflict areas limit the movement of analysts that stay confined in secured locations with reduced capacity to interact with the local population. Also, analysts frequently encounter language barriers in these contexts and have limited capacity to correct the biases introduced by interpreters and translators. All these result in data gaps. Transforming our world: the 2030 Agenda for Sustainable Development. 2 United Nations Development Programme (2017). 3 F. Oliva and L. Charbonnier (2016). 4 ” 5
  • 8. Effective prevention and conflict mitigation measures rely on timely information to identify trends as they emerge and to monitor contexts as they evolve. Public social media and radio discussions can fill some of these data gaps and supplement official reporting when data quality is insufficient 5 . UN Global Pulse worked with partners to understand whether data from social media, namely Facebook, and public radio shows (radio remains the most widespread means of communication for most communities across Africa) can provide insights on SDG16. The following sections present the results of the research process and the lessons learned in the definition of new methods of analysis developed with experimentation in 2 test cases: mining social media in Somalia and analysing data from radio broadcasts in Uganda. Transparency, Accountability and Participation for 2030 Agenda (2017). 5 6
  • 9. TESTING NEW METHODS OF ANALYSIS EXPERIMENTING WITH SOCIAL MEDIA MINING IN SOMALIA Social interactions have changed due to the adoption of technology with people worldwide being constantly inter-connected. Media platforms allow actors to reach out, influence and mobilize parts of society at scale in unprecedented ways. A variety of opinion-makers 6 have emerged and with them, new patterns of social mobilization. In recent years, analysts have witnessed the significant role of social media to drive social movements – during the Arab Spring uprisings and with increasing online recruitment efforts from various extremist groups. In the private sector, companies use a myriad of social media mining techniques to provide business intelligence 7 and increase revenue. Specialized companies offer a variety of data mining services to track brand perception, campaign performance and detect market trends. Also, an increasing number of companies are providing analysis of real time information on social media and breaking news alerts to inform financial markets. DATAMINR SOCIAL MENTION HOAXY CRIMSON HEXAGON SPROUTSOCIAL MELTWATER PREDATA F. Oliva and L. Charbonnier (2016). 6 United Nations Global Pulse (2013). 7 Figure 1. Data mining of social media: Overview of companies, services and data sources. By Pulse Lab Kampala. 7 Data sourcesCompany INSTAGRAM WEB PAGESLINKEDIN YOUTUBE FACEBOOK TWITTER
  • 10. Using techniques similar to those developed by private sector companies, UN Global Pulse experimented analysing social media data from public Facebook groups to understand people’s perceptions of various topics related to peace and security issues in Somalia. The experimentation process resulted in the analysis of: influencers, fake news and trending topics. 8 Data mining process The following combination of human and machine-learning processes were implemented: Development of the software to stream information from public Facebook groups using Facebook's Graph Application Programming Interface (API). Machine analysis of 2,300 public Facebook groups for analysis conducted with software based on the group or location name and high number of participants. Manual analysis of additional 200 Facebook groups that were not identified with the software. Groups were selected basedon name, location and a high number of participants. Development of a taxonomy of keywords related to the topic of analysis to filter 3 categories of data identified: a) public posts, b) comments and c) reactions to comments. Definition of a query of analysis based on the defined taxonomy and defined timeframe of analysis. Filtering of comments based on the query. Aggregation of results preserving the anonymity of individuals posting in public forums. Categorization of filtered posts by gender (based on group name) and other defined categories.
  • 11. Representativeness and biases There are an estimated 1.2 million users of Facebook in Somalia (around 8% of the total population 8 ). Facebook users are literate and it is expected that the majority of users are male, young and live in urban areas. The analysis results reflect only on the interactions on public social media groups of this portion of the population. An analysis of the biases of the data was conducted to inform the methods of analysis and the interpretation of the results. The following biases were identified: It is expected that the Facebook groups are created by people living in Somalia and by Somali diaspora, but it is not possible to distinguish them. It is expected that individuals or groups create more than one Facebook group or an unknown number of fake groups. Translators might introduce biases during the translation from Somali to English of public posts. Commonly, Facebook posts include emoticons and these are lost during the translation process. The machine and manual targeting processes of Facebook groups might leave out an unknown number of groups. The analysis conducted did not distinguish between public posts posted by one or different users. The influencers analysis “…The group (Al-Shabaab’s) uses Facebook, Twitter, YouTube and websites like alfurqan.net and somalimemo.net, as well as Radio Andalus to spread its rhetoriclocally ... . Countering Al-Shabaab Propaganda and recruitment mechanisms in South Central Somalia 9 . InternetWorldStats (https://www.internetworldstats.com/africa.htm). 8 The radicalization of young people is a source of deep concern for countries around the world, especially regarding the risk of being recruited into terrorist groups. Violent extremism in Africa threatens prospects of achieving the SDGs 10 . Social media is a vehicle used by power groups to spread ideologies online and create opinions. ” United Nations Assistance Mission in Somalia (2017). 9 United Nations Development Programme Regional Bureau for Africa (2017). 10 9
  • 12. Borama News Net... 24,763 comments 89,280 followers Barxada Videos 18,595 comments 129,536 followers Barcelona fc ... 15,511 comments 44,057 followers Universal Soma... 14,091 comments 756,454 followers 10 45,305 comments 384,770 followers Somali world A social media influencer is a user who reaches a large audience and has a certain credibility for that audience and therefore, has the potential to influence or persuade it. According to research, terrorist groups have systematically used social media platforms to spread propaganda and recruit young people in Africa. The team explored how data from social media can help identify groups that might be influencing the opinions of other online users. To test the hypothesis, the project used the topic of corruption. To identify influencers relevant to the topic, the project team first identified all the groups that had posted anything related to the subject matter. Then, the groups were ranked according to the following variables: a) number of posts, b) number of comments posted and c) the number of shares of content posted and the number of followers. As the number of followers is not available through the API, the team excluded this variable. Results A total of 114 out of the 2,500 public Facebook groups analysed (from January to December 2017) included content related to corruption. The top 5 influencers under each variable were news agencies, with only one exception, the Barcelona fc somalian fans. Number of comments
  • 13. Fake news can cause social alarm, generate opinions, contradict governmental channels of communication or manipulate people’s perceptions of events. The use of fake news at scale can influence electoral results, gear social tensions or mobilize social rage towards a common target with violent acts. The analysis of fake news Number of posts SONNA 1,396 posts 55,582 followers Caasimada Online 1,369 posts 423,094 followers Radio Dalsan 1,333 posts 122,719 followers Goobjoog 1,323 posts 237,128 followers Somali world 295,029 shares 384,770 followers Universal Soma... 263,574 shares 756,454 followers Borama News ... 192,783 shares 89,280 followers Telefishinka Qar... 174,617 shares 357,212 followers Dalsoor 158,097 shares 280,836 followers Figure 2. Ranking of public Facebook group influencers on the topic of corruption. By Pulse Lab Kampala. 1,539 posts 274,453 followers Radio Mogadishu Number of shares 11
  • 14. Patterns in fake Facebook posts All posts mentioned senior level govern- ment officials in the Government of Soma- lia such as the Prime Minister and line min- isters. All posts were posted up to 20 times from 3-4 different accounts within 1-2 minutes. Most posts contained the words sir (secret) and culus (big). All posts were almost 1 page long while non-fake posts are normally brief (a few lines maximum). Examples of fake news Translation from Somali: “Today’s News 28 November 2017 Big Secret! Who can hide the truth today? Today’s Questions: Why did President Mr. Mohamed Abdullahi Farmaajo nominate individuals to the highest office, whose corrupt practices have been exposed and documented by the UN Somalia and Eritrea Monitoring Group.” Patterns in fake Facebook groups Out of the 100 Facebook groups with more number of posts, 15 groups were fake. Most used the name of real media outlets (for example Radio Mogadishu, Villa Somalia or SONNA) and popular public figures such as politicians, journalist, bloggers or activists. The number of followers of some fake groups is significant in comparison to the number of followers of the genuine groups, see example in the next page. Translation from Somali: “Emirate military barrack (military training academy use to managed by UAE) of Gen. Gordan in Hodan District of Mogadishu has been looted today”. 12 Identifying fake news in real time can support efforts to counter the groups that are disseminating them. In this test case, the project team detected fake news and then iden- tified patterns that can guide the machine-automatic identification of both fake news and the groups that promote them. Results The analysis was conducted on the 2,500 Facebook groups for content posted from January 2017 to May 2018.
  • 15. While there is no official Facebook group of the former President of Somalia, Sheikh Sharif Sheikh Ahmed, the team found 5 fake groups with his name: NOTE: The official Facebook group of the President of Somalia had 344,394 likes and 356,693 followers in May 2018. Sharif Sheikh Ahmed Sharif 69,402 likes 72,069 followers Sheikh Ahmed Official Page 90,602 likes 91,745 followers Sheikh Sharif Sheikh Ahmed Ahmed 7,471 likes 7,427 followers Sheikh Sharif 291 likes 295 followers President Sheikh Sharif Sheikh 5,571 likes 5,559 followers Examples of fake Facebook groups The team found 15 Facebook groups with the name of the President of Somalia, Mohamed Abdullahi Farmaajo, these are the top 5 in terms of number of followers: Maxamed Cabdullaahi Farmaajo 59,141 likes 59,628 followers Farmaajo 30,118 likes 30,285 followers Madaxweyne 27,182 likes 27,454 followers Maxamed C/laahiFarmaajo 3,079 likes 3,133 followers Mohamed abdullahi mohamed farmaajo 15,988 likes 16,375 followers Image 1. Example of genuine and fake Facebook groups. The genuine group has a verification mark next to the group name. By Pulse Lab Kampala. 13
  • 16. “…If we are to counter terrorists’ manipulative messages, we must engage with young people on their terms... . Statement of António Guterres, UN Secretary-General 11 . The analysis of trending topics Statement delivered at the UN forum “Investing in Youth to Counter Terrorism” (12 April 2018). 11 InternetWorldStats (https://www.internetworldstats.com). 12 ” 14 Facebook is the most popular social media platform around the world. The number of subscribers in Africa reached 177 million 12 in 2017 and increases exponentially every month because of the increase in mobile phone penetration. A trending topic on social media is a subject that experiences a surge in popularity for a limited duration of time. Private sector conducts analysis of trending topics to understand what holds consumer interest. The analysis of trending topics among social media users can guide policies to support peace and security efforts. The team developed software to conduct this process automatically. The software detects terms that are most repeated in a defined timeframe (last 48 hours). To identify them, the software uses an algorithm based on the information retrieval technique, term frequency–inverse document frequency (TF-IDF), to calculate the weight of each word in a text corpus (in this case all public Facebook group discussions within the specified timeframe). The weight is calculated based on 2 dimensions: term frequency (TF) that measures the frequency of a word in the text and the inverse document frequency (IDF) that measures how significant that word is in the text. The software excludes stop words that are used to connect words like the, and or but in English and identifies the top 20 most frequent words. Examples of how the software works The software identifies trending terms in the last 48 hours, for example: the trending terms in all the Facebook groups selected (2,500) for the 48 hours prior to November 14, 2018 (5:30pm) were: alpha, at the fair, ereteriya (name of country), corners, communication, master, wine, Radio Mogadishu, republic and theatre 13 . The software identifies terms most associated to keywords selected, for example: we selected dagaal as the keyword (that means war) and we did a query to select all posts from the target Facebook groups (2,500) containing the term dagaal in 2017. The query results showed 4,225 retrieved posts. Then, the software identified the most frequent terms associated with the word daagaal in these posts. These terms were: mosque, shed, courage, longer, agree, virtual, shere (name of place), financial, he is sick, moods, opposition, is enough, conference, boom, book, horjeesaty (name), no money and proud 14 . Translated to English from Somali. 13 Translated to English from Somali. 14
  • 17. World Radio Day 2017. 15 Radio was used to incide the genocide in Rwanda. Reference: BBC News article: The impact of hate media in Rwanda (http://news.bbc.co.uk/2/hi/africa/3257748.stm). 16 15 EXPERIMENTING WITH RADIO CONTENT ANALYSIS IN UGANDA “…Radio gives voices to women and men everywhere... ” Irina Bokova, Director-General of UNESCO at the World Radio Day 13, February 2017. The 2030 Agenda agreed by 193 countries to address challenges of today’s world has a central commitment: leaving no-one behind. With technology, unprecedented large volumes of data are now available in real-time to ensure that people’s voices are heard, even those who are normally categorized as disconnected from digital devices. According to United Nations Education Scientific and Cultural Organization (UNESCO) 15 , radio is the most reliable and affordable medium of accessing and sharing information in Africa. Radio shows are a channel to influence behavioral change on topics like hygiene, violence against women or AIDS. At the same time, as recent history in Rwanda 16 showed, radio is also used to create opinion and mobilize large population groups. Three years ago, UN Global Pulse started an experimental programme in Uganda to analyse people’s voices from radio broadcasts. The research revealed that analyzing radio data captures testimonials, people’s sentiments and reports that are not gathered from other sources 17 . This can inform early warning systems to prevent violence, conflict and social tensions from escalating in support of peace and security efforts. Qualitative information is as valuable as quantitative information in an early warning systems as their ultimate goal is to provide alerts. Unsolicited testimonials expressed on public radio are a valuable source of information showing various dynamics in societies, which can help inform early warning systems. The test case explored the detection of: rumors and misconceptions, social tensions and testimonials that cause social alarm. Data mining process UN Global Pulse developed a toolkit that uses AI technology to analyse large amounts of information from public radio broadcasts. There are hundreds of hours of content, in the form of raw data, that the toolkit streams everyday. By using convolutional neural networking technology to create Automated Speech Recognition (ASR) toolkits for African languages, these large and unstructured datasets become smaller and more manageable. United Nations Global Pulse (2017). 17
  • 18. 16 The toolkit includes the following components: Software to filter content out. Out of 24-hour radio programmes, around 50% consists of music. The software identifies and filters out clips with more than 70% of music content. A second filter is then applied to select audio files from radio programmes relevant for analysis that were targeted manually by an analyst as for example, radio shows with people phoning in. Speech recognition software. The software transforms speech to text automatically for Luganda and Acholi (vernacular languages of Uganda). Pronunciation dictionaries were created for those languages showing the commonest sequences of sounds people make as they say each word. With the dictionaries and transcribed recordings, acoustic models were built. The acoustic models reflect all the sounds people utter as they talk about topics of interest. The team worked on language modelling, which is an analysis of how different words in the target languages are related. For example, if the word flood is uttered, the system determines the probability of the next word, e.g. destroy. This needs to be done to help the system identify keywords of interest based on the context of the words being said in the same sentence 18 . Keyword spotter software Spock. A new system for speech recognition was developed with deep learning methods to identify keywords. The system requires less transcribed speech recordings as the software learns words in the new language instead of the entire dictionary. The advantage of this software is that the deployment for new languages is much faster than the previous version. The disadvantage is that the accuracy is lower as the system can no longer search for a set of keywords, nor rely on the context to ensure the right word has been identified. Goldie software. The software distributes the computing resources so that they are used for the most relevant radio content targeted in the ranking of radio content. An interface allows analysts to identify, tag and translate audio clips. John Quinn, Artificial Intelligence advisor at UN Global Pulse. 18 To facilitate the data mining process, the team at the Lab mapped out the programmes of 92 radio stations. It is estimated that there are 292 19 operational FM radio stations in Uganda, which means that the team analysed the content of one third of them. The team organized them in the following categories: Uganda Communications Commission (UCC). 19
  • 19. Gold list includes programmes with grassroots discussions and public participating by phoning the radio studio. Silver list includes programmes with radio studio discussions with grassroots, local/national/ regional officials and public participating by phoning the studio. Bronze list includes programmes of news reports. Black list includes programmes on sports, celebrity gossip, strictly musical shows or similar. 17 The team estimated that every day, an average of 20,000 to 25,000 20 people voice their opinions and share reports on radio shows in Uganda. It is presumed that people who participate in radio shows are outspoken members of society, who can afford airtime for a local call, and that the majority of these people are men. An analysis of the biases of the data was conducted to inform the methods of analysis and the interpretation of the results. The following biases were identified in the data: Estimation by Pulse Lab Kampala/UN Global Pulse based on analysis of radio content in a sample size of 92 radio stations (2017). 20 Representativeness and biases The accuracy of the transcription software is not 100%. As a result, relevant content might be missed out. Intermittent or weak radio signal might reduce the accuracy of the transcription software. Radio call-in shows might be sponsored by international assistance, NGOs, or government, which might introduce a bias in the selection of public opinions to be aired. While radio presenters moderate public discussions, it is expected that their personal agendas might influence the public discourse. The translation process into English might introduce interpretation of the public discourse. To reduce the amount of data to be processed and increase the speed and accuracy of the final results, radio programmes in the Bronze and Black lists were excluded from analysis using machine filtering.
  • 20. “…now South Sudan, there is a war and you see that many refuge seekers come here in Uganda. And when every person walks, he walks with his diseases and problems…and you see that puts us on alert. Now we also see, that there are some districts which have not immunized… now reports show us that we have almost 200,000 children who are not immunized. Now, if those children stay there when they have not been immunized they became dangerous to others. It can be the source of the disease spreading to other areas…”. A listener explained (West Nile, October 2017) to the audience: “…It may not have been refugees, but we are already seeing stock out of ARVs, of recent we also saw stock out of anti TB drugs… we are even so scared that whether the government will be able to meet the cost or buy more of these ARVs for the refugees…”. A community leader (Northern Region, September 2017) explained on radio: “…now you notice that there is increased murder in Uganda especially of women... So, all of the refugees have run into Uganda. Uganda has opened all its borders just for anyone to come in. For this reason, we do not know the people that we are living with in our surroundings. If you look at this tribe X (name of a South Sudanese tribe), their population is way beyond already in Uganda. At this moment, this is a security threat to all of us...”. A listener (Central region, September 2017) explained: “…I was actually tempted to think that the source of the anthrax outbreak in West Nile was due to the animals that entered our country (to refugee settlements) without being screened…”. A government official (Central Region, September 2017) said: 18 Results To foster peaceful, just and inclusive societies which are free from fear and violence, we need to better identify changes in the behavior of population groups that might pose a threat to others in time. The proposed method is to use qualitative reports from public radio discussions to feed early warning systems in near real time. The type of content identified as valuable to feed these systems was categorized under the following categories: rumors and misconceptions, social tensions and testimonials that can cause social alarm. Detecting rumors and misconceptions Many challenges for sustainable peace are the result of collective reactions powered by rumors and misconceptions. Social tensions can rise as a result of rumors that spark them, and early identification is essential to be able to effectively address them. These are some examples:
  • 21. Social tensions usually have long-term historical, cultural or religious roots and build up over time. Sudden changes in contexts affecting societies might spark these tensions and also create new ones that will again leave a print in the collective memory. Identifying emerging social tensions and the circumstances that spark them is critical to respond promptly. Detecting social tensions A community leader (Northern Uganda, September 2017) expressed: “…now if we are to ask ourselves, what could be the reason for insecurity within the country? You are going to find out that Uganda has become a regional hub for thugs and all the thieves within the region…”. Two radio presenters (m) (Northern Uganda, September 2017) discussed: “…(m1) when refugees are coming, we should be assured that there could be other risks that result from their coming as refugees. (m1): Yes, we have for examples of diseases. (m2): Not only that, you know that these refugees come with their culture and this culture is not in Uganda here and you are aware that in South Sudan, these people have guns and the whole community too. (m1) So, they are familiar with the gun. (m2) Yes, for that reason we fear that they will enter with their guns into Uganda…”. A community leader (Central Region, July 2016) explained: “… over 300 Ugandans have been able to run from the country of South Sudan after an internal war erupt again inside that country… the leaders in the parts of Moyo also expressed worry because of the illegal guns that are continually being brought into their region and the all country as well…it led to the residents especially the Madi to come out and complain to the security, showing that there are guns that enter in their regions in unlawfully ways…”. A radio presenter (West Nile, December 2017) explained: “…leaders in Arua district are appealing to the various institutions like the church schools and the individuals to partner with the government to restore the environment that has been destroyed by the presence of refugee in the district. The refugees who have settled in West Nile and northern region negatively contributed to the destruction of trees and road network...”. 19
  • 22. Detecting reports that can cause social alarm Individual testimonies can cause social alarm when they touch upon underlying structural issues affecting societies in a negative way. Collective reactions to alarming testimonials might occur in a peaceful way or with violence. A radio presenter informed (West Nile, October 2017): “…number of refugees now in Imvepi refugee camp is around 133,000. X( name) who is the field officer for Arua said that the reason why this is happening is because more names than people are being written (registered)…”. A listener (p) and a community leader (Lo) (West Nile, October 2017) explained: “… (p) - In the past, level one registration was done at collection points, which promoted a malpractice of one person registering many times levying burden on the Aid Agencies…(Lo): We are trying to help each other with Office of the Prime Minister and government to make sure the systems are well established to address double registration here…”. A listener (West Nile, November 2017) shared with the audience: “…she delivered…and she recognized the sex of that baby, a baby girl…how comes that you people bring a dead boy? I need my daughter. Then from there shortly that doctor explained to her that now if you want me to go and look for that baby, give me 20,000 (Ugandan Shillings), so that I am in position to go and look for your baby. The woman said she is a refugee-she can't afford 20,000 (Ugandan Shillings)-where will she get that money from? She started to cry…”. An official explained (West Nile, November 2017): “…3 men, calling themselves (XYZ names) had come to recruit clandestinely the youth by promising them jobs, money, all that…. the man arrested confessed to recruiting youth to participate in rebel activities in South Sudan… Two of the youths gave in their statement, confessing that truly they escaped from that training ground…”. 20
  • 23.
  • 24. DEVELOPING A NEW GENERATION OF ANALYSIS TOOLS: QATALOG UN Global Pulse developed a data mining tool that UN personnel and partners can use to inform development, humanitarian and peace efforts, called QataLog. The tool allows users to extract, analyse, and visualize data from social media and radio broadcasts. The interface is built using D3, a JavaScript visualization library and database management tool. QataLog stands for: Q A T A Query Assign Tag Analyze Functionalities Users define the parameters for the extraction of public content for analysis. Elements of the query include: timeframe of analysis and keywords. To facilitate the selection of keywords on social media, trending terms are identified automatically. Also, terms mostly associated with the keywords selected are automatically identified. Users can work in groups and assign tasks or content to be analysed by team members. Users define their own tags and use them to organize the content in categories to facilitate the analysis. Users access raw data that has the parameters selected. To facilitate analysis, the following functionalities are available: a) through an integration with Google Translate, rough text translations are automatically generated for social media content; b) automatic geotagging of social media posts assigning a location tag to posts containing place names is done automatically; c) raw data extracted for analysis can be transferred to a spreadsheet and d) basic visualization of analysis results (volume and trends over time) is available. Limitations Data mining applied to low-resource languages presents a challenge and affects the accuracy of the analysis tool. In some cases, false matches are identified during the mining process. For example, refugee in Acholi is translated as luring ayela, the literal meaning of which is runners of problems. As a result, discussions about athletes and problems in general are selected with the English keyword refugee. Another example is the term HIV/AIDS that is translated in Lugbara as two jonyo, that is literally translated as slimming disease 21 . As a result, conversations about disease or slimming in general are selected automatically. To correct this bias, manual analysis is required to identify false matches. John Quinn, Artificial Intelligence advisor at UN Global Pulse. 21 22
  • 25. Benefits analysis QataLog allows users to conduct analysis of big data from social media and radio content at a large scale to inform humanitarian and peace efforts, among others. The exciting aspect is that it captures people’s voices from places where traditionally very sporadic and unreliable data is collected at a lower cost than other means of doing analysis BENEFITS OF QATALOG FOR RADIO AND SOCIAL MEDIA TOOL NO TOOL TARGETING RADIO CONTENT ACCESS TO RADIO BROADCAST FILTERING OUT MUSIC CONTENT MULTIPLE RADIO STATION ANALYSIS REAL TIME TRANSCRIPTION OF CONTENT An analyst manually listens to radio stations to extract relevant content on a given topic It is not possible to filter out music on the radio An analyst automatically targets all relevant content on a given topic from unlimited stations Music on the radio is automatically filtered out An analyst can analyse a maximum of 1 radio station in real-time An analyst can analyse more than 10 radio stations in real-time Transcription is limited because the analyst has to rely on note-taking and memorisation Easier, faster and more reliable transcriptions Radio broadcasts can be accessed from remote locations Analysts have to physically be in the same location as the radio station 10 1 AUTO 23
  • 26. TOOL NO TOOL ANALYSIS OF FACEBOOK GROUPS A limit of 20 to 30 groups are analysed daily An unlimited number of groups are analysed daily GEOLOCATION OF POST A limit of 20 to 30 posts are geolocated daily An unlimited number of posts are geolocated daily ANALYSIS OF TRENDING TOPICS TRANSLATION FROM SOMALI TO ENGLISH FACEBOOK COMMENTS Open up GoogleTranslate from the browser, copy and paste the source text for translation One single click is required to translate the text Each Facebook comment must be individually copied and pasted into a spreadsheet One click is needed to download an unlimited number of Facebook comments to a spreadsheet ANALYSIS OF TRENDS STATISTICS ON SELECTED MESSAGES TARGETING COMMENTS Feeds with reoccuring words within the last 48 hours suggests trending topics 500 messages are selected and tagged per day Automatically counts total tagged messages per day Automatic targeting of posts and comments containing keywords Posts are selected manually. Reoccurring hashtags, likes and replies suggests trending topics 50 messages are selected and tagged manually per day Manually counts up to 50 tagged messages per day Manual search of keywords using the facebook search function & comments reviewed one at a time 24
  • 27. United Nations Development Group (2017). 22 25 INTRODUCING ETHICS TO ANALYSE PEOPLE’S VOICES New technologies and new methods of analysis pose challenges to policy, legal frameworks and ethics. The UN Secretary-General's Strategy on New Technologies calls for overcoming these challenges and reconciling interest on ethics to use frontier technologies. Although privacy norms have been long established to protect personal data from misuse and ensure individual privacy in the digital world, ethics has become an additional tool in AI applications used to protect fundamental human rights. A recent example in which ethics was included in the United Nations (UN) policy is the Guidance Note on Big Data for the achievement of the 2030 Agenda 22 adopted by the UN Development Group. The note, which UN Global Pulse helped develop, contains a set of principles to ensure that data ethics is included as part of standard operating procedures for data governance. UN Global Pulse also builds ethical considerations into its data practices by conducting a risks, harms, and benefits assessment, which helps identify anticipated or actual ethical and human rights issues that may occur during a data innovation project. The assessment considers the proportionality of potential benefits compared to risks of harm from data use. If the risks outweigh the benefits, the project does not proceed. To conduct the analysis of public Facebook and radio content, the team applied the following data privacy principles: Objective of the analysis A common concern from stakeholders is the potential misuse of data and tools. UN Global Pulse applies a Purpose of Use principle to all its projects: we access, analyse or otherwise use data for the purposes consistent with the United Nations mandate and in furtherance of the Sustainable Development Goals. To protect from risks and harms that can occur to individuals and groups, UN Global Pulse applies the principle of Data Security: we ensure reasonable and appropriate technical and organizational safeguards are in place to prevent unauthorized disclosure or breach of data. Informed consent A common question from partners is whether the analysis conducted on Facebook is done on personal pages or if people calling into talk shows have given consent for their opin- ions to be analysed.
  • 28. When users create a group on Facebook, they can choose between 3 privacy settings: public, closed or secret. Public groups are visible to everyone and people who join that group agree to the chosen privacy setting. In the case of public radio talk shows, these conversations are public. UN Global Pulse applies the Right to Use principle: we access, analyze or otherwise use data that has been obtained by lawful and fair means, including, where appropriate, with the knowledge or consent of the individual whose data is used. Anonymity and re-identification In some cases, the identity of people expressing opinions on public social media or radio could be identified via voice or names mentioned. To ensure that this does not happen, the data we use is anonymised to the extent possible. UN Global Pulse also applies the principle of Individual Privacy: we do not access, analyse or otherwise use the content of private communications without the knowledge or proper consent of the individual. We do not knowingly or purposefully access, analyse, or otherwise use personal data, which was shared by an individual with a reasonable expectation of privacy without the knowledge or consent of the individual. We do not attempt to knowingly and purposefully re-identify de-identified data and we make all reasonable efforts to prevent any unlawful and unjustified re-identification. 26
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