The proliferation and adoption of mobile phones and social media technologies presents new ways of capturing conversations surrounding crisis events in real-time. This allows researchers, analysts, and first-responders to explore events by monitoring many media sources (blogs, photos, web feeds, news sources, and tweets) from one environment. The tragic situation unfolding in South Sudan is complex and evolving rapidly. The rate at which the fledgling state has descended into political and social unrest is distressing and highlights the need for urgent intervention. Thus, having ways to identify and engage influencers and to anticipate and potentially mitigate disastrous scenarios is greatly needed. Using a combination of the data-analysis products available from D8A Group, we’ve been monitoring the unfolding events in real-time to illustrate ways our technology platforms can be used by NGOs, first-responders, civil society organizations and government agencies make data informed decisions in real-time in crisis scenarios.

1. CRISIS
2014 CASE STUDY
Turning Online Activity into Actionable
Insights During Crisis Scenarios

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Data-Driven Crisis Monitoring
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Real-Time Analysis and Data Mining
of the 2014 South Sudan Crisis
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By Jon Gosier
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TABLE OF CONTENTS
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Data-Driven Crisis Monitoring
Real-Time Media Monitoring
Contextual News Discovery
Momentum
Real-Time Zeitgeist
Filtering by Keyword Exclusion
Keyword and Phrase Networks
Identifying Influencers
Sentiment Analysis
Geography Trends and
Locations of Interest
Predictive Analytics
Risk Mitigation and the Timing
Of Information
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CONTACT
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D8A Group
http://d8a.com
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Phone: (520) 301-7906
Email: jon@d8a.com
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Data-Driven Crisis Monitoring
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The proliferation and adoption of mobile phones and social media technologies
presents new ways of capturing conversations surrounding crisis events in realtime. There is high demand for products that allow researchers, analysts,
journalists, and first-responders to explore events by monitoring many media
sources (blogs, photos, web feeds, news sources, and tweets) from one
environment.
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As a real time example, we can look at the tragic situation unfolding in South
Sudan, which is complex and evolving rapidly. The rate at which the fledgling
state has descended into political and social unrest is distressing and highlights
the need for urgent intervention. Thus, having ways to identify and engage
influencers and to anticipate and potentially mitigate disastrous scenarios is key
to timely intervention.
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Using a combination of the data-analysis products available from D8A Group,
we’ve been monitoring the unfolding events in real-time to illustrate the ways our
technology platforms can be used by NGOs, first-responders, civil society
organizations and government agencies to make data informed decisions in realtime crisis scenarios.
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The solutions used for this analysis include:
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SiftDeck: a product that connects online conversations to the people,
places, and things being referenced offline. This helps organizations
manage real-world risk to predict and avoid their offline assets from being
threatened (think staff, office locations, or property).
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Themes: a product that allows users to visually sort through large
amounts of text data or streaming data to surface patterns and trends in
the content. It allows for the visual navigation of real-time data using
search, word trees, keyword & phrase network analysis, and various
filters.
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Muxboard: a product with remixable analytic dashboards that allows
researchers to apply various algorithms and third-party APIs to real-time,
ever-evolving data sets using drag and drop ease. Muxboaard makes it
easy to quickly create dashboards for different scenarios, each with
intricate customizable analytics.
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Real-Time Media Monitoring
The primary purpose of using technologies like the D8A suite of analytic products
is to monitor and capture real-time data for forensic analysis and research. D8A’s
products work across multiple communication channels.
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Though most users are primarily interested in analyzing Twitter’s real-time global
data stream, our products work with mobile data streams (text messages), news
articles and headlines, blogs, RSS feeds, JSON feeds, email, and can hook
virtually any API made available. This makes our products flexible for any type of
online activity monitoring.
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The added advantage of D8A’s particular set of products is the ability to research,
sift through, and sort data streams in real time, allowing organizations to make
data-driven decisions while events are still unfolding.
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Themes
SiftDeck
Muxboard
Total
Day 0 - Jan 8
0
0
0
0
Day 1 - Jan 9
7,933
4,348
2,000
14,281
Day 2 - Jan 10 14,936
8,537
8,000
31,473
Day 3 - Jan 11 23,821
17,056
9,869
50,746
Day 4 - Jan 12 27,689
21,522
11,127
60,338
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Between Wednesday evening on January 8th and Sunday January 12th a total of
60,338 tweets were archived. Based on the amount of data filtered our products
filtered in just four days , there’s more information than would ever be possible to
track individually; to do so would be more time-consuming than it would be
productive.
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The keywords tracked were various terms of interest, South Sudan,
Sudan, juba, JubaCrisis, SouthSudanCrisis, Bor, Ichoosepeace, EastAfrica, Leer,
Malualkon, Turalei, Nasir, MyTribeIsSouthSudan as well as the hashtag
variations of each.
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These terms could be tracked individually, or in Boolean combinations (South
AND Sudan, Sudan AND NOT football etc.). Tracking variations of how these
terms might appear allows analysts to tell our products to aggregate very specific
types or combinations of information, making the results more useful and
relevant to their work and valuable to their organizations.
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The image above illustrates how all the raw information of our searches is
accessible to analysts. They do not have to, and are encouraged not to, simply
trust the algorithmic analysis we provide. All data can be viewed individually or
exported into other environments (like Excel) where further analysis can be
performed.
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Contextual News Discovery
SiftDeck learns to aggregate news headlines based on keywords parsed from
aggregated content. This is different from only aggregating content based on the
keywords users enter because it provides a contextual stream of headlines
based on the real-time conversation. In other words, SiftDeck recommends
potentially related news headlines that a user may not even be aware of. So it
serves as a real-time discovery and recommendation engine.
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This feature tries to answer the question: “what if I don’t know what I’m looking
for?” Rather than the user programming every single detail into our products,
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they learn from both the user and the content creators to make new suggestions
of which news items might be relevant to the research underway.
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Momentum
Momentum is the term we use to refer to the qualities of a conversation. Does
the conversation activity seem to be building or slowing? Are new people joining
or are they leaving? Are the people involved from the beginning conversing more
or less than they were from the start? Which keywords, influencers, and
communication channels are leading the conversation?
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The image above was snapped at 2:58 PM EST on Friday, January 10, 2013 and
chronicled the drop and eventual rebound of momentum surrounding the various
keywords being tracked over the previous hour.
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Likewise, looking back over the previous days or weeks shows that there are lulls
and bumps in the flow of the conversation over time. This directly correlates to
events occurring in the real-world and the virality of news spreading online. For
instance, we know from looking through the data that this uptick in activity
correlates with when the U.S. made statements indicating that American troops
might be deployed in Sudan.
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Real-Time Zeitgeist
What are the recurring themes and phrases in a real-time conversation? The
words, phrases, names, and locations that repeat may allow analysts to draw
correlations between seemingly unrelated conversations.
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Were one not even paying attention to the situation in Sudan, if a word cloud all
of a sudden started surfacing words like ‘conflict’, ‘prisoners’, ‘troops’ and
‘army’ (like in the above image) they could easily determine a dangerous
situation might be unfolding in the region of focus.
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This ability to actively monitor the ‘zeitgeist’ or thematic relationships between
conversations happening across disparate communication channels often proves
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powerful for organizations who have to plan suggested interventions or activities
in real-time.
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Filtering By Keyword Exclusion
More importantly, these word clouds make it possible to conditionally filter out
conversations that actually are unrelated.
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In this case, the recurrence of the word ‘Munich’ in data streams monitoring
conversations about Sudan was because of a football match between Sudanese
and German teams1. After identifying messages that are skewing the research,
with our product,Themes, the user can simply click on the word (in this case
‘Munich’) and opt to exclude all data where Sudan and Munich (or other
unwanted words) appear in the same sentence, while keeping all other data
intact.
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Bayern
Start
2014
on
Winning
Note
http://news.sudanvisiondaily.com/details.html?rsnpid=230943
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Organizations using other products for social media analytics forget that many
such tools don’t allow for the selective ‘cleansing’ of datasets to remove
misleading or non-relevant content.
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Keyword and Phrase Networks
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Themes’ network graphs of words and phrases can provide a powerful means
for visually controlling the underlying dataset. In this case, clicking on any word in
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the above graph, gives you the option to focus only on content that contains that
word, or only on the content that doesn’t contain a particular word. A researcher
might want to only view content where the phrases ‘troops’, ‘Sudan’, ‘president’
and ‘usa’ appear together. If so, it’s simply a matter of point and click, and the
data is re-organized to fit that criteria. Terms can just as easily be excluded from
the dataset.
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Identifying Influencers
Monitoring digital conversations allows organizations to identify potential ‘thought
leaders’, activists, actors or other people who may be influential in a given
scenario. While it’s usually impossible to verify exactly who these actors are, and
what their motives are, it’s useful to identify them, to conduct strategies for
engagement and outreach.
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In the real-time conversation regarding South Sudan the following non-news
outlets were only some of those identified as potential influencers:
@Juba_Horan, @juba_ddon, @Oxenstiema_IRL, @moseswasamu,
@MundekeM, @PeterAcheyoLive, @AlMasryAlYoum, @Evalopa
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Having this information allows analysts to follow the public conversations of
specific individuals.
For instance, if any of these (or other) individuals are
civilians actually in the country of interest, quickly building up such a list of
trusted sources might be something the benefits the media monitoring activities.
Analysts can then refocus their analysis on the contributions of specific
individuals (or groups of individuals) as opposed to all individuals.
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Sentiment Analysis
Sentiment analysis is a method of measuring the emotional tone of written text
using computer programs. It attempts to weight different words in a body of text
against one another, to ultimately provide a ‘score’ to the whole body of text that
is either positive, negative, or neutral.
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Why is this useful? Because it allows users to algorithmically determine whether
an online conversation is skewing positive or negative in tone.
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In the image above, it’s easy to quickly see that of the more than 6,972
messages analyzed in the first column, 1679 (25%) have been marked as being
negative in tone, while 700 (10%) are positive. If the analyst wants to focus on
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the dataset that’s been marked negative, they simply click on that area of the
graph.
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The content and related analysis is then sorted to focus on the ‘negative’ content.
To give a usecase scenario, this would allow a researcher to view a list of
influencers leading the negative tone of a conversation.
In the past, this has
allowed our users to identify individuals whom they would qualify as the
‘antagonists’ or ‘instigators’ who might be inciting violence or other unwanted
activities. Being able to sort data in this way provides a powerful lens of context
and discovery. More importantly, it allows analysts to constantly ask questions of
the data itself through our simple drag and drop interface.
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The above screenshot looks at only the analysis of content negative in tone from
a different data set than the previous image. You can see that 379 messages
represent the negative content, of which 376 comes from Twitter, 3 items come
from Google News, and we have a list of potential conversation influencers, as
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well as how much content they’ve contributed to the overall conversation.
Analysts can now reach out to them directly, or begin monitoring these new
sources of interest. Again, all of this is being done in real-time.
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Geography Trends and Locations
of Interest
Connecting this type of online research to offline activities and actions is a big
portion of why people use data products like the ones provided by D8A. We use
the social graph and natural language processing to algorithmically map various
locations of interest to researchers.
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This might serve as a point for additional research (ex. “How does India relate
what’s going on in Southern Sudan?”) or it might indicate hotspots of relevant
activity, as indicated in the map below, where the discussion of refugees fleeing
to Kenya and Uganda lead to those countries receiving pins on the map.
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The power of this information is that even with the most minimal knowledge of a
situation, the maps and graphs generated tell a story. While knowing the context
and having professional expertise in the given subject matter is absolutely
necessary, when such knowledge is coupled with these kinds of visual data
exploration tools, it’s possible to make the job of experts faster, more nuanced
and efficient.
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D8A’s products (SiftDeck, Muxboard/MetaLayer, and Themes) are not meant to
replace professional analysts and researchers, but to save them incredible
amounts of time,
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Predictive Analytics
When all of our products are combined, it’s possible to anticipate events,
demands, or activities that have not happened yet. This is type of anticipatory
response to data is based on an area of research called predictive analytics.
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By combining all of our insights into an informed narrative, researchers might be
able to determine the correct actions to take well before it’s obvious. As with all
systems, it’s possible these predictions can be wrong so rather than give
researchers objectives, our products serve to provide the appropriate information
for informed conversation and action.
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In a scenario where an analyst is viewing multiple dashboards in an unfolding
scenario, it’s possible to piece each of these different insights together to suggest
action and give reasons for that action.
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In the case of South Sudan, well before these stories played out in the media,
our team identified several influencers in-country and around the world. We knew
that the situation was no longer contained to just South Sudan, but was now
affecting the whole of the East Africa region; we knew that there appeared to be
a rapid build of momentum in the conversations on the evening of January 9th
leading into the 10th, and we know that the thematic tone of conversation was
trending towards some sort of conflict. We also had the related breaking news
stories confirming as much.
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Risk Mitigation and the Timing of
Information
While it’s possible to come to the same conclusions in a number of other ways,
the timing of information often dictates its value, as well as the time it takes to
aggregate all data sources to predict future conclusions.
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For a Wallstreet broker, receiving information that the CEO of a major company is
about to be fired might indicate he needs to sell his position in that companies
stock. However, receiving the information after the fact (ex. “the CEO was fired
yesterday”) is an entirely different scenario. The first scenario allows him to
mitigate risk in anticipation of a potential disaster. The other scenario allows him
to make the same decisions, but the information is less valuable because he has
less control over how the news affects things. A portion of the risk is already
realized, thereby making the information less valuable. For the Wallstreet broker,
the value of information could be valued in the millions or billions of dollars. For
humanitarian organizations and journalists, the type of risk we try to help them
mitigate might be measured in loss of life & property, or at the very least, quality
of life for the people affected by these events.
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D8A’s products are designed to shift critical analysis of any situation, event, or
phenomena from a retroactive exploration, to a real-time one. In the above
scenario, the case was made that value of information is very much related to its
timing.
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Thus, even if our products only slightly move the needle in regards to the time of
information, there is a direct correlation to the amount of value that analysis
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provides. Knowing how to potentially affect a situation in real-time can be
exponentially more valuable than waiting for everything to play out, only to deal
with the aftermath.
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While such actions need to be tempered with consideration for culture, context,
privacy and law, there is value in time-shifting the research. It gives organizations
the informed option of not waiting, allowing them to potentially influence more
people, provide more help when its needed, and ultimately affect (and possibly
save) more lives.
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