Data-Driven Crisis Monitoring: Turning Online Activity into Actionable Insights During Crisis Scenarios

2014 CASE STUDY

CRISIS

Turning Online Activity into Actionable Insights During Crisis Scenarios

� 2Real-‐Time Analysis of Social Media Conversations On the South Sudan Crisis

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� !Data-Driven Crisis Monitoring

!Real-Time Analysis and Data Mining

of the 2014 South Sudan Crisis !

By Jon Gosier !!!!!!!!!!!

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!!TABLE OF CONTENTS !Data-Driven Crisis Monitoring 3 Real-Time Media Monitoring 4 Contextual News Discovery 5 Momentum 7 Real-Time Zeitgeist 10 Filtering by Keyword Exclusion 11 Keyword and Phrase Networks 12 Identifying Influencers 13 Sentiment Analysis 15 Geography Trends and

Locations of Interest 17 Predictive Analytics 19 Risk Mitigation and the Timing Of Information 21 !

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CONTACT !D8A Group http://d8a.com !Phone: (520) 301-7906 Email: [email protected]

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Data-Driven Crisis Monitoring !!The proliferation and adoption of mobile phones and social media technologies

presents new ways of capturing conversations surrounding crisis events in real-

time. 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.

!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.

!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 real-

time crisis scenarios.

!The solutions used for this analysis include:

!• 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. • 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.

!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.

!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.

!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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!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.

!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.

!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.

!

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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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.

!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.

!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.

!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.

!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 teams . After identifying messages that are skewing the research, 1

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 1

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.

! 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.

!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

!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.

!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.

!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.

!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.

!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.

!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.

!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.

!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.

!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.

!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.

!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.

!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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