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 companies, PR firms, marketing agencies, political groups, celebrities, and NGOs to make data informed decisions in real-time crisis scenarios. In this case study document, we analyze breaking news scenarios involving Chris Christie's Brigegate scandal, Kerry Washington's appearance at the Golden Globes, and the Knight Foundation who we weren't aware had any news events at the time, but we quickly became aware of two through the use of our software. The primary purpose of using technologies like the D8A suite of analytic products is to monitor and capture real-time data for analysis and research. They are also predictive, helping to surface trends, patterns, and happenings before one might find out about them otherwise. D8A’s products work across multiple communication channels.

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2014 CASE STUDY
HOW TO MINE ONLINE AUDIENCES
TO IMPROVE PUBLIC RELATION
STRATEGIES
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Social Data Monitoring with D8A
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Real-Time Analysis and Data Mining Online Audiences
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TABLE OF CONTENTS
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Data-Driven Audience Monitoring
Real-Time Media Monitoring
Case Studies
1- Public Figures & Reputation
Monitoring
2- Real-Time Opinion Polling for
Political Groups
3- Corporate Awareness
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 Audience Monitoring
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The proliferation and adoption of mobile phones and social media technologies
presents new ways of capturing conversations surrounding events in real-time.
There is high demand for products that allow companies, celebrities, public
figures and others to explore events by monitoring many media sources (blogs,
photos, web feeds, news sources, and tweets) from one environment.
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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 companies, PR firms, marketing agencies,
political groups, celebrities, and NGOs 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 remixable analytic dashboard 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 tracking different people or brands, 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 analysis and research. They are also
predictive, helping to surface trends, patterns, and happenings before one might
find out about them otherwise. 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 also 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 companies to make
data-driven decisions while events are still unfolding.
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Case Study 1: Reputation
Monitoring for Public Figures
Using our products is like having a ‘crisis management’ consultant on staff at all
times - much like the one Kerry Washington plays on the hit TV show Scandal.
Using Kerry Washington herself, we did some real-time research on her following
the Golden Globe Awards in January of 2014.
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We began tracking Kerry Washington shortly after the 2014 Golden Globes
where she was nominated for Best Performance by an Actress for her work on
Scandal. So it’s no wonder that her name increasingly appeared alongside other
celebs like Cate Blanchet, Amy Adams, and Idiris Elba.

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Hrmm, what’s up with that Idiris Elba connection….could it be a SCANDAL!?! No,
actually it turns out both Idiris and Kerry revealed the fact that they were
preggers on the Globe’s red carpet. Idiris, debuting his pregnant girlfriend, Kerry
revealing her own ‘baby bump’ with her husband.

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This shows up in the data, the keyword maps allude to “Elba” and “Washington”
and couples making appearances at the “Golden Globes”.

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Case Study 2: Real-Time Opinion
Polling for Political Groups
In early January 2014, Chris Christie became linked to a scandal that became
known as ‘Bridgegate’. Many organizations concerned with politics have faced
similar scenarios where the integrity of their candidate, party, or staff is called into
question.
For organizations working in the political sphere, our products help
them monitor in real-time exactly what is going on, how the public is responding,
and suggest ways that they might intervene to change the story.
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Our case study monitoring Chris Christie during this timeframe was in many ways
fascinating.
The real-time feed of contextual news offered insight into what the press was
saying. This is a panel that can appear next to any streaming data feed, so that
analysts can compare trends in social media activity to trends in news media
coverage.
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Unsurprisingly, as the news leaked of the story, the partisanship of online
audiences increased. See the above image where ‘liberals’ is one of the
strongest signals.
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Perhaps more interesting is some of the finding about Rachel Maddow’s ratings
boost from the scandal or the Tea Party turning on Christie.

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But the information a candidate like Christie wants to know in an unfolding crisis
is whether or not the scandal seems to be picking up steam or slowing down, and
how public opinion is changing in the political scandal’s wake.
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Our products can account for this, too. On the evening of January 14th 2014, the
scandal was still up and down (hour on hour), but the over-arching trend was flat.
In the chart below, you can also see that the tide of public opinion about the
entire debacle is 34% majority negative versus 5% positive
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Case Study 3: Corporate
Awareness
Companies large and small have a lot to protect when it comes to how they are
represented online. D8A’s products allow them to monitor their various brands,
products, customers, partners, and competitors to ensure that their reputations
aren’t affected by any unfortunate occurrences.
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Companies with operations in multiple states and countries also may want to stay
up to date on what is happening in multiple locations.
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The Knight Foundation is a philanthropic organization that funds technology
innovations for news and civic engagement communities around the globe.
Apparently, the data set illustrates that “Detroit” seems to be high on their list of
priorities.
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Indeed, a quick look at recent headlines of theirs confirm that the Knight
Foundation and other philanthropic partners have pledge more than $330 million
to save the Detroit Institute of Art from shutting down, or selling off its assets.
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I didn’t know anything about this, and heard about it through my social networks.
It was only through the use of our own products that even knew to investigate
Knight’s links to Detroit.
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I also inadvertently discovered that their 2014 class of News Challenge winners
was about to be announced either today or tomorrow. It turns out that it was
“today”, this image is a graphic of our systems on the morning of January 14th,
the same day the Knight Foundation announced the recipients of $2.3 million
dollars in funding through it’s News challenge.
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Why does “tomorrow” appear bigger than “today” you might ask? Because our
systems deal with real-time data, there was a fair amount of information in the
archive from the day before. Because I was looking at the data in the morning,
the old data outweighed the new as a signal, something an analyst would know
to correct for. By the end of the day the older data would diminish.
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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,
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 chronicles the drop and eventual rebound of momentum
surrounding the various keywords being tracked.
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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.
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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 looking for a scandal involving Chris Christie, if a word cloud
all of a sudden started surfacing words like ‘scandal’, ‘bridge’, ‘taxes’ and
‘campaign’ (like in the above image) they could easily determine a big story might
be breaking and take action.
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This ability to actively monitor the ‘zeitgeist’ or thematic relationships between
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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 non-relevant words appear in the
same sentences together, while keeping all other data intact.
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Bayern
Start
2014
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Note
http://news.sudanvisiondaily.com/details.html?rsnpid=230943

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Organizations using other products for social media analytics often 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
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.
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In the above example, a very large dataset was used to show how select key
words appear in high frequency in the same datasets. But by clicking on each
word, and choosing to focus or exclude certain ones, the dataset is refined as
needed.
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A researcher might want to only view content where the phrases ‘taxes’, ‘liberals’,
‘scandal’ and ‘work’ appear together as it relates to Chris Christie. If so, it’s
simply a matter of point and click (the collection of keywords in the bottom right),
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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Having this information allows analysts to follow the public conversations of
specific individuals.
For instance, if any of these (or other) individuals are
influential bloggers, employees of other companies, investors, journalists etc.
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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
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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
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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. For example, how does a
spike in activity in Texas relate to Chris Christie? Turns out that’s where the
outspoken branch of a major division of the Tea Party is based.
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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 broader
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 one use of our products in South Sudan, well before stories played out in the
media, our team identified several influencers in-country and around the world.
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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. For brands or public figures, their
reputation is directly correlated with their value and ability to derive revenue from
customers.
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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
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 great value in time-shifting the analytics so that
companies can react to events more readily because they were able to anticipate
potential risk scenarios.