Data Mining Online Audiences with D8A Group

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HOW TO MINE ONLINE AUDIENCES TO IMPROVE PUBLIC RELATION

STRATEGIES

2014 CASE STUDY

BRANDS

� 2Real-‐Time Analysis and Data Mining Online Audiences

� !Social Data Monitoring with D8A

!Real-Time Analysis and Data Mining Online Audiences

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!!TABLE OF CONTENTS !Data-Driven Audience Monitoring 3 Real-Time Media Monitoring 4 Case Studies

1- Public Figures & Reputation Monitoring 5

2- Real-Time Opinion Polling for Political Groups 7

3- Corporate Awareness 12 Contextual News Discovery 15 Momentum 16 Real-Time Zeitgeist 18 Filtering by Keyword Exclusion 19 Keyword and Phrase Networks 20 Identifying Influencers 21 Sentiment Analysis 23 Geography Trends and

Locations of Interest 25 Predictive Analytics 27 Risk Mitigation and the Timing Of Information 29 !

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


Data-Driven Audience Monitoring !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.

!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.

!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). • 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 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.

!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.

!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.


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.


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.

!Perhaps more interesting is some of the finding about Rachel Maddow’s ratings

boost from the scandal or the Tea Party turning on Christie.


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.

!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.

! 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.

!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.

!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.

!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.

!This ability to actively monitor the ‘zeitgeist’ or thematic relationships between

conversations happening across disparate communication channels often proves


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 non-relevant words appear in the

same sentences together, while keeping all other data intact.

!! Bayern Start 2014 on Winning Note 1

http://news.sudanvisiondaily.com/details.html?rsnpid=230943


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.

! 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.

!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.

!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.

!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


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


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.

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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.

!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.

!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 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.

!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. For brands or public figures, their

reputation is directly correlated with their value and ability to derive revenue from

customers.

!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.

!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.

Technology

Data Mining Online Audiences with D8A Group