Appinions has developed an algorithm to measure influence by tracking how attention flows through networks as people react to and spread opinions. The algorithm analyzes data from various structured and unstructured sources to build influence maps showing connections between influencers. Influence scores represent the total attention received directly and indirectly. The algorithm accounts for different types of evidence and allows influence to dissipate over several connections to model real-world dynamics more accurately than simple link-based models.

1. WHITE PAPER
MEASURING GLOBAL ATTENTION:
HOW THE APPINIONS PATENTED ALGORITHMS
ARE REVOLUTIONIZING INFLUENCE ANALYTICS
Overview
There are many associations that come to mind when people hear the word, “influence.”
Power. Prestige. Prominence. However, what it boils down to is the ability to capture
peoples’ attention. When an individual expresses an opinion in traditional or online
media, which then travels beyond its original source, that individual has managed to
capture attention. How far the opinion travels, and over what networks and media, help
define how much influence an individual is generating based on that opinion.
In this white paper, Appinions’ data scientist, Stewart Siu and Chief Technology Officer,
David Pierce shed light on our approach to influence from a technical point of view.
Copyright © 2013 Appinions. All rights reserved.

2. Introduction
The Appinions influence marketing platform combs through more than six million sources daily, extracting
the most resonant opinions on given topics. Influence is evidenced as a myriad of activities online and
offline, across traditional and social media, through actions realized in unstructured text and in structured
interactions. Our algorithm deals with the richness and complexity of influence by providing a general
framework for scoring influencers based on the reactions they elicit, regardless of the structure or source
of the interaction. To clarify what is meant by “structure,” and “source,” here are a few examples: quoting
an individual in print (unstructured, offline), liking, promoting or retweeting a post (structured, online),
commenting on a post (unstructured, online). The key distinction between structured and unstructured
text lies in the degree of ease or difficulty required to attribute the text to the original opinion holder as
opposed to the person sharing the opinion.
In order to score across a variety of content platforms and types of interactions, we formulated a general
model of influence using a common abstract unit of measurement -- attention. An individual’s influence is
in proportion to the attention she captures through what she says or writes, and her influence is
measured by the reactions generated by what she says or writes. In this paper we describe the design
and implementation of the attention model of influence.
Figure 1: This image combines the Influence Maps of Marc Benioff, Julie Bort and Christina Farr, which are the subjects
of discussion throughout this paper. The arrows illustrate the directional flow of influence in Appinions’ attention model.
Bruce Rogers
Chief Insights Officer, Forbes
Copyright © 2013 Appinions. All rights reserved.
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3. What We’re Trying to Achieve
We formulated our influence model from the ground up with the following requirements.
1) The model must capture the relationship between people who pass influence on certain topics from
one to another.
2) The model must make use of many different types of evidence of online influence from a wide variety
of sources, such as news, blogs, social media, etc. The model should have the potential to become
richer and more accurate in the presence of new data, while still delivering meaningful results when
the data is incomplete.
3) As a corollary to 2), the model must allow us to continue to excel at processing unstructured text to
understand influence, while taking into account other types of interactions among people.
4) The numerical measures of influence must allow for meaningful comparison among different
influencers and topics.
5) The influence measure should have the interpretation both as a reflection of how the influencer or
topic has performed in the recent past, and as a predictor of what can happen in the very near future
if the dynamics between influencers remain unchanged.
6) The influence measure should naturally reward the diversity of audience and media platforms across
which the influencers’ opinions are spread.
Approach
In this section we elaborate on the attention model of influence. The heterogeneous data originating from
online and offline sources house the influential opinions that form the backbone of the attention
measurement. The attention interactions are construed as a social network, and influence is allowed to
flow through the network to determine influence scores. These processes are described below.
First Step: Keeping Only the Relevant Data
Appinions’ influence scores are always topical; they reflect an individual’s influence about and around a
particular topic. Thus, the first step in every analysis is to isolate the relevant contexts. Finding the
relevant data within a specific context is treated as an information retrieval task: the corpus of contexts
is indexed for text search, and each topic is associated with a search query, which determines the
relevant body of contexts. From these matching contexts, the influence interactions are mined to feed
the scoring process.
Bruce Rogers
Chief Insights Officer, Forbes
Copyright © 2013 Appinions. All rights reserved.
3

4. Second Step: Taming Heterogeneous Data
Our data about an individual’s influence comes in many different forms. For example:
• A Tweet from a speech given at a conference
• A quote in the New York Times
• An opinion from a blog
• A +1 affirmation on Google Plus
Despite the apparent difference in format, we can interpret each piece of the data above as saying,
“Within the context of this subject, here these N individuals are paying w percent of their attention to
another set of individuals.”
The intuition here is that:
1. We can characterize different types of data by choosing different N and w, and we can define a
unifying concept of attention that’s determined by only these two parameters for each piece of data.
2. The more attention someone receives on the subject, the more influential she becomes.
The parameter w varies depending on the type of activity recorded, so the challenge is to establish
approximate equivalence relations between different data types. It depends on a careful analysis of the
method with which we obtain the data and the intended application.
The parameter N is generally easier to calculate when there are explicit reactions. The algorithm
tabulates the individuals involved in conversations as defined by the topic. Such cases form the
backbone of our data. However, since we cannot track all the conversations in the world, as powerful as
our opinion extraction process is, there are many cases when we have to rely on aggregate traffic and
link estimates from different websites and publications. In these cases, N is harder to calculate. We can
estimate prudently how many people are paying attention. To get the best possible results, we pull data
from many sources in the form of pageviews, ranks, number of links, etc. and combine them in a
mathematically consistent way. We noticed for example that top 1% blog websites have a different
distribution from the rest, so the blending of estimates are done with careful attention to any such issues.
Finally, the last part of blending heterogeneous sources involves the identification of the different
personas of the same individual, as the same person can appear in different newspapers, blogs, reposts,
social accounts, etc. We perform Bayesian statistical matching on hundreds of millions of possible
combinations, based on the extracted features about these personas on the media platforms.
Bruce Rogers
Chief Insights Officer, Forbes
Copyright © 2013 Appinions. All rights reserved.
4

5. Third Step: Constructing an Embedded Network
Once we successfully convert our data into evidence of attention, the next step is to connect the dots
and account for the interaction between people. The aggregate of the total attention an individual or
entity receives in the network yields the influence score on a topic. The image below, which was taken
from our platform, shows the top influencers ranked by influence score on the topic of Salesforce.
Figure 2: This screen shot from the Appinions platform results page shows the top 10 of 708 influencers on the topic of
Salesforce. The influencers are ranked by influence score. Details of their opinion volume and credibility are also shown.
Bruce Rogers
Chief Insights Officer, Forbes
Copyright © 2013 Appinions. All rights reserved.
5

6. The following images illustrate the individual influence networks for Marc Benioff, Christina Farr, and
Julie Bort, who are all Salesforce influencers. Their influence scores are highlighted in red.
Figure 3: Marc Benioff’s Influence Map
Figure 4: Julie Bort’s Influence Map
Figure 5: Christina Farr’s Influence Map
The arrows on the edges indicate the directional flow of influence; the circles represent individuals or
entities, except for the following symbol:
This symbol represents the audience of a publisher, and it usually represents the attention of
more than one person. This is where the Appinions platform visualizes the reputation and
credibility of publishers as a reflection of their audience size. For example, metrics here
indicate that there are more readers of an article in the New York Times than in a small blog.
Copyright © 2013 Appinions. All rights reserved.
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7. Influence scores are a measurement of the amount
of attention received, so we can think of the attention
arrows as going in the opposite direction as the
influence arrows. For instance, Marc Benioff has
many influence arrows going out (the screenshot
actually only shows the first page of connections);
each arrow represents recent evidence of influence
(reactions to his opinions), which shows that he has
captured the attention of many people and entities.
Thus, it makes intuitive sense that he has a high
score of 717. By comparison, Julie Bort and Christina
Farr have much lower scores at 105 and 36
respectively, as their influence networks are
considerably smaller and contain less prominent
publishers.
The Appinions model also accounts for indirect
attention, meaning that as Bort and Farr capture
more attention, Benioff receives more attention as
well. We can think of Bort and Farr as potentially
passing Benioff’s opinions to their audience. Benioff
himself actually pays attention to and is influenced
by another individual, as we can see in Figure 6.
Figure 6: David Linthicum influences Marc Benioff, as
shown by the directional arrow pointing at Benioff.
The individual with an arrow pointing towards Benioff
is David Linthicum. The arrow implies that if Bort and
Farr’s scores increase, not only would Benioff’s score
increase, but in addition the increase would also pass
to Linthicum.
The technical details of what fraction of attention is
transferred from one individual to another on these
edges depends on many factors including the
timestamp of the data and the activity level of the
individuals. We will not go into further detail, but it is
natural to ask how far we allow indirect transfer of
attention to travel. We need to recognize that much of
what contributes to opinion formation is hidden from
public view, and how we influence each other is far
more complex than, say, the relationship between
webpages linking to each other. With the exception
of viral content, it is generally difficult to pass content
from one to another; in that sense influence formation
is a more local phenomenon than web surfing. We
can think of our network as embedded in a larger
network, as in Figure 7.
The transfer of attention should not go very far, and
we describe this effect as a dissipation of attention.
A big increase in the score of Bort and Farr will only
lead to a small increase in Linthicum’s score
Copyright © 2013 Appinions. All rights reserved.
Figure 7
Bruce Rogers
Chief Insights Officer, Forbes
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8. This network model allows us to define an influence score for every individual as the expected total
attention she receives. It lets us answer questions of the form, “Suppose Alice gets X amount of reaction
when she goes out and shares an opinion on the subject. Based on our data and all other things being
equal, what is the expected amount of reaction Bob receives when he does the same thing?”
Furthermore, when we aggregate scores of all the influencers in a topic, we get a measure of the activity
level of the topic, so we can then compare the activity of a topic to another for business analysis.
Future Direction
Our framework allows many possibilities for refinement and customization, such as taking into account
correlation between evidences of influence, or including specialized data specific to an industry. This
enables us to adapt to the ever-changing needs of our customers.
About Appinions
Appinions is the only opinion-powered influence marketing platform designed to give companies the unmatched ability to identify,
analyze, engage, monitor and measure influencers. Built on more than a decade of research conducted at Cornell University,
Appinions extracts and aggregates the opinions that generate the most reaction from more than six million sources including blogs,
social networks, forums, newspapers and magazine articles, thus providing a more complete picture of influence. Appinions helps
today’s businesses insert trust into the purchase journey by converting faceless touchpoints into influential trust points™ to optimize
marketing efforts. For more information on the science of influence marketing, visit http://www.appinions.com/.
Bruce Rogers
Chief Insights Officer, Forbes
Copyright © 2013 Appinions. All rights reserved.
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