MEASURING GLOBAL ATTENTION: HOW THE APPINIONS PATENTED ALGORITHMS ARE REVOLUTIONIZING INFLUENCE ANALYTICS

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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.

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. 2

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. 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. 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. 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. 6

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. 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 Figure 6: David Linthicum influences Marc Benioff, as shown by the directional arrow pointing at Benioff. Figure 7 7

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 8

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