Abstract:
Systems and methods for leveraging social media data by entities to identify key influencers are disclosed. Monitoring criteria is based on a selected subject-matter. The analysis permits quantification of influencers and takes into account the conversari platforms and the published platforms in the social media.

Description:
This application claims the benefit of Indian Patent Application Filing Number 2731CHE/2013, filed on Jun. 24, 2013, which is hereby incorporated by reference in its entirety. 
     FIELD 
     The present disclosure relates in general to the field of computer networks, and more particularly, to a system and methods for use with social media platforms to determine at least one influencer. 
     BACKGROUND 
     Social media is one the fastest-growing segment. The term ‘social media’, as used herein, means a way to transmit or share information with a larger audience. One of the popular channels of social media is social networks, which are platforms to engage, interact, communicate and collaborate with users. The term ‘social network’, as used herein, means web-based services that allow members to construct a public or a semi-public profile within the boundary of that particular community. Social networks provide a facility to consolidate a list of other members with whom a member may want to share a connection. 
     Social media channels provide platforms to express opinions and share ideas about companies or products, which make social media of utmost importance in corporate communications, public relations and advertising campaigns. This property of Social media, and, in turn, social networks, to engage a large number of members as potential content generators yields massive source of information. Hence, businesses require solutions to monitor the social media for a member&#39;s opinion towards their brands as well as to increase their brand recognition and influence opinions. For example, current guidelines do not mandate any stringent application level security other than encryption of the transmission media. Accordingly, HTTS based interfaces between a handheld computing device and in-store server components are merely designed to ensure that card data is secure during transmission. As a result, with these new types of POS emerging, higher levels of security to protect this sensitive transaction POS data are needed. 
     In order to influence a member&#39;s opinion, companies look to identify the key influencers. The term ‘influencer’, as used herein, means a member who transmits information, through social media channels, that have an impact on other member&#39;s ideas and practices. Members of the social media who get the most attention may be considered as influencers for a particular topic on a social media channel. These members have the power to affect other member&#39;s opinions, and thus their perception of the brand and purchase decisions. Influence is dynamic and depends on variety of factors for example, perceived authority and audiences reach. Influential zones can be found across social media, for example, Facebook®, Twitter®, blogs and forums by applying social network analysis technics. 
     There exist several methodologies for identifying influencers. Trusov, Bodapati et al describe how to identify influencers, given member&#39;s behavior among his network of friends. The approach proposes a non-standard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each member. The approach identifies the specific members who most influence others&#39; activity. Cha, Haddadi, Benevenuto, Gummad et al describe how to measure member influence in Twitter® by comparing three measures: 1) indegree influence, the number of followers of a member, directly indicates the size of the audience for that user 2) retweet influence, measured through the number of retweets containing one&#39;s name, indicates the ability of that member to generate content, 3) mention influence, measured through the number of mentions containing one&#39;s name, indicates the ability of that member to engage others in a conversation. This approach examines how the influential members perform in spreading popular news topics by investigating the dynamics of an individual&#39;s influence by topic and over time and characterizing the precise behaviors that make ordinary individuals gain high influence over a short period of time. Wu, Hofman, Mason, Watts et al describe a method for analysis of ‘lists’ feature of Twitter® to distinguish between elite members and ordinary members to different news topics. This solution examines attention on Twitter® paid by the different member categories to different news topics. Bakshy, Hofman, Mason, Watts et al describe investigation of the attributes and relative influence of Twitter® members by tracking diffusion events. This approach tries to identify influencers by: 1) determining members who have been influential in the past and who have a large number of followers, 2) finding web-links that were rated interesting. Weng, Lim, Jiang, He et al describe analyzing impact of member&#39;s followers and persons that member follows. TwitterRank, an extension of PageRank is used to identify influencers taking the link structure and topical similarity between members into consideration. The underlying assumption of this approach is homophily between members, which mean that twitterers follow other members because of the topical similarity between them and their friends. Agarwal, Liu, Tang, Yu et al describe a method for determining influential bloggers based on analyzing bloggers properties: recognition i.e. number of in-links, activity generation i.e. number of comments, novelty i.e. number of out-links and eloquence i.e. length of post. Moon, Han et al describe a quantitative method for identifying influential bloggers by weighting readers based on homophily and vulnerability with bloggers. This approach is based on the Quantifying Influence Model (QIM), which includes two components: 1) interpersonal similarity, which presents the interaction among bloggers and interpersonal similarity between readers 2) degree of information propagation, which represents how many readers a blogger has, where the readers diffuse the blog posts. 
     The above-mentioned methodologies describe different methods of analyzing social media channels, particularly, conversations in social networks. For the purposes of this disclosure, the social media channel, particularly, social network conversations are referred to as ‘Conversari Platform’. These methods focus on social networks and do not take into account other social media channels, for example, but not limited to, product launches, product evangelizations, conference presentations, content platforms, product recommendations and research papers. This may lead to inaccurate identification of influencers and their estimated influence. For the purposes of this disclosure, this category of social media channels is referred to as ‘Published Platform’. 
     There lies a need to ascertain scores of influencers by taking into account the ‘Published Platform in addition to the ‘Conversari Platform’. The present disclosure improves the accuracy of identifying influencers and quantifying their influence by taking into account the ‘Published Platform’. 
     SUMMARY 
     Aspects of the disclosure relate to a system and method for identifying key influencers in various social media platforms. 
     It is therefore an object of the present disclosure to provide systems and methods for arriving at one or more effective influencers and quantifying their influence in the social media by taking into the ‘conversari platform’ and the ‘published platform’. 
     It is another object of the present disclosure to provide systems and methods for automation of the determinations of the events and information associated with the key influencers. 
     The above as well as additional aspects and advantages of the disclosure will become apparent in the following detailed written description 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The aspects of the disclosure will be better understood with the accompanying drawings. 
         FIG. 1  is an illustration of a prior art; 
         FIG. 2  shows a schematic block diagram to illustrate system for identifying key influencers in accordance with the present disclosure; and 
         FIG. 3  is a flow diagram showing steps typically performed to identify key influencers. 
     
    
    
     While systems and methods are described herein by way of example and embodiments, those skilled in the art recognize that systems and methods disclosed herein are not limited to the embodiments or drawings described. It should be understood that the drawings and description are not intended to be limiting to the particular form disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the appended claims. Any headings used herein are for organizational purposes only and are not meant to limit the scope of the description or the claims. As used herein, the word “may” is used in a permissive sense (i.e., meaning having the potential to) rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including, but not limited to. 
     DETAILED DESCRIPTION 
     Disclosed embodiments provide computer-implemented methods, systems, and computer-readable media for leveraging social media platforms to arrive at one or more effective influencers. The embodiments described herein are related to identification of influencers by the combined influence of ‘conversari platform’ and ‘published platform’. While the particular embodiments described herein may illustrate the invention in a particular domain, the broad principles behind these embodiments could be applied in other fields of endeavor. To facilitate a clear understanding of the present disclosure, illustrative examples are provided herein which describe certain aspects of the disclosure. However, it is to be appreciated that these illustrations are not meant to limit the scope of the disclosure, and are provided herein to illustrate certain concepts associated with the disclosure. 
     It is also to be understood that the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. Preferably, the present invention is implemented in software as a program tangibly embodied on a program storage device. The program may be uploaded to, and executed by, a machine comprising any suitable architecture. 
       FIG. 1  (PRIOR-ART) is a block diagram of a computing device  100  to which the present disclosure may be applied according to an embodiment of the present disclosure. The system includes at least one processor  102 , designed to process instructions, for example computer readable instructions (i.e., code) stored on a storage device  104 . By processing instructions, processing device  102  may perform the steps and functions disclosed herein. Storage device  104  may be any type of storage device, for example, but not limited to an optical storage device, a magnetic storage device, a solid state storage device and a non-transitory storage device. The storage device  104  may contain an application  104   a  which is a set of instructions (i.e. code). Alternatively, instructions may be stored in one or more remote storage devices, for example storage devices accessed over a network or the internet  106 . The computing device also includes an operating system and microinstruction code. The various processes and functions described herein may either be part of the microinstruction code or part of the program (or combination thereof) which is executed via the operating system. Computing device  100  additionally may have memory  108 , an input controller  110 , and an output controller  112  and communication controller  114 . A bus (not shown) may operatively couple components of computing device  100 , including processor  102 , memory  108 , storage device  104 , input controller  110  output controller  112 , and any other devices (e.g., network controllers, sound controllers, etc.). Output controller  110  may be operatively coupled (e.g., via a wired or wireless connection) to a display device (e.g., a monitor, television, mobile device screen, touch-display, etc.) in such a fashion that output controller  110  can transform the display on display device (e.g., in response to modules executed). Input controller  108  may be operatively coupled (e.g., via a wired or wireless connection) to input device (e.g., mouse, keyboard, touch-pad, scroll-ball, touch-display, etc.) in such a fashion that input can be received from a user. The communication controller  114  is coupled to a bus (not shown) and provides a two-way coupling through a network link to the internet  106  that is connected to a local network  116  and operated by an internet service provider (hereinafter referred to as ‘ISP’)  118  which provides data communication services to the internet. Members or subscribers of social media may be connected to the local network  116 . A network link typically provides data communication through one or more networks to other data devices. For example, network link may provide a connection through local network  116  to a host computer, to data equipment operated by an ISP  118 . A server  120  may transmit a requested code for an application through internet  106 , ISP  118 , local network  116  and communication controller  114 . Of course,  FIG. 1  illustrates computing device  100  with all components as separate devices for ease of identification only. Each of the components may be separate devices (e.g., a personal computer connected by wires to a monitor and mouse), may be integrated in a single device (e.g., a mobile device with a touch-display, such as a smartphone or a tablet), or any combination of devices (e.g., a computing device operatively coupled to a touch-screen display device, a plurality of computing devices attached to a single display device and input device, etc.). Computing device  100  may be one or more servers, for example a farm of networked servers, a clustered server environment, or a cloud network of computing devices. 
     The disclosure provides for systems and methods for identifying key influencers from the ‘published platforms’ for different domains and analyzes these events along with the ‘conversari platform’ to arrive at a more effective result of identifying influencers and their social media influence. 
     In various exemplary embodiments, an application  104   a  or service, typically operating on a host site (for example, a website) to facilitate social media platform management and influence scoring for a member at a platform. The platform may be a ‘published platform’ or a ‘conversari platform’. Each type of platform provides for a plurality of content channels of which a user may become a member. A member can provide by use of a data processor, a user interface to enable a member to specify one or channels of interest in the ‘published platform’ and the ‘conversari platform’. The user interface can further enable a member to provide authentication credentials for each platform channel of interest. The authentication credentials can be used to automatically collect platform channel data from each of the plurality of platforms of interest, the channel data including channels from ‘published platform’ and the ‘conversari platform’. As with any of the content sources, published platform can include a data storage device or a depository of content, with one or more servers to serve that content to users at the user platform via network. The identities and data associated with members who have subscribed to a particular channel and data associated with the particular content on that channel can be collected once the user of the user platform provides an authentication. 
     Content may be collected for each of the plurality of channels of interest from the ‘published platform’ and the ‘conversari platform’ using content access mechanisms, including, but not limited to, a push mechanism, a pull mechanism, a download mechanism and the like. Examples of ‘published platform’ include domains for example, but not limited to, science and technology, entertainment, sports, health and the like. Science and technology may include academics related channels, for example, IEEE, Google Scholar, Microsoft Academic Research and the like. Science and technology may also include industry related channels, for example, slideshare, techcrunch, techmeme and the like. Entertainment industry may include movies related channels with information pertaining to earnings, number and type of theatres, television rating points and the like. Entertainment industry may also include music related channels with information pertaining to salaries, rankings of albums sold and the like. Sports may include salaries of sports starts, rankings based on competition results. Health may include doctor ratings, blogs on veterinary doctor&#39;s advices. ‘Published platform’ may also include product launches, product evangelizations, conference presentations, content platforms, product recommendations and research papers and the like. Content, in addition to including social interactions, may also include alterations made by a member to their own social content. ‘Conversari platforms’ represent one or more social networks that provide a platform for social interactions between members of each of the ‘conversari platforms’. A social interaction between two members may take many forms in the social network. For example, a social interaction may be a comment left on a ‘conversari platform’ channel by another member. A social interaction may also include a link to another member&#39;s site. Social interactions may further be created by a member when he creates a list of other members and becomes a potential viewer to their website. 
       FIG. 2  illustrates a system  200  to which the embodiments of the present can be applied. A Published Platform Channel Analyzer (hereinafter may be referred to as ‘PPCA Analyzer’)  202  analyze content from social media channels, other than social networking channels. A Conversari Platform Channel Analyzer (hereinafter may be referred to as ‘CPCA Analyzer’)  220  gather and identify data from social networking channels. A Topic Aggregator  230  receives input in the form of data sets. The data set comprises of at least one selected topic for which the system needs to determine at least one influencer. Examples of topic include, but are not limited to, cloud computing, open source software, product launch and a company. A published depository  204  and a conversari depository  222  can be respectively pre-configured for various channels of ‘published platforms’ and ‘conversati platforms’. The published depository  204  and conversari depository  222  receive data sets in the form of selected topics or subject-matter of choice from the topic aggregator  230 . Data is captured from the pre-configured channels in the published depository  204  by a data querying module  206  and analyzed by a published data cluster  208 . Similarly, the conversari data cluster  226  analyzes the data captured by the data querying module  206  for pre-configured channels in the conversari depository  222 . The system may be configured to query the social media platforms, specifically, the published platform channels and the conversari platform channels about the selected topic by including one more keywords indicative of the topic or domain. More specifically, for each content type from a platform channel, meta-data or information describing the content is identified. For example, for a paper publication, the metadata may include information such as a publisher, authors, year of publication, number of references and the like. This data may be a measurement of the number of page views on the social content of the ‘conversari platforms’ and the ‘published platforms’. Data captured from the respective depositories is analyzed for various influence metrics. Analysis on one or more metrics may be based on the pre-assigned weightages to quantify the relative importance of an event or influencer. According to an embodiment of the present disclosure, metrics may include, but is not limited to, activity, authority and audience. Other metrics may include resonance, velocity and content types. The term, activity, as used herein, means the volume or quantity of information shared between members. The term, authority, as used herein, means the quantitative measure of trust on the content generated by a member. The term, audience, as used herein, means the size of members that actively listens and reacts to a members&#39; content. For example, the system may be pre-configured for published platforms such as SlideShare based on metrics such as activity, authority and audience. The activity in published platforms may relate to the number of files or documents uploaded on SlideShare. Authority may relate to the total number of views of documents uploaded by a member or the quantitative measure of trust. Audience metric may relate to the size of members that actively listens and reacts to members&#39; generated content in terms of number of followers, number of documents and number of times documents were added to members&#39; favorites. The activity in conversari platforms may relate to aggregated mentions across the respective conversari platform, for example, total number of posts, comments or links. Authority may relate to acknowledgement by a member who is an authority on the selected topic or domain, for example, number of members who reposted the content and uniqueness of content. Audience metric may relate to the size of the members who are responsive to the content generated by the prospective influencer, for example, number of followers, number of other members added to the list of the prospective influencer, and follower versus following ratio. Another metric that may be utilized by the social influence measurement module is incoming message data. This data may also include the influence measurement, if available, of the members sending the messages to the conversari platforms and published platforms. Thus if a member is receiving messages from more influential members within the platforms, the member may carry a degree of influence within the community. A member that adds new content to his website frequently will generally receive a higher value than a member that updates only periodically. According to an embodiment of this disclosure, the system may be configured to detect members  202  who attempt to generate in increased value by spamming or by generating some other form of content with no value. 
     While the scoring criteria may or may not be the same for the ‘conversari platform’ and the ‘published platform’, the weights for the scoring criteria may be assigned separately for each platform. The published depository  204  and the conversari depository  222  may be further pre-configured to assign weightages for various content types and/or channels. For example, associated content type in published platforms include but is not limited to paper publications, case studies, product launches, product evangelizations and roadshows (table 1). As used herein, content may be referred to as member-generated content. For the purposes of this disclosure, the term content, as used herein, may be used interchangeably with social content. Various types of contents may be available on various published platform channels, for example, SlideShare. The system can be pre-configured to assign weightages for determining the degree of influence and the related events. Social content may take the form of text, graphics, video, or any other electronic form. Social content may also be presented in the form of a blog, an online journal, an e-mail message.aphotograph.an illustration, a sound file, a video file, or some other combination of electronic content. 
     
       
         
               
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Published Platforms Content Type 
                 Pre-Configured Weights 
               
               
                   
                   
               
             
             
               
                   
                 Paper publication (for example, 
                 0.2 
               
               
                   
                 research paper) 
               
               
                   
                 Case study 
                 0.1 
               
               
                   
                 Product launch 
                 0.3 
               
               
                   
                 Product evangelization 
                 0.3 
               
               
                   
                 Road shows 
                 0.1 
               
               
                   
                   
               
             
          
         
       
     
     A published engagement scorer  210  and a conversari engagement scorer  228  can identify influencers in ‘published platforms’ and ‘conversari platforms’ respectively based on one or more metrics. The term, score, as used herein refers, to a numerical value. While a score is referred herein to a numerical value, it may also refer to any type of indicator signifying a degree of relevance of the content from a channel of a published platform and/or conversari platform. The degree of relevance may be the degree of similarity between a particular content based on pre-configured preferences of the user. Preferably, the scoring criterion is pre-configured by the user. Alternatively, the scoring module may be default configuration of the system or system defined. For example, the scoring criterion may include pre-defined weightages for different influence metrics and the published engagement scorer  210  and conversari engagement scorer  226  operate to score the influencers based on these pre-defined weightages of metrics and pre-defined weightages of content types in published depository  204  and conversari depository  222 . The weight generation operates to programmatically assign the weights to the scoring criteria for each of metrics. More specifically, for each member, the weight generation operates to assign the weights to the scoring criteria for that member based on an analysis of a group of content types associated with that member. The published engagement scorer  210  calculates an influence score in the ‘published platform’ by assigning weights to each of the identified metrics, for instance, activity, authority and audience. The published engagement scorer  210  determines the influencer in published platforms through an additive function that determines the combined value of associated events. Using equation I, conversari engagement scorer  226  may be calculated:
 
( W   a *Authority)+( W   ad *Audience)+( W   ac *Activity)  [Equation I]
 
     Similarly, the conversari engagement scorer  226  is calculated by assigning weights to each of the identified metrics, for instance, activity, authority and audience. Weights are assigned to various metrics and score may be calculated using equation II:
 
( W   a *Authority)+( W   ad *Audience)+( W   ac *Activity)  [Equation II]
 
     The final ranking may be done by a social influence measurement scorer  240 . Influence scores from ‘published platform’ and ‘conversari platforms’ may be combined to arrive at a score that describes the degree of influence using equation III:
 
( W   ed *Event Driven Influencer Score)+( W   cd *Content Driven Influencer Score)  [Equation III]
 
       FIG. 3  is a flow diagram showing steps typically performed to identify key influencers. The topic or domain for which influencers have to be identified is provided by a user  302 . In one example, influencers, influencers were identified for cloud computing domain. Pre-configured ‘published platform’ channels and ‘conversari platform’ channels are identified  304  through the published depository  204  and conversari depository  222  respectively. Data was captured from the pre-configured platform channels  306 . Using Equation I, the published engagement scorer  210  determined the published engagement score  310  and ranked the top ten influencers (table 2). 
     
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                   
                   
                   
                 Content-driven 
               
               
                 Ranking 
                 Company 
                 Name 
                 influencer score 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1 
                 Amazon WS 
                 Werner Vogels 
                 80 
               
               
                 2 
                 Enomaly 
                 Reuven Cohen 
                 78 
               
               
                 3 
                 GoGrid 
                 Michael Sheehan 
                 60 
               
               
                 4 
                 Amazon WS 
                 Jeff Ban 
                 54 
               
               
                 5 
                 Cloudscaling 
                 Randy Bias 
                 41 
               
               
                 6 
                 EngineYard 
                 Ezra Zygmuntowicz 
                 41 
               
               
                 7 
                 Cisco 
                 James Urquhart 
                 37 
               
               
                 8 
                 Google 
                 Patrick Chanezon 
                 33 
               
               
                 9 
                 RightScale 
                 Josh Fraser 
                 32 
               
               
                 10 
                 CohesiveFT 
                 Yan Pritzker 
                 32 
               
               
                   
               
             
          
         
       
     
     Similarly, the conversari data cluster  226  determined the conversari engagement score  310  and ranked the top ten influencers from the pre-configured channels in the conversari depository  222 . Using Equation II, the conversari engagement scorer  228  determined the top ten influencers (table 3). 
     
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                   
                 Event-driven 
               
               
                 Ranking 
                 Company 
                 Name 
                 influencer score 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1 
                 Google 
                 Chris Schalk 
                 42 
               
               
                 2 
                 Netflix 
                 Adrian Cockroft 
                 43 
               
               
                 3 
                 Google 
                 Patrick Chanezon 
                 31 
               
               
                 4 
                 Amazon WS 
                 Jinesh Varia 
                 24 
               
               
                 5 
                 Oracle 
                 Rex Wang 
                 20 
               
               
                 6 
                 Amazon WS 
                 Werner Vogels 
                 20 
               
               
                 7 
                 Amazon WS 
                 Simone Brunozzi 
                 18 
               
               
                 8 
                 EngineYard 
                 Ezra Zygmuntowicz 
                 16 
               
               
                 9 
                 OpenStack 
                 Bret Piatt 
                 13 
               
               
                 10 
                 Cloudscaling 
                 Randy Bias 
                 9 
               
               
                   
               
             
          
         
       
     
     Using Equation III, the social influencer measurement scorer  240  calculated the social influence score  312  and derived the top ten influencers in the social media, which included ‘conversari platform’ and ‘published platform’ (table 4). 
     
       
         
               
               
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                   
                   
                   
                 Published 
                 Social Influence 
                   
               
               
                   
                 Name of 
                 Conversari 
                 Platform 
                 Measurement 
               
               
                 Company 
                 Influencer 
                 Platform Score 
                 Score 
                 Score 
                 Ranking 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Amazon WS 
                 Matt Tavis 
                 9 
                 — 
                 — 
                   
               
               
                 GoGrid 
                 John Keagy 
                 5 
                 — 
                 — 
               
               
                 GoGrid 
                 Rob Larson 
                 4 
                 — 
                 — 
               
               
                 Amazon WS 
                 Werner Vogels 
                 20 
                 80 
                 44 
                 1 
               
               
                 Google 
                 Chris Schalk 
                 42 
                 30 
                 37 
                 2 
               
               
                 Enomaly 
                 Reuven Cohen 
                 4 
                 78 
                 33 
                 3 
               
               
                 Google 
                 Patrick Chanezon 
                 31 
                 33 
                 32 
                 4 
               
               
                 Netflix 
                 Adrian Cockroft 
                 43 
                 10 
                 30 
                 5 
               
               
                 GoGrid 
                 Michael Sheehan 
                 8 
                 60 
                 29 
                 6 
               
               
                 Amazon WS 
                 Jeff Barr 
                 8 
                 54 
                 27 
                 7 
               
               
                 EngineYard 
                 Ezra 
                 16 
                 41 
                 26 
                 8 
               
               
                   
                 Zygmuntowicz 
               
               
                 Amazon WS 
                 Jinesh Varia 
                 24 
                 22 
                 23 
                 9 
               
               
                 Oracle 
                 Rex Wang 
                 20 
                 27 
                 23 
                 10 
               
               
                 Cloudscaling 
                 Randy Bias 
                 9 
                 41 
                 22 
               
               
                 Amazon WS 
                 Simone Brunozzi 
                 18 
                 26 
                 21 
               
               
                 Cisco 
                 James Urquhart 
                 8 
                 37 
                 19 
               
               
                 OpenStack 
                 Bret Piatt 
                 13 
                 25 
                 18 
               
               
                 Netflix 
                 Kevin McEntee 
                 5 
                 30 
                 15 
               
               
                 Appistry 
                 Sam Charrington 
                 8 
                 25 
                 15 
               
               
                 CohesiveFT 
                 Yan Pritzker 
                 3 
                 32 
                 14 
               
               
                 Dell 
                 Barton George 
                 5 
                 29 
                 14 
               
               
                 RightScale 
                 Josh Fraser 
                 2 
                 32 
                 14 
               
               
                 EngineYard 
                 Wesley Beary 
                 3 
                 30 
                 14 
               
               
                 VMWare 
                 Dave McCrory 
                 3 
                 28 
                 13 
               
               
                 EngineYard 
                 Tom Mornini 
                 1 
                 31 
                 13 
               
               
                 Eucalyptus 
                 Marten Mickos 
                 1 
                 30 
                 13 
               
               
                 Joyent 
                 Scott Herson 
                 2 
                 28 
                 13 
               
               
                 Cloudera 
                 Jeff Hammerbacher 
                 5 
                 23 
                 12 
               
               
                 VMWare 
                 Derek Collison 
                 8 
                 17 
                 12 
               
               
                 CohesiveFT 
                 Ryan Koop 
                 3 
                 23 
                 11 
               
               
                 Rackspace 
                 Graham Weston 
                 1 
                 25 
                 11 
               
               
                 Citrix 
                 Michael Harries 
                 5 
                 18 
                 10 
               
               
                 Rackspace 
                 John Engates 
                 2 
                 21 
                 10 
               
               
                 GoGrid 
                 Paul Lancaster 
                 2 
                 22 
                 10 
               
               
                 Eucalyptus 
                 Rich Wolski 
                 3 
                 18 
                 9 
               
               
                 Rackspace 
                 Erik Carlin 
                 1 
                 18 
                 8 
               
               
                 OpenStack 
                 Stephen Spector 
                 2 
                 14 
                 7 
               
               
                 Novell 
                 Ben Goodmann 
                 4 
                 9 
                 6 
               
               
                 RightScale 
                 Michael Crandell 
                 2 
                 10 
                 5 
               
               
                 Rackspace 
                 Andy Schroepfer 
                 3 
                 3 
                 3 
               
               
                   
               
             
          
         
       
     
     According to an embodiment of the present disclosure, weightages may be tuned at various staged based on the estimated influence scores and the actual influence metrics. According to another embodiment of the present disclosure, influence score may be measured over periodic intervals. The estimated influence score and be compared with the actual influence score and based on this the weightages may be tuned at various stages based on the estimated influence scores and the actual influence metrics. 
     Having described and illustrated the principles of the disclosure with reference to described embodiments and accompanying drawings, it will be recognized by a person skilled in the art that the described embodiments may be modified in arrangement without departing from the principles described herein. Although the disclosure has been described in connection with specific preferred embodiments, it should be understood that the disclosure as claimed should not be unduly limited to such specific embodiments.