Patent Publication Number: US-2012036080-A1

Title: Method and system for tracking online social interactions

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 61/166,943 filed Apr. 6, 2009, which is hereby incorporated by reference herein. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to the field of online social interactions. 
     BACKGROUND OF THE INVENTION 
     The phrase Web 2.0 refers to the second generation of web-based communities, such as social networking sites, wikis, blogs, etc, which aim to facilitate interactions among users. 
     SUMMARY OF THE INVENTION 
     According to the invention, there is provided a method of ranking an online user account, comprising: ranking each of a group of posts owned by a user account as a function of quantity of online interactions with the post; and ranking the user account as a function of the rank of each post owned by the user account in the group. 
     According to the invention, there is also provided a system for ranking an online user account comprising: at least one post ranker for ranking each of a group of posts owned by a user account as a function of quantity of online interactions with the post; and at least one user account ranker for ranking the user account as a function of the rank of each post owned by the user account in the group. 
     According to the invention, there is further provided a method of concluding that two online user accounts correspond to a common profile, comprising: analyzing data relating to two online user accounts; increasing a probability that the two online user accounts correspond to a common profile as more of the data suggests that the two user accounts correspond to a common profile; and if a probability that the two online user accounts correspond to a common profile is above a predetermined threshold, concluding that the two online user accounts correspond to a common profile. 
     According to the invention, there is still further provided a system for concluding that two online user accounts correspond to a common profile, comprising: at least one counter for increasing a probability that two online user accounts correspond to a common profile as more data relating to two online user accounts suggests that the two user accounts correspond to a common profile; and at least one comparer for analyzing data relating to two online user accounts and for concluding that the two online user accounts correspond to a common profile, if a probability that the two online user accounts correspond to a common profile is above a predetermined threshold. 
     According to the invention, there is provided a method of noting online roles of user accounts, comprising: for at least one post from a group of posts: noting at least one role played by a user account which owns the post; and for at least one interaction from a collection of interactions with the post, noting at least one role played by associated interacting user account based on type of interaction. 
     According to the invention, there is also provided a system for noting online roles of user accounts, comprising: at least one counter for noting for at least one post from a group of posts at least one role played by a user account which owns the post; and for noting for at least one interaction from a collection of interactions with the post, at least one role played by associated interacting user account based on type of interaction. 
     According to the invention, there is further provided a method of calculating alternative cost of funding a traditional online campaign that would lead to equivalent exposure with equivalent partiality as posts, comprising: for each post relevant to an online campaign: determining a partiality score of the post to the campaign; and estimating cost of funding in a traditional campaign equivalent exposure at external source including post; and calculating the alternative cost by summing the partiality score multiplied by the estimated cost for each post. 
     According to the invention there is still further provided a system for calculating alternative cost of funding a traditional online campaign that would lead to equivalent exposure with equivalent partiality as posts, comprising: at least one partiality scorer for determining a partiality score for each post relevant to an online campaign; at least one traditional campaign cost estimator for estimating for each post a cost of funding in a traditional campaign equivalent exposure at external source which includes the post; and at least one alternative cost calculator for calculating an alternative cost by summing the partiality score multiplied by the estimated cost for each post. 
     According to the invention, there is provided a method of management of an online campaign, comprising: receiving a definition of a campaign; selecting a subset of posts based on the definition; and reporting at least one of the following: at least one user account, the reporting reflecting user account rank as a function of rank of each post owned by the user account in the subset; a user account who played a specific role as a post owner or interacting user account for at least one post in the subset; a profile corresponding with a probability above a predetermined threshold to at least one user account, each of which owns or interacted with at least one post in the subset; or an alternative cost of funding a traditional online campaign that would lead to equivalent exposure with equivalent partiality as the subset of posts. 
     According to the invention, there is also provided a system for management of an online campaign, comprising: an application for receiving a definition of a campaign; an index searcher for searching an index to select a subset of posts based on the definition; and a reporting module for reporting at least one of the following: at least one user account, the reporting reflecting user account rank as a function of rank of each post owned by the user account in the subset; a user account who played a specific role as a post owner or interacting user account for at least one post in the subset; a profile corresponding with a probability above a predetermined threshold to at least one user account, each of which owns or interacted with at least one post in the subset; or an alternative cost of funding a traditional online campaign that would lead to equivalent exposure with equivalent partiality as the subset of posts. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order to understand the invention and to see how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which: 
         FIG. 1  is a high level block diagram of a network including a system for tracking social interactions, according to some embodiments of the invention; 
         FIG. 2  is an example of an interactions graph, according to some embodiments of the invention; 
         FIG. 3  is a block diagram of a worker module, according to some embodiments of the invention; 
         FIG. 4  is a flowchart of a method of ranking user accounts, according to some embodiments of the invention; 
         FIG. 5  is a flowchart of a method of keeping track of roles played by user accounts with respect to a trend, according to some embodiments of the invention; 
         FIG. 6  illustrates graphs of the number of posts and interactions per day and graphs of the running average deltas of posts and interactions, according to some embodiments of the invention; 
         FIG. 7  is an example of a social graph, according to some embodiments of the invention; 
         FIG. 8  is a flowchart of a method for identifying corresponding user accounts, according to some embodiments of invention; 
         FIG. 9  is a screenshot of a page in Google Social Graph API, according to some embodiments of the invention; 
         FIG. 10  is a flowchart of a method of campaign management, according to some embodiments of the invention; 
         FIG. 11  is a screenshot illustrating some aspects of campaign definition, according to some embodiments of the invention; 
         FIG. 12  is a screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 13  is another screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 14  is another screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 15  is another screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 16  is another screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 17  is another screenshot illustrating some aspects of campaign reporting, according to some embodiments of the invention; 
         FIG. 18  is a graph illustrating the rank of one user account converging with each iteration, according to some embodiments of the invention; and 
         FIG. 19  is a flowchart of a method of calculating an alternative campaign cost, according to some embodiments of the invention. 
     
    
    
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the present invention. 
     As used herein, the phrase “for example,” “such as”, “for instance” and variants thereof describe non-limiting embodiments of the present invention. 
     Reference in the specification to “one embodiment”, “an embodiment”, “some embodiments”, “another embodiment”, “other embodiments”, “one instance”, “some instances”, “one case”, “some cases”, “other cases” or variants thereof means that a particular feature, structure or characteristic described in connection with the embodiment(s) is included in at least one embodiment of the invention. Thus the appearance of the phrase “one embodiment”, “an embodiment”, “some embodiments”, “another embodiment”, “other embodiments” one instance”, “some instances”, “one case”, “some cases”, “other cases” or variants thereof does not necessarily refer to the same embodiment(s). 
     It should be appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. 
     Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “ranking”, “weighting”, “normalizing”, “determining”, “sorting”, “filtering”, “analyzing”, “increasing”, “concluding”, “creating”, “noting”, “estimating”, “counting”, “calculating”, “receiving”, “selecting”, “defining”, “outputting”, “reporting”, “processing”, “tracking”, “collecting, “summing”, “multiplying”, “dividing”, or the like, refer to the action and/or processes of any combination of software, hardware and/or firmware. For example, these terms may refer in some cases to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical, such as electronic quantities, within the computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within the computing system&#39;s memories, registers or other such information storage, transmission or display devices. 
     Embodiments of the present invention may include apparatuses for performing the operations herein. Each of these apparatuses may be specially constructed for the desired purposes, or may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs) electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions, and capable of being coupled to a computer system bus. 
     The processes and displays presented herein are not necessarily inherently related to any particular computer or other apparatus. Various general purpose systems may in some cases be used with programs in accordance with the teachings herein, or it may in other cases prove convenient to construct a more specialized apparatus to perform the desired method. Possible structures for a variety of these systems will appear from the description below. In addition, embodiments of the present invention are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the inventions as described herein. 
     Embodiments of the current invention relate to systems and methods of tracking online social interactions. In various instances, tracking may include any of the following inter-alia: ranking user accounts, identifying corresponding user accounts, noting online roles of user accounts and/or calculating the alternative cost of funding a traditional online campaign equivalent in exposure and partiality. In some cases, tracking may be used as part of end to end management of a campaign (e.g. advertisement, marketing, public relations, political, etc.). 
     Referring now to the drawings,  FIG. 1  illustrates a network  100 , including a system  110  for tracking social interactions, according to some embodiments of the invention. System  110  includes any combination of software, hardware and/or firmware capable of performing the operations as defined and explained herein. In the illustrated embodiments, system  110  includes one or more crawlers  115  which collect data from external sources via the Internet  160 . For example, an application programming interface “API”  176  may be used to collect data from external sources which are social networks such as Flickr®, YouTube, Facebook, Twitter™, Google Buzz, Delicious, Digg™, etc. In another example, additionally or alternatively, external sources such as Blogs and/or forums can be “scraped” using for example hypertext markeup language “HTML” scraping  174 , and/or data can be gathered from blogs and/or forums using really simple syndication “RSS”  178 . In another example, additionally or alternatively, data can be collected from external sources which are third party data providers  172 , such as search engines (Google™, Collecta, etc), or such as data aggregators (Gnip, etc). Data can be additionally or alternatively collected from external sources using other processes. Optionally a proxy  165  may be used in gathering the data. System  110  also includes a job management module  125  which assigns tasks to the crawlers, so that the crawlers are directed in what to collect. For example, in some embodiments, job management module  125  may include a central job repository in a database which assigns tasks in parallel to crawlers  115 . In some cases each task may be defined by type, state, priority and frequency (e.g. one-time or recurrent). In some embodiments, there may be a plurality of servers running the crawlers in order to cope with the large scale of data to be collected. 
     The invention does not specify the number and type of job management module(s), the number and type of proxy(s), the number and type of crawler(s), the number and type of external source(s) or the number and type of mechanism(s) (e.g. API, RSS scraping, HTML scraping, third party data providers, etc.) used, and in various embodiments the number and type of each may vary and/or be omitted. 
     Depending on the embodiment, the extent of the data collected may vary. In the illustrated embodiments, the collected data is stored in content database  130 . For example content database  130  may in some embodiments include a relational database such as MySQL®, etc, and/or a non-relational database such as Vertica, etc. The invention does not limit the type of content data which is collected and the content data may include any of the following inter-alia: text, links, videos, photos, metadata, geolocation information, timing information, external source of data, device used to create data, or any other appropriate content. 
     In the illustrated embodiments, the collected data includes inter-alia relationship data. The relationship data is stored in content database  130 , for example by storing links. Relationship data may alternatively or additionally be created by system  110  (as will be explained further below) and stored in content database  130 . 
     It is appreciated that various external sources may use different model schemas, APIs etc and therefore it may be challenging to store relationship data. In some embodiments, the various data models are generalized to a superset, i.e. a generic schema able to accommodate the data. Examples of generic schema include inter-alia “activityStrea.ms”, etc. For instance, in some cases the generic schema generalizes the relationships to owner accounts, posts, interactions and counts of ownerless interactions such as views, ratings, etc. In some cases, generalizing to a superset resolves any difficulties in ranking non-uniform sets, for example in a ranking method such as method  400  described below. 
     Refer to  FIG. 2  which illustrates an example of an interactions graph according to some embodiments of the invention.  FIG. 2  will be used to illustrate various aspects of the present invention. It is noted that an interactions graph is presented here as a visual tool to aid in the understanding of the reader, but that system  110  does not necessarily construct nor store such a graph. It is also noted that the number of interactions, user accounts, and profiles included in the interactions graph of  FIG. 2  are purposely not many so as to not confuse the reader. 
     In the illustrated embodiments, a post (AKA item, object, content) such as post  202  is created or initiated by an online user account (AKA user, owner) such as user account  204 . Therefore the relationship between user account  204  and post  202  is a relationship of ownership (i.e. user account  204  owns post  202 ). The relationship is shown in  FIG. 2  as a one-direction edge  203 . When there are at least two corresponding user accounts, a profile (AKA entity) functions as the identity of the corresponding user accounts. For example, in  FIG. 2 , user account  204  definitely or with a given probability corresponds to profile (AKA entity)  206 , with the relationship shown as a two-direction edge  205 . In some cases, the relationship between a user account and a profile may not be definite and therefore the percentage probability of the user account being identical to the entity is associated with the edge, for example here as 70%. The probability assignment will be discussed in further detail below. In embodiments where probabilities are associated with edges, the probability of definite relationships (i.e. 100%) may be associated with the edge or may be inherent if no other probability is associated with the edge. In some embodiments, the edges between profiles and user accounts in an interactions graph may not show the probability. 
     In the illustrated embodiments, two users accounts  240  and  260  may have a relationship (e.g. friends, family connections, work relations, following you, commented on your item, etc). The edge between two connected user accounts may be one-directional or two-directional depending on the nature of the relationship. The relationship may be explicitly defined by one or both of the user accounts, or may be implicit due to interactions between the two user accounts. For example, if a user account interacted with a post owned by another user account, then the relationship is implicit even if neither user account has explicitly defined a relationship with the other user account. In  FIG. 2 , for example only user accounts which are explicitly related are connected by an edge. For example, user account  260  follows user account  240 , as illustrated by a one-direction edge  262 . However, in some embodiments user accounts with an implicit relationship may also be connected by an edge in an interactions graph. 
     In the illustrated embodiments, a first user account (AKA interacting user account) may have a relationship with a post owned by a second user account by interacting with the post owned by a second user account. Depending on the type of interaction, the continued existence of the interaction may be dependent on the existence of the post or independent of the existence of the post. Examples of interactions which are dependent include comments, likes, favorites, ratings, etc. In these examples, if the post is deleted the interactions will necessarily be deleted. This type of interaction is illustrated as a one-direction edge between the interacting user account and the post that is the focus of the interaction. For example in  FIG. 2 , user account  250  comments on post  212  which is owned by user account  210 . The comment is illustrated as a one-direction edge  252  in  FIG. 2 . Examples of interactions which are independent include: links, shares (e.g. retweets), etc. In these examples, if the post that is the focus of the interaction is deleted, the interactions will not necessarily be deleted. This type of interaction is shown as a one-direction edge between the post of the interacting user account and the post which is the focus of the interaction. For example user account  260  interacts with post  202  by adding a link to post  202  in post  264  which is owned by user account  260 . The link is shown as a one-direction edge  266  in  FIG. 2 . As another example, user account  270  interacts with post  242  by sharing post  242  through post  272  owned by user account  270 . The share is shown as a one-direction edge  274  in  FIG. 2 . There may also be interactions with a post whose interacting owner account is not known, for example because the information is not publicly available. For example, in  FIG. 2 , post  212  has been viewed  500  thousand times but since the viewers are not known these interactions are considered ownerless. (It is also possible that the interacting user account may not be known in some instances for comments, ratings, or other interactions and in these instances these types of interactions may therefore be considered ownerless.) 
     In the illustrated embodiments, a first user account may interact via an owned post directly with a second user account rather than with a post owned by the second user account. An example of this type of direct interaction is a mention. For example interacting user account  260  may post a post  268  which mentions user account  240 . The mention is shown as a one-direction edge  269  in  FIG. 2 . 
     Referring again to  FIG. 1 , in the illustrated embodiments, one or more workers  120 , for example under the direction of job management module  125 , process the collected data in accordance with tracking requirements. In some embodiments the tasks performed by workers  120  may be defined by type, state, priority and frequency (one time or recurrent). Depending on the embodiment, there may be tracking requirements specified by a particular consumer  190  in addition to or instead of tracking requirements which are universally applied regardless of who the consumer is. An example of working module  120  will be presented further below. In some embodiments, in order to cope with the scale of the tracking requirements, workers  120  run on a plurality of servers running in parallel. In some embodiments, workers interface with third party tools  182  via the Internet  160  in order to perform some or all of the processing. 
     In the illustrated embodiments, reporting module  145  prepares reports in accordance with reporting requirements. Depending on the embodiment, there may be reporting requirements specified by a particular consumer  190  in addition to or instead of reporting requirements which are universally applied regardless of who the consumer  190  is. Depending on the embodiment, report(s) for any particular consumer  190  may be prepared on demand and/or according to a schedule defined by particular consumer  190  and/or defined by system  110 . Depending on the embodiment, report(s) for any particular consumer may be provided to particular consumer  190  in any known manner, including inter-alia via email. For example, in one embodiment, a specific report may be generated according to a schedule defined by particular consumer  190  and delivered via email to that consumer. 
     In some embodiments, in order to cope with a large number of user accounts, content database  130  is clustered into shards. For example, in various embodiments with shards, the partitioning may follow any suitable scheme, including inter-alia directory based partitioning, vertical partitioning, range based partitioning, key/hash based partitioning, etc. For example, in some embodiments with directory based partitioning, each user account and/or profile is assigned a shard number upon creation. In these directory based portioning embodiments, all posts owned by the user account and all interactions originating with the user account are stored in the shard of the user account. In some of these embodiments with directory based partitioning, interactions involving user accounts (or posts thereof) that belong to different shards may be in some cases stored on both shards so as to be accessible when traversing in either direction (e.g. for a given user accounts can retrieve interactions or for a given interaction can retrieve participating user account(s)). In some embodiments, content database  130  is not clustered into shards. 
     In the illustrated embodiments, the collected data is indexed in an index  135 , for example a full text index such Lucene, Solr, etc. In some cases, the index includes all text but not relationship data. For example, in some of these cases the index may include the text associated with user accounts, interactions, profiles and posts, etc. but not the links among various user accounts, interactions, profiles and posts, etc. In some cases, index  135  runs on multiple servers in parallel. 
     In some embodiments, index  135  may be searched for post(s) by worker  120  (for example by index searcher  340 —see  FIG. 3 ). Depending on the embodiment, the search may occur for various purposes. For example, in some embodiments, the search may occur as posts are collected. Continuing with the example, assume that posts are clustered together in clusters. For every newly collected post, index  135  is searched for other similar posts (e.g. “MoreLikeThis”) and index  135  returns a list of other posts including the same frequent terms and a similarity score. If the similarity score is above a predetermined threshold then the new post is linked to the first post (and/or any other posts) in the cluster, thereby building up the cluster. In some cases with clusters, the clustering algorithm is trained using documents belonging to specific categories (and/or market verticals). Therefore, in these cases each cluster can be categorized as belonging to one or more categories and/or market verticals. In another example, additionally or alternatively in some embodiments the search occurs in order to find a subset of posts which fulfill predetermined criteria such as for instance relevance to a defined campaign. Depending on the embodiment, the subset of posts may belong to one or more clusters, categories, and/or market verticals. For instance, in one embodiment of the example, one of the predetermined criteria may specify that the subset of posts belongs to a particular category or categories (or cluster(s), market vertical(s)) whereas in another embodiment of the example the subset of posts are not restricted to any particular category or categories (or cluster(s), market vertical(s)). 
     In the illustrated embodiments, batch  140  is an additional database used as temporary storage for batch operations. For example, in some cases batch  140  may contain partial information copied from the content database  130  and when batch operations are finished, data may be copied back to content database  130 . In some embodiments, batch  140  may be omitted. 
     In the illustrated embodiments, system  110  also includes one or more applications  150  which allow a consumer  190  to provide and/or receive information regarding social interactions via Internet  160 . For example an application may run on a web server and include accessing of stored information. The accessing of the application by consumer  190  is not limited by the invention, and the access may be for example, via any of the following: designated web site, pages on existing websites, desktop and webgadgets and widgets, RSS and email notifications, short message service SMS and extensible messaging and presence protocol XMPP agents (e.g. Twitter), third party integrations, etc. In some embodiments there may be a plurality of slave database servers with replicated data from content database  130  and/or index  135 , so as to be able to service a large number of concurrent consumers  190  with reasonable response time. In some cases load balancing of the applications is provided, for example via load balancer  155 . The functionality of the application may vary depending on the embodiment. In some embodiments, for example, the application allows end to end campaign management as will be described in more detail below. 
     It is noted that the invention does not limit the type of consumer  190 . The term consumer is used in the broad sense of any person, organization, object, etc. which interacts with application  150  directly or indirectly. Therefore in various embodiments, consumer(s)  190  which interact with application  150  may be human and/or non-human. For example, in some embodiments an API may allow interaction with the application without human intervention. 
     The invention does not specify the number and type of worker(s), the number and type of content database(s), the number and type of index(es), the number and type of batch(es), the number and type of reporting module(s), the number and type of load balancer(s), or the number and type of application(s) used, and in various embodiments the number and type of each may vary and/or be omitted. 
     In some embodiments, system  110  may comprise fewer, more and/or different modules than illustrated in  FIG. 1 . In some embodiments, the functionality of system  110  described herein may be divided differently among the modules shown in  FIG. 1 . In some embodiments, the functionality of system  110  described herein may be divided into fewer, more and/or different modules than shown in  FIG. 1 . In some embodiments, system  110  may include additional or less functionality than described herein. In some embodiments, system  110  may be divided into two or more systems, which may possibly be dispersed geographically. For simplicity of description, unless explicitly stated otherwise, the single form of system  110  is used below to include both embodiments with a single system and embodiments with a plurality of systems. Similarly, unless explicitly stated otherwise, the single form of any of the modules shown in  FIG. 1  is used below to include both embodiments with one of that module (for example one worker  120 ) and embodiments with a plurality of those modules (for example a plurality of workers  120 ). For embodiments with a plurality of systems or a plurality of modules, when reference is made in the discussion below to the system or to a module in the single form (for example worker  120 ), the reference should be understood to encompass both embodiments where the reference is to one of the systems or to one of the modules and embodiments where the reference is to a plurality of the systems or modules. 
     It is appreciated that consumers  190  may in some cases not be equivalently interested in all posts, user accounts and/or profiles. It is appreciated that consumers  190  may in some cases be interested in information relating to all user accounts corresponding to a profile, in addition to or instead of separate information relating to each user account. It is appreciated that consumers in some cases may be interested in role(s) played by a user account and/or profile. It is appreciated that consumers may in some cases be interested in quantifying the monetary value of online exposure. For any of these reasons and/or for any other reason, it is appreciated that consumers  190  may in some cases be interested in processing of the collected data in order to extract analysis in accordance with tracking requirements. 
       FIG. 3  is a block diagram of a worker module  120 , according to some embodiments of the present invention. Worker module  120  may be comprised of any combination of software, hardware and/or firmware capable of performing the operations as defined and explained herein, including inter-alia processing of the collected data. In the illustrated embodiments, worker module  120  (aka “worker”) includes one or more counters  302  for keeping track of amounts, one or more comparers  320  for comparing, one or more sentiment analyzers  330  for analyzing sentiments, one or more index searchers  340  for performing searches on index  135 , one or more partiality scorers  350  for determining the partiality of a post, one or more alternative cost calculators for calculating the alternative cost of a funding a traditional campaign, one or more post rankers  360  for ranking posts, one or more user account rankers  370  for ranking accounts, one or more profile processors  375  for processing profiles, one or more trend determiners  380  for determining trends, and one or more traditional campaign cost estimators  390  for estimating traditional campaign cost. In embodiments with more than one worker  120 , each worker module  120  may be the same or different than the other worker modules. For example, in one case, one worker  120  may include all the counters  302 , another worker all the comparers  320  etc, whereas in another case each worker  120  may include the same number of counters  302 , comparers  320 , etc. In embodiments with more than one worker, workers may possibly be dispersed geographically. 
     In some embodiments, worker  120  may comprise fewer, more and/or different modules than illustrated in  FIG. 3 . In some embodiments, the functionality of worker  120  described herein may be divided differently among the modules shown in  FIG. 3 . In some embodiments, the functionality of worker  120  described herein may be divided into fewer, more and/or different modules than shown in  FIG. 3 . In some embodiments, worker  120  may include additional or less functionality than described herein. For simplicity of description, unless explicitly stated otherwise, the single form of any module in worker  120  (for example counter  302 ) is used below to include both embodiments with a single module and embodiments with a plurality of modules. For embodiments with a plurality of modules (for example a plurality of counters  302 ), when reference is made in the discussion below to the module in the single form (for example counter  302 ), the reference should be understood to encompass both embodiments where the reference is to one of the modules (for example one of counters  302 ) and embodiments where the reference is to a plurality of the modules (for example a plurality of counters  302 ). 
     It is assumed that one of the tracking requirements is to rank user accounts. It is appreciated that due to the large number of user accounts, from various external sources, it may be advantageous in some cases to rank user accounts. In some embodiments, system  110  is not affiliated with any particular external source or with any user account and therefore the ranking may be considered unbiased. In some embodiments, the ranking algorithm is publicly available and therefore transparent. 
     In the illustrated embodiments the ranking of a user account includes two phases. In the first phase, ranks of posts are determined by calculating for each a function of the quantity of online interactions with the respective post. In various embodiments, calculation of the function for a given post may include any of the following inter-alia: weighting by type of interaction, weighting by the rank of the interacting user account (if any), dividing the quantity of interactions with a given post by the total quantity of interactions with posts, weighting based on whether or not the interaction is owned or ownerless, and/or taking into account the age of the given post. 
     In the second phase, ranks of user accounts are determined by calculating for each a function of the ranks of posts that are owned by the respective user account. In some embodiments, the rank of a user account is also a function of the number of direct interactions with the user account. In various cases, therefore, calculation of the function for a given user account may include any of the following inter-alia: summation of the ranks of owned posts, weighting by type of direct interaction, weighting by the rank of the directly interacting user account, dividing the quantity of direct interactions with the given user account by the total quantity of direct interactions that an interacting user had with all user accounts; weighting based on whether the contribution to the given user account rank is from posts or from direct interactions, and/or normalizing the rank of the given user account by a function of the quantity of user accounts and maximum user account rank. 
     In an optional third phase, ranks of profiles may be determined by calculating for each as a function of the ranks of all user accounts whose probability of corresponding to the respective profile is at least a certain percentage (where the percentage may be 100% or less depending on the embodiment). 
     In various embodiments, the group of posts which are ranked and/or the group of posts which are considered when determining the ranking of user accounts, may include all available posts, or may include only a subset of posts, for example those fulfilling predetermined criteria. In various embodiments, all direct interactions between user accounts may be taken into account when ranking user accounts, or only direct interactions between owners of posts fulfilling predetermined criteria may be taken into account. In various embodiments, the user accounts which are ranked may include all available user accounts or only a subset of user accounts, for example those fulfilling predetermined criteria. For example in one embodiment, only posts belonging to a user account of interest may be ranked, in order to be able to subsequently rank the user account, or only a subset of posts belonging to a user account of interest which fulfill predetermined criteria may be ranked in order to be able to subsequently rank the user account. 
     One example of a method of ranking user accounts will now be described with reference to  FIG. 4 . This example is provided to aid in the understanding of the reader and should not be construed as restricting the invention. To further illustrate stage  436  to the reader, some numerical calculations of equation 2 are now provided. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. 
       FIG. 4  is a flowchart of a method  400  of ranking user accounts, according to some embodiments of the present invention. In some cases, method  400  may include fewer, more and/or different stages than illustrated in  FIG. 4 , the stages may be executed in a different order than shown in  FIG. 4 , and/or stages that are illustrated as being executed sequentially may be executed in parallel. 
     In the illustrated embodiments in stage  404  the rank of each user account (AR owner ) is initialized to the previous rank thereof (from previous iteration) or to a predetermined initial value if no rank exists. For example, referring to  FIG. 2  and assuming that this is the first time method  400  is performed, and that all user accounts in  FIG. 2  are being ranked, user accounts  204 ,  210 ,  220 ,  240 ,  250 ,  256 ,  260  and  270  are initialized to a predetermined initial value, say 1. Stage  404  can be performed for instance by user account ranker  370 . 
     In the illustrated embodiments in stage  408 , the total number of (owned) interactions by type executed by each user account n type     owner    is counted. Stage  408  may be performed for instance by counter  302 . For example, in the interactions graph of  FIG. 2  and assuming all posts are part of the group, user accounts  250 ,  260  and  270  each performed one interaction on posts, each of a different type, user account  260  performs one direct interaction on another user account, and the remaining user accounts performed no interactions on posts nor direct interactions. 
     In another example, assume that only the subset of posts that reflect “making history” (relating to the election of Barak Obama) such as post  242  fulfill predetermined criteria and are therefore part of the group, that the video in post  202  relates to “making history” and therefore fulfills predetermined criteria and is part of the subset but the photo in post  212  is not associated with “making history” and therefore does not fulfill predetermined criteria and is not part of the subset. In this case, user account  270  performed one interaction on posts in the subset of type “share”, user account  260  performed one interaction on posts in the subset of type “link”, and the remaining user accounts performed no interactions on posts in the subset. 
     In the illustrated embodiments in stage  412  the total number of ownerless interactions on posts by type n type     ownerless    is counted. Stage  412  may be performed, for instance by counter  302 . For example, assuming all posts in  FIG. 2  are part of the group, post  202  had 1 million views and post  212  had 500 thousand views so the total number of ownerless views on posts was 1.5 million. It is assumed that the user accounts who rated post  202  are unknown and therefore the ratings are ownerless. For example, in some cases the ratings may follow the 5 star rating system employed for example by YouTube or the Thumbs-up/Thumbs-down rating procedure which track the total number of rating votes, the sum of all votes, and the average rating. Here it is assumed that there is an average rating of 5, and that the number of ownerless ratings is ten thousand (i.e. ten thousand votes were cast). 
     In another example, it is again assumed that only ownerless interactions are considered which are on posts in the subset relating to “making history” (only this subset being in the group). It is again assumed that post  202  relates to making history and is therefore part of the subset but post  212  does not. In this example total number of ownerless views would reflect only those on post  202 , e.g. 1 million. 
     In the illustrated embodiments in stage  416  the number of interactions on a given post by type executed by each user account n type     owner    is counted. For instance, stage  416  may be performed by counter  302 . For example, referring to  FIG. 2 , post  242  has one interaction of type share by user account  270 , post  202  has one interaction of type link by user account  260 , and post  212  has one interact account  250 . There were no interactions on posts  268  and  270 . Among the posts with at least one interaction if it is assumed that all posts  242 ,  202 , and  212  are in the group then the number of interactions is counted for each of posts  242 ,  202 , and  212 . 
     In another example, however only posts  242  and  202  are among the subset of posts with at least one interaction that are assumed to fulfill predetermined criteria (relating to “making history”) and therefore the number of owned interactions for post  212  may not be counted or the number may be counted and ignored. 
     In the illustrated embodiments in stage  420 , the number of ownerless interaction on a given post by type 
     
       
         
           
             n 
             
               obj 
               
                 type 
                 ownerless 
               
             
           
         
       
     
     is counted. For instance stage  420  may be performed by counter  302 . For example referring to  FIG. 2 , post  202  has 1 million views and post  212  has 500 thousand views. Post  202  also has ten thousand ownerless ratings. No other posts were the focus of ownerless interactions. As another example, if post  212  is assumed to not fulfill predetermined criteria (relating to “making history”) then the number of ownerless interactions for post  212  may not be counted or the number may be counted and ignored. 
     In the illustrated embodiments in stage  424  the number of direct interactions by type executed by each user account on another user account 
     
       
         
           
             n 
             
               owner 
               
                 type 
                 owner 
               
               ′ 
             
           
         
       
     
     is counted (in order to differentiate in the description of owner method  400  the interacting user account is written without a prime and the user account which is the focus of the interaction is written with a prime). For instance stage  420  may be performed by counter  302 . For example, in  FIG. 2 , interacting user account  260  provides one mention of user account  240 . In another example where only a subset of posts are evaluated, then in some cases only interactions between owners of posts in the subset are evaluated. In  FIG. 2  it is assumed that posts  242  and  264  are in the subset since both relate to “making history” and therefore the direct interaction by user account  260  on user account  240  is counted. 
     In the illustrated embodiments in stage  428 , the ranks for posts are calculated, for instance, by post ranker  360 . In some embodiments post ranker  360  uses the map function in the Hadoop map/reduce framework in of these embodiments, several map/reduce job flows are executed one after another, where each map phase uses the previous reduce phase as input thereof. 
     In one example, referring to  FIG. 2  if all posts are in the group then the ranks for all posts are calculated. In another example if only posts in the subset relating to “making history” are considered, then post  212  and  268  which are assumed to not relate to making history are not ranked. 
     A sample equation for calculating the rank of each post AR obj  is provided by equation 1: 
     
       
         
           
             
               
                 
                   
                     AR 
                     obj 
                   
                   = 
                   
                     
                       ( 
                       
                         
                           
                             ∑ 
                             owner 
                           
                            
                           
                             
                               ∑ 
                               type 
                             
                              
                             
                               
                                 
                                   m 
                                   type 
                                 
                                 · 
                                 
                                   n 
                                   
                                     obj 
                                     
                                       type 
                                       owner 
                                     
                                   
                                 
                                 · 
                                 
                                   AR 
                                   owner 
                                 
                               
                               
                                 n 
                                 
                                   type 
                                   owner 
                                 
                               
                             
                           
                         
                         + 
                         
                           k 
                            
                           
                             
                               ∑ 
                               type 
                             
                              
                             
                               
                                 
                                   m 
                                   type 
                                 
                                 · 
                                 
                                   n 
                                   
                                     obj 
                                     
                                       type 
                                       ownerless 
                                     
                                   
                                 
                               
                               
                                 n 
                                 
                                   type 
                                   ownerless 
                                 
                               
                             
                           
                         
                       
                       ) 
                     
                     · 
                     
                        
                       
                         - 
                         
                           
                             λ 
                              
                             age 
                           
                           obj 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     eqn 
                      
                     
                         
                     
                      
                     1 
                   
                   ) 
                 
               
             
           
         
       
     
     where k is a constant used to adjust the weight between interactions with a known origin and ownerless interactions; m type  is a constant used to adjust the weight of different types of interactions, age is the number of days between the date the post was posted and the current date, and λ is a decay-exponent constant, for example close to zero. 
     To further illustrate stage  428  to the reader, some numerical calculations of equation 1 are now provided. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. 
     For example, assume m share  is 4, m comment  is 3, m link  is 2, m view  is 1, and m rating  is the average rating divided by a predetermined rating of 2.5. Further assume that k is 1, λ, is 0.01 and that posts  242  and  272  are 50 days old, post  212  is 30 days old, post  264  is 5 days old and posts  202  and  264  are 40 days old. It is further assumed that the previous ranks of user accounts  204  and  210  were 12, the previous rank of user account  240  was 15, the previous rank of user account  220  was 1, the previous rank of user account  260  was 5, the previous rank of user account  270  was 3, the previous rank of user account  250  was 6, and the previous rank of user account  256  was 1. 
     Beginning with post  242 , post  242  is the focus of one interaction by user account  270  (rank 3) with m share  of 4 out of a total of one share interaction executed by user account  270  on any posts. There are no ownerless in age of the post is 50 days. Therefore the rank of the post is 
     
       
         
           
             
               
                 ( 
                 
                   
                     
                       4 
                       × 
                       1 
                       × 
                       3 
                     
                     1 
                   
                   + 
                   0 
                 
                 ) 
               
               · 
               
                  
                 
                   
                     - 
                     0.01 
                   
                   × 
                   50 
                 
               
             
             = 
             7.3 
           
         
       
     
     Post  272  is the focus of no interactions and therefore has rank of 0. Post  268  is the focus of no interactions and therefore has rank of 0. Post  264  is the focus of no interactions and therefore has rank of 0. 
     Post  202  (age 40 days) is the focus of one interaction by user account  260  (rank of 5) with m link  of 2, out of a total of one link interactions performed by user account  260  on any posts. Post  202  also is the focus of 1 million ownerless views with m view . of 1, and the focus of ten thousand ratings with m rating  of 5/2.5=2. The total number of ownerless views on all posts is 1.5 million (total of views of post  202  and  212 ). The total number of ownerless ratings is ten thousand. k is assumed to equal 1. Therefore the rank of the post is 
     
       
         
           
             
               
                 ( 
                 
                   
                     
                       2 
                       × 
                       1 
                       × 
                       5 
                     
                     1 
                   
                   + 
                   
                     1 
                     × 
                     
                       ( 
                       
                         
                           
                             1 
                             × 
                             1 
                              
                             
                                 
                             
                              
                             million 
                           
                           
                             1.5 
                              
                             
                                 
                             
                              
                             million 
                           
                         
                         + 
                         
                           
                             2 
                             × 
                             10 
                              
                             
                                 
                             
                              
                             thousand 
                           
                           
                             10 
                              
                             
                                 
                             
                              
                             thousand 
                           
                         
                       
                       ) 
                     
                   
                 
                 ) 
               
               · 
               
                  
                 
                   
                     - 
                     0.01 
                   
                   × 
                   40 
                 
               
             
             = 
             8.5 
           
         
       
     
     Post  212  (age 30 days) is the focus of one interaction by user account  250  (rank of 6) with m comment  of 3 out of a total of one comment by user account  250  on any posts. Post  212  is the focus also of 500 thousand ownerless views out of a total number of ownerless views of 1.5 million with of 1. k is assumed to be 1. Therefore the rank of the post is 
     
       
         
           
             
               
                 
                   3 
                   × 
                   1 
                   × 
                   6 
                 
                 1 
               
               + 
               
                 1 
                 × 
                 
                   
                     
                       1 
                       × 
                       500 
                        
                       
                           
                       
                        
                       thousand 
                     
                     
                       1.5 
                        
                       
                           
                       
                        
                       million 
                     
                   
                   · 
                   
                      
                     
                       
                         - 
                         0.01 
                       
                       × 
                       30 
                     
                   
                 
               
             
             = 
             13.6 
           
         
       
     
     If only the subset of posts which relate to “making history” are considered (e.g. posts  264 ,  272 ,  242 , and  202 ), then the ranks of posts  264 , post  272  and post  242  remain unchanged, but the rank of  202  would change because the total number of ownerless views on posts relating to making history equals 1 million (views on  202 ) rather than 1.5 million (views on  202  and  212 ). The rank of  202  would be increased to 8.7. 
     It should be evident that equation 1 is only one example of an equation which may be used to rank posts and that other equations which rank posts may be used in some embodiments. 
     In the illustrated embodiments in stage  432 , ranks for user accounts are calculated. Stage  432  may be executed for instance by user account ranker  370 . In some embodiments user account ranker  370  uses the reduce function in the Hadoop map/reduce framework in performing stage  432 . In some of these embodiments, several map/reduce job flows are executed one after the other, where each map phase uses the previous reduce phase as input thereof. 
     A sample calculation of user account ranks is given by equation 2: 
     
       
         
           
             
               
                 
                   
                     AR 
                     
                       owner 
                       ′ 
                     
                     ′ 
                   
                   = 
                   
                     
                       l 
                        
                       
                         
                           ∑ 
                           owner 
                         
                          
                         
                           
                             ∑ 
                             type 
                           
                            
                           
                             
                               
                                 m 
                                 type 
                               
                               · 
                               
                                 n 
                                 
                                   owner 
                                   
                                     type 
                                     owner 
                                   
                                   ′ 
                                 
                               
                               · 
                               
                                 AR 
                                 owner 
                               
                             
                             
                               n 
                               
                                 type 
                                 owner 
                               
                             
                           
                         
                       
                     
                     + 
                     
                       
                         ∑ 
                         object 
                       
                        
                       
                         AR 
                         
                           obj 
                           
                             owner 
                             ′ 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     equation 
                      
                     
                         
                     
                      
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
     where l is a constant used to adjust the weight between direct interactions and interactions with posts. 
     To further illustrate stage  432  to the reader, some numerical calculations of equation 2 are now provided. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. It is assumed in the numerical examples below that l=1 and m mention  is 3. 
     User account  240  was the focus of one direct interaction mention from user account  260  (rank of 5) with m mention  of 3 out of a total of one mention performed by user account  260 . User account  240  owns one post  242  with rank of 7.3. Therefore the rank of user account  240  is 
     
       
         
           
             
               
                 1 
                 · 
                 
                   
                     3 
                     · 
                     1 
                     · 
                     5 
                   
                   1 
                 
               
               + 
               7.3 
             
             = 
             22.3 
           
         
       
     
     User account  220  owns no posts and was not the focus of any direct interactions and therefore the rank is 0. User account  210  owns one post  212  with rank of 13.6 (but was not the focus of any direct interactions) and therefore the rank of user account  210  is 13.6. User account  204  owns one post  202  with rank of 8.5 (but was not the focus of any direct interactions) and therefore the rank of user account  204  is 8.5. User account  256  owns no posts and was not the focus of any direct interactions and therefore the rank of user account  256  is 0. User account  250  owns no posts and was not the focus of any direct interactions and therefore the rank is 0. User account  270  owns post  272  which has 0 rank (and was not the focus of any direct interactions) and therefore user account  270  also has rank 0. User account  260  owns two posts  268  and  264  which both have tank of 0 (and was not the focus of any direct interactions) and therefore user account  260  has rank of 0. 
     If only posts relating to “making history” and direct interactions between owners of posts relating to “making history are taken into account” then all user account ranks remain unchanged except for the ranks of user accounts  210  and  204 . Post  212  does not relate to making history and therefore is ignored. The rank of user account  210  which owns one post  212  and was not the focus of any direct interactions would therefore be 0. The rank of  202  would be increased to 8.7 and therefore the rank of user account  204  which owns post  202  would also be increased to 8.7. 
     It should be evident that equation 2 is just one example of an equation for ranking user accounts and that other equations for ranking user accounts may be used instead in some embodiments. 
     In the illustrated embodiments in stage  436 , the ranks of the user accounts may be normalized. Stage  436  may be performed for example by user account ranker  370 . For example one normalization function is presented here as equation 3. 
     
       
         
           
             
               
                 
                   
                     
                       AR 
                       
                         owner 
                         ′ 
                       
                     
                      
                     
                       ( 
                       normalized 
                       ) 
                     
                   
                   = 
                   
                     
                       
                         AR 
                         
                           owner 
                           ′ 
                         
                         ′ 
                       
                       · 
                       
                         n 
                         
                           owners 
                           ′ 
                         
                       
                     
                     
                       max 
                        
                       
                         ( 
                         
                           AR 
                           
                             owner 
                             ′ 
                           
                           ′ 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     equation 
                      
                     
                         
                     
                      
                     3 
                   
                   ) 
                 
               
             
           
         
       
     
     where n owners&#39;  is the number of user accounts and max(AR′ owner′ ) is the maximum user account rank from stage  432 . 
     To further illustrate stage  436  to the reader, some numerical calculations of equation 3 are now provided. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. 
     For example, in  FIG. 2  there are eight user accounts, and from stage  432  the maximum rank was 22.3. The normalized ranks would therefore be zero for user accounts  220 ,  250 ,  256 ,  260  and  270 . For user account  240  the normalized rank would equal 22.3*8/22.3=8. For user account  210  the normalized rank would equal 13.6*8/22.3=4.9. For user account  204 , the normalized rank would equal 8.5*8/22.3=3.0. If the ranks of user account  210  and  204  are instead the ranks relating to “making history” then the normalized rank of  210  would be 0 and the normalized rank of  204  would be 8.7*8/22.3=3.1. 
     It should be evident that equation 3 is just one example of an equation for normalization and that other equations for normalization may be used instead in some embodiments. In other embodiments normalization stage  436  may be omitted. 
     In the illustrated embodiments in stage  440  profiles are ranked as a function of those user accounts definitely corresponding (e.g. with 100% probability) with the profiles or whose probability of corresponding with the profiles is above a predetermined percentage which may in some cases be less than 100%. Stage  440  may be executed for instance by profile processor  375 . For example, the rank of a profile may equal the sum of the un-normalized or normalized ranks of all corresponding user accounts. 
     For example, assume that profile  206  corresponds to user accounts  240 ,  220 ,  210 , and  204 , and that profile  254  corresponds to user accounts  256  and  250 . Assuming that the rank of a profile equals the sum of normalized ranks of corresponding user accounts, the rank of profile  206  would equal the sum of the normalized ranks of user accounts  240 ,  220 ,  210 , and  204  and the rank of profile  254  would equal the sum of the ranks of user accounts  256  and  250 . 
     In some embodiments, other functions may be used for calculating the profile rank. In some embodiments, stage  440  may be omitted. 
     In the illustrated embodiments in stage  444 , the new user account ranks (normalized and/or un-normalized) are saved and optionally the new ranks of the profiles are stored. In some embodiments, stage  444  may be omitted. Method  400  then iterates beginning with stage  404 . In some embodiments, each new iteration brings user account ranks closer to convergence. Refer for example to  FIG. 18  which shows the rank of one user account converging with each iteration, according to some embodiments of the invention. It should be noted that the convergence illustrated in  FIG. 18  is not necessarily representative of convergence in all embodiments. 
     In various embodiments, none, any, or all of the following may be applied to user accounts and/or profiles after ranking: filtering, selecting, and/or sorting. 
     In some embodiments where all posts and user accounts were ranked (and not only those relating to a subset), a filtering process may in some cases then be performed to only choose those user accounts which fulfill predetermined criteria. Alternatively, in some embodiments where only a subset of posts and/or user accounts were ranked, a further filtering process may in some cases then be performed to only choose those user accounts which fulfill further predetermined criteria. For example, assume all user accounts in  FIG. 2  were ranked. However, only a subset of user accounts, those relating to “making history”, are of interest to a given consumer  190 . Depending on the embodiment, the subset may include those user accounts which own a minimum number of posts relating to making history. Alternatively or additionally the subset may include those user accounts which interacted a minimum number of times with posts and/or other user accounts relating to “making history”. For instance, assume the subset includes only those user accounts owning at least one post relating to making history, namely accounts  240 ,  270 ,  260 , and  204  in this example. Therefore the ranks of all other user accounts may in some cases be ignored and filtered out. 
     In some embodiments, the ranks may be presented to consumers  190 , and/or may be used internally by system  110 . In some embodiments, the presentation of the user accounts and/or profiles to the consumer reflects the rank in any one of various ways including inter-alia: explicit presentation of the rank, selection of user accounts/profiles to be presented based on rank, and/or sorting the user accounts/profiles to be presented based on the rank. For example, the user account ranks and/or profile ranks may in some cases be used to determine the order of display with higher ranked user accounts/profiles outputted in a more prominent position than lower ranked user accounts/profiles. In another example, additionally or alternatively the user accounts/profiles ranks may in some cases be used as the sole or one of the factors to determine whether or not those user accounts/profiles are selected to be provided to consumers  190 . Continuing with the example, in some of these cases only user accounts/profiles with rank above a predetermined threshold, and/or the in the top predefined percentile of ranks are selected to be provided. In embodiments where the ranking is used internally, the ranking may serve as a primary selection and/or sorting factor, a secondary selection and/or sorting factor, or an even less important selection/sorting factor. See also below description with reference to  FIG. 15 . 
     In some embodiments, various user accounts may provide one or more functions (i.e. play one or more roles) in the online environment. For example roles may include inter-alia:
         i. Contributor—posts items that become interesting/popular   ii. Spotter—early discoverer of items that become interesting/popular   iii. Connector—people ask Connectors questions and they refer them to Mavens. Connectors are people who, early on, share the information among their peers   iv. Maven—people being referred to by Connectors for help or information   v. Salesman—posts links to interesting/upcoming items. Links and discusses upcoming topics and spreads them to the mainstream   vi. Cataloger—adds meaningful tags and information to existing items   vii. Commenter—discusses interesting/upcoming items and provokes further discussion. Engages in conversations, usually experts in the field   viii. Filter—marks interesting/upcoming items as favorites and acts as a filter for other people   ix. Spammer—repeatedly posts the same information on multiple items   x. Attracts views—posts items that attract views   xi. Engaging—posts items that attract comments       

     In some embodiments, it may be useful to determine which role or roles various user accounts play. In some of these embodiments, it is determined which role or roles various user accounts play over time and therefore as posts and interactions are collected, the role or roles played by various user accounts via posts and/or interactions is/are noted. For example, measurements (AKA metrics) for roles may be kept so that user accounts can be distinguished from one another according to how the user accounts perform in the various roles. 
     In some of these embodiments, it is alternatively or additionally determined which role various user accounts play with respect to a trend, and therefore the time point that a trend begins is determined by worker  120 , for example by trend determiner  380 . A time period before the trend begins is selected and/or a time period after the trend begins is selected. The role or roles played by various user accounts via posts and/or interactions in the time period is/are noted. For example, measurements (AKA metrics) for roles may be kept so that user accounts can be distinguished from one another according to how the user accounts perform in the various roles. 
     Depending on the embodiment the notation of roles may or may not reflect the extent of the roles. For example, if not based on the extent then worker  120 , for example counter  302 , may reflect the notation but may be indifferent to the extent, whereas if based on the extent then worker  120 , for example counter  302 , may reflect the extent by the metric value. 
     In some cases, the noted roles (over time and/or with respect to a trend) can be used as a basis for selecting user accounts. For instance user accounts which were noted as contributors (or catalogers, etc) may be selected. In various cases where the noted roles are a basis for selecting user accounts, selection of user accounts may or may not be based on the extent of the roles. For example, assuming the extent of roles is expressed by role metrics, a first user account may have a high metric for the contributor role and a low metric for a commenter role whereas a second user account may have a low metric for the contributor role and a high metric for the commenter role. Therefore, if a particular consumer  190  is interested in user accounts which are contributors, the consumer in this example may be more interested in the first user account, whereas if the particular consumer  190  is instead interested in commenters, then the consumer in this example may be more interested in the second user account. 
     In various embodiments, the group of posts which are considered and/or the collection of interactions which are considered may include all available posts and interactions (bound or not bound by given time period(s)), or may include only a subset of posts, for example those fulfilling predetermined criteria (bound or not bound by given time period(s)), and associated interactions. 
     An example of a method of keeping track of roles played by user accounts with respect to a trend will now be described with reference to  FIG. 5 . This example is provided to aid in the understanding of the reader and should not be construed as restricting the invention. Furthermore, the example is described with reference to the interactions graph of  FIG. 2 , in order to aid in the understanding of the reader, but the ranking of user accounts is not restricted to social interactions as illustrated in  FIG. 2 . 
       FIG. 5  is a flowchart of a method  500  of keeping track of roles played by user accounts with respect to a trend, according to some embodiments of the present invention. In some cases, method  500  may include fewer, more and/or different stages than illustrated in  FIG. 5 , the stages may be executed in a different order than shown in  FIG. 5 , and/or stages that are illustrated as being executed sequentially may be executed in parallel. 
     In the illustrated embodiments in stage  504 , the beginning of a trend is detected. Stage  504  can be performed for example by trend determiner  380 . 
     For example, refer to  FIG. 6  which shows over a given period of time graphs of the number of posts and interactions per day and graphs of the running average deltas of posts and interactions, according to some embodiments of the present invention. Curve  602  represents the number of posts per day and the curve  604  represents the number of interactions with those posts per day. Curve  606  represents the running average delta for posts and curve  608  represents the running average delta for interactions. For example, the running average delta may be calculated by calculating the difference between the current value and the previous value and then taking a running average of the differences. 
     In some embodiments, the posts graphed in  FIG. 6  belong to a subset of posts collected by system  110  which fulfill certain criteria over the time illustrated. In other embodiments, the posts graphed in  FIG. 6  represent all of the posts collected by system  110  over the time illustrated. 
     In the illustrated embodiments, when looking for local maximums in the running average delta curves in a variable time window, it can be seen that at timepoint B  610 , running average delta curves  606  and  608  reach maximums (peaks). The variable time window is not limited by the invention, and depending on the embodiment may be a few days, a week, etc. After timepoint B  610 , the increase in posts and interactions per day slows down. In some embodiments, if the difference D  612  between the interactions running average delta curve and the posts running average delta curve is above a predetermined threshold then it is assumed that timepoint B  610  represents the beginning of a trend. In these embodiments, if the difference D  610  is below a predetermined threshold then it is assumed that timepoint B  610  is not the beginning of a trend. For example, it may be assumed that if there are too many posts in relation to the number of interactions and that therefore the posts have not had sufficient online impact to mark a trend. In other embodiments, there may not be a comparison to a threshold and a trend may be detected for peaks regardless of the value for difference D  610 . 
     In other embodiments, alternatively or additionally the beginning of a trend may be detected based on input from a consumer  190  who inputs the timepoint B, stating that at this time the trend began. 
     In the illustrated embodiments, in the remaining stages of method  500 , role metrics are determined for a predetermined period (AKA timeframe) before the beginning of the trend (in  FIG. 6 , period A  620 ) and for a predetermined period (AKA timeframe) after the beginning of the trend (in  FIG. 6 , period C  630 ). The duration of the predetermined period before the beginning of the trend and the duration of the predetermined period after the beginning of the trend may vary depending on the embodiment. The remaining stages of method  500  may be executed for instance by counter  302  and/or post ranker  360 . 
     For example, in some embodiments, in order to measure performance in various roles, a specific counter  302  is maintained for each user account and for each role that is tracked by system  110 . For instance, the counter can be defined by &lt;user account, &lt;role, rank&gt;&gt;. Alternatively or additionally, in some embodiments of this example a specific counter may be maintained for each subset of posts. For instance the counter can be defined by &lt;user account, &lt;subset, &lt;role, rank&gt;&gt;&gt;. 
     In the illustrated embodiment, for each post in the timeframe before the beginning of the trend, beginning with a first post in stage  508  one or more role performance measurements are increased for the user account of the owner of the post, for instance by increasing the count in the corresponding counter(s). For example, the counter associated with the contributor role may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. The reader is referred to method  400  above for an example of a procedure for ranking a post. As another example, additionally or alternatively, if the post had ownerless views, the counter associated with “attracting views” role may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. As another example, additionally or alternatively, if the post had ownerless ratings the counter associated with “engaging role” may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. 
     Next in the illustrated embodiments, each interaction on the currently considered post within the timeframe before the beginning of the trend is considered. Beginning with a first interaction, in stage  516 , one or more role performance measurements are increased for the interacting user account, for instance by increasing the count in the corresponding counter(s). For example, the counter associated with connector role may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day, if the interaction was a share or link. As another example, if the interaction including marking as a favorite, the counter associated with spotter role may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. As another example, if the interaction included a comment, the counter associated with maven role may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. As another example, if the interaction included adding a tag, the counter associated with cataloger may be increased by the rank of the post (AR obj ), where the age is assumed to be 1 day. 
     It should be evident that the invention is not bound by the described values added to the role measurements for posts and for various types of interactions, and that in some embodiments the values added may be more or less than described above (or may be insignificant if the extent of the roles is not important). It should also be evident that the invention is not bound by the described specific roles for which measurement values are increased for posts and/or interactions and that in some embodiments more, less, and/or different roles may have measurement values increased additionally or alternatively to the roles described above. 
     If there are more interactions on the currently considered post (yes to stage  520 ) within the timeframe before the beginning of the trend, then stage  516  is repeated for a next interaction. Once all interactions on the currently considered post within the timeframe before the beginning of the trend have been considered (no to stage  520 ), then if there are remaining posts in the timeframe before the trend began (yes to stage  528 ) then stages  508  to  528  are repeated for the next post. Once all posts from the timeframe before the trend began have been processed (no to stage  528 ) then method  500  continues to stage  540 . 
     To further illustrate stages  504  to  532  to the reader, some numerical calculations are now presented. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. 
     Referring again to  FIG. 2 , assume that posts  242  and  272  occurred in the timeframe before the beginning of the trend. Therefore user account  240  which owns post  242  has contributor role counter increased by the rank of the post (assuming age of 1 day). As assumed above, post  242  is the focus of one interaction in the timeframe by user account  270  (rank 3) with m share  of 4 out of a total of one share interaction executed by user account  270  on any posts. Therefore the rank of the post using equation 1 is computed to be 
     
       
         
           
             
               
                 ( 
                 
                   
                     
                       4 
                       × 
                       1 
                       × 
                       3 
                     
                     1 
                   
                   + 
                   0 
                 
                 ) 
               
               · 
               
                  
                 
                   
                     - 
                     0.01 
                   
                   × 
                   1 
                 
               
             
             = 
             
               11.9 
               . 
             
           
         
       
     
     Similarly user account  270  which owns post  272  has contributor role counter increased by the rank of post  272 , but since the rank of post  272  is zero the counter is not affected. However, since user account  270  shared post  242  (via post  272 ), during the predetermined time frame, the connector role counter for user account  270  is increased by the rank of post  242 , where the rank of the post is 11.9 as calculated above. 
     In the illustrated embodiments, for each post in the timeframe after the beginning of the trend, beginning with a first post in stage  540  one or more role performance measurements are increased for the user account of the owner of the post, for instance by increasing the count in the corresponding counter(s). For example, the counter associated with the salesman role may be increased by the rank of the post (AR obj ), where the age is assumed to be the date of the post minus the date of the beginning of the trend (recall that the date of the beginning of the trend is represented by date B  610 ). As another example, additionally or alternatively, if the post had ownerless views the counter associated with “attracting views” role may be increased by the rank of the post (AR obj ), where the age is assumed to be the time passed from time B  610  when the trend began until the time of the views. As another example, additionally or alternatively, if the post had ownerless ratings the counter associated with “engaging role” may be increased by rank of the post (AR obj ), where the age is assumed to be the time passed from time B  610  when the trend began until the time of the ratings. 
     Next, in the illustrated embodiments, each interaction on the currently considered post within the timeframe after the beginning of the trend is considered. Beginning with a first interaction, in stage  544 , one or more role performance measurements are increased for the interacting user account, for instance by increasing the count in the corresponding counter(s). For example, the counter associated with salesman role may be increased by the rank of the post (AR obj ), where the age is assumed to be date of the interaction minus the date of the beginning of the trend, if the interaction was a share or link. As another example, if the interaction including marking as a favorite, the counter associated with filter role may be increased by the rank of the post (AR obj ); where the age of the post is assumed to be the date of the interaction minus the date of the beginning of the trend. As another example, if the interaction included a comment, the counter associated with commenter role may be increased by the rank of the post (AR obj ), where the age of the post is assumed to be the date of the interaction minus the date of the beginning of the trend. As another example, if the interaction included adding a tag, the counter associated with cataloger may be increased by the rank of the post (AR obj ), where the age of the post is assumed to be the date of the interaction minus the date of the beginning of the trend. 
     It should be evident that the invention is not bound by the described values added to the role measurements for posts and for various types of interactions, and that in some embodiments the values added may be more or less than described above (or may be insignificant if the extent of the roles is not important). It should also be evident that the invention is not bound by the described specific roles for which measurement values are increased for posts and/or interactions and that in some embodiments more, less, and/or different roles may have measurement values increased additionally or alternatively to the roles described above. 
     If there are more interactions on the currently considered post (yes to stage  556 ) within the timeframe after the beginning of the trend, then stage  552  is repeated for a next interaction. Once all interactions on the currently considered post within the timeframe after the beginning of the trend have been considered (no to stage  556 ), then if there are remaining posts in the timeframe after the trend began (yes to stage  564 ) then stages  544  to  564  are repeated for the next post. Once all posts from the timeframe after the trend began have been processed (no to stage  564 ) then method  500  ends. 
     To further illustrate stages  540  to  566  to the reader, some numerical calculations are now presented. It should be evident that these values are just for the sake of providing numerical calculations and are not binding. 
     For example, assume that posts  202 ,  264 , and  212  were posted after the trend began but within the time frame, with posts  202  and  264  posted 5 days after the trend began and post  212  was posted 15 days after the trend began. (It is assumed that post  268  was posted past the time frame). 
     In this example, user account  204  which owns post  202  has salesman role counter increased by the rank of post  202  (AR obj  with post age equaling 5). As assumed above post  202  is the focus of one interaction by user account  260  (rank of 5) in the timeframe with m link  of 2, out of a total of one link interaction performed by user account  260  on any posts. Further assume that 0.5 million of the 1 million views on post  202  occurred after the trend began but within the time frame (out of a total of 600 thousand views for all posts in the time frame) with m view  equaling 1. Further assume that five thousand of the ten thousand ratings on post  202  occurred after the trend began but within the time frame (out of a total of five thousand views on all posts in the time frame) with M rating  equaling 2 Using equation 1 the rank of post  202  is 
     
       
         
           
             
               
                 ( 
                 
                   
                     
                       2 
                       × 
                       1 
                       × 
                       5 
                     
                     1 
                   
                   + 
                   
                     1 
                     × 
                     
                       ( 
                       
                         
                           
                             1 
                             × 
                             500 
                              
                             
                                 
                             
                              
                             thousand 
                           
                           
                             600 
                              
                             
                                 
                             
                              
                             thousand 
                           
                         
                         + 
                         
                           
                             2 
                             × 
                             5 
                              
                             
                                 
                             
                              
                             thousand 
                           
                           
                             5 
                              
                             
                                 
                             
                              
                             thousand 
                           
                         
                       
                       ) 
                     
                   
                 
                 ) 
               
               · 
               
                  
                 
                   
                     - 
                     0.01 
                   
                   × 
                   5 
                 
               
             
             = 
             12.2 
           
         
       
     
     and therefore the salesman role counter for user account  204  is increased by 12.2. Further for simplicity&#39;s sake, instead of using the daily counts of views assume that the age of the views can be represented as 10 days after the trend began. The “attracts views” counter for user account  204  is therefore increased by the rank of post  202  with views age of 10. Using equation 1 the rank of post  202  with views age of 10 is 
     
       
         
           
             
               
                 
                   2 
                   × 
                   1 
                   × 
                   5 
                 
                 1 
               
               + 
               
                 1 
                 × 
                 
                   
                     ( 
                     
                       
                         
                           1 
                           × 
                           500 
                            
                           
                               
                           
                            
                           thousand 
                         
                         
                           600 
                            
                           
                               
                           
                            
                           thousand 
                         
                       
                       + 
                       
                         
                           2 
                           × 
                           5 
                            
                           
                               
                           
                            
                           thousand 
                         
                         
                           5 
                            
                           
                               
                           
                            
                           thousand 
                         
                       
                     
                     ) 
                   
                   · 
                   
                      
                     
                       
                         - 
                         0.1 
                       
                       × 
                       10 
                     
                   
                 
               
             
             = 
             
               11.6 
               . 
             
           
         
       
     
     For simplicity&#39;s sake, 
     instead of using the daily counts of ratings assume that the age of the ratings can be represented as 12 days after the trend began. The “engaging” counter for user account  204  is therefore increased by the rank of post  202  with rating age of 12. Using equation 1 the rank of post  202  with ratings age of 12 is 
     
       
         
           
             
               
                 
                   2 
                   × 
                   1 
                   × 
                   5 
                 
                 1 
               
               + 
               
                 1 
                 × 
                 
                   
                     ( 
                     
                       
                         
                           1 
                           × 
                           500 
                            
                           
                               
                           
                            
                           thousand 
                         
                         
                           600 
                            
                           
                               
                           
                            
                           thousand 
                         
                       
                       + 
                       
                         
                           2 
                           × 
                           5 
                            
                           
                               
                           
                            
                           thousand 
                         
                         
                           5 
                            
                           
                               
                           
                            
                           thousand 
                         
                       
                     
                     ) 
                   
                   · 
                   
                      
                     
                       
                         - 
                         0.01 
                       
                       × 
                       10 
                     
                   
                 
               
             
             = 
             
               11.4 
               . 
             
           
         
       
     
     In this example, user account  210  which owns post  212  has salesman role counter increased by the rank of post  212  (AR obj  with post age 15). It is assumed as above that the comment interaction occurred within the time frame by user account  250  (rank of 6) with m comment  of 3 out of a total of one comment by user account  250  on any posts. It is also assumed that 100,000 of the 500,000 views occurred in the time frame (out of a total of 600,000 views for all posts in the time frame) with m view  equaling 1. Using equation 1 the rank of post  212  is 
     
       
         
           
             
               
                 
                   
                     3 
                     × 
                     1 
                     × 
                     6 
                   
                   1 
                 
                 + 
                 
                   1 
                   × 
                   
                     
                       
                         1 
                         × 
                         100 
                          
                         
                             
                         
                          
                         thousand 
                       
                       
                         600 
                          
                         
                             
                         
                          
                         thousand 
                       
                     
                     · 
                     
                        
                       
                         
                           - 
                           0.01 
                         
                         × 
                         15 
                       
                     
                   
                 
               
               = 
               15.6 
             
             , 
           
         
       
     
     and therefore the salesman counter is increased by 15.6. Again for simplicity&#39;s sake instead of using the daily counts of views assume that the age of the views can be represented as 20 days after the trend began. The attracts views counter for user account  210  is therefore increased by the rank of post  212  with views age of 20. Using equation 1 the rank of post  212  with views age of 20 is 
     
       
         
           
             
               
                 
                   3 
                   × 
                   1 
                   × 
                   6 
                 
                 1 
               
               + 
               
                 1 
                 × 
                 
                   
                     
                       1 
                       × 
                       100 
                        
                       
                           
                       
                        
                       thousand 
                     
                     
                       600 
                        
                       
                           
                       
                        
                       thousand 
                     
                   
                   · 
                   
                      
                     
                       
                         - 
                         0.01 
                       
                       × 
                       20 
                     
                   
                 
               
             
             = 
             
               14.9 
               . 
             
           
         
       
     
     In this example, if it is instead assumed that post  212  was not in a subset of posts studied, for example because the subset only dealt with posts relating to “making history” then the salesman counter and attracts view counter for user account  210  are not increased. 
     In this example, user account  260  which owns post  264  does not have salesman role counter increased since the rank of post  264  is assumed to be zero. However user account  260  has a link to post  202 . Therefore the salesman role counter for user account  260  is increased by the rank of post  202  with interaction age of 5 days. Using equation 1, the rank of post  202  with interaction day of 5 is 12.2 and therefore the salesman role counter for user account  260  is increased by 12.2. 
     In this example, if instead it is assumed that post  212  was not in a subset of posts studied, for example because the subset only dealt with posts relating to “making history” then post  202  would have all views in the relevant time frame and so the rank of post  202  (with age 5 for the post and for the link interaction to the post) would have been adjusted to 12.4. Therefore the salesman rank counters of the owner of the post (user account  204 ) and of the linker to the post (user account  260 ) would have been instead increased by 12.4. It is noted that the age of the interaction and the post may in some embodiments be different and therefore the salesman rank counters for the owner and linker would have been increased by different amounts in these embodiments. Similarly if instead it is assumed that post  212  was not in a subset of posts studied, then the “attracts views” counter for user account  204  would be adjusted to 11.8 and the engaging counter for user account  204  would be adjusted to 11.6. 
     In this example, assuming post  212  was among the posts studied then user account  250  has commenter role counter increased by the rank of post  212 . Assume the comment was posted within the timeframe 2 days after post  212  was posted and therefore the age is 17 days. It is again assumed as above that the comment interaction was by user account  250  (rank of 6) with m comment  of 3 out of a total of one comment by user account  250  on any posts, and that assumed that 100,000 of the 500,000 views occurred in the time frame (out of a total of 600,000 views for all posts in the time frame) with m view  equaling 1. Using equation 1 the rank of post  212  is 
     
       
         
           
             
               
                 
                   3 
                   × 
                   1 
                   × 
                   6 
                 
                 1 
               
               + 
               
                 1 
                 × 
                 
                   
                     
                       1 
                       × 
                       100 
                        
                       
                           
                       
                        
                       thousand 
                     
                     
                       600 
                        
                       
                           
                       
                        
                       thousand 
                     
                   
                   · 
                   
                      
                     
                       
                         - 
                         0.01 
                       
                       × 
                       17 
                     
                   
                 
               
             
             = 
             
               15.3 
               . 
             
           
         
       
     
     If post  212  was not among those studied then the commenter role counter of user account  250  is not increased. 
     In some embodiments, the role counters for user accounts definitely or with at least a predetermined probability of corresponding to the same profile can be combined through a function. For example, in one of these embodiments counter values for the same role from each user account corresponding to the same profile can be added together. 
     In various embodiments, none, any, or all of the following may be applied to user accounts and/or profiles whose roles have been noted: filtering, selecting, and/or sorting. 
     With regard to filtering, depending on the embodiment a request from consumer  190  may relate to a subset of posts which fulfill certain criteria or to all posts. If for a subset of posts, then depending on the embodiment the role analysis may proceed in any of the following ways. In some cases, only the subset of posts and interactions thereof may be analyzed to evaluate the role(s) of various user accounts for example using method  500 . Alternatively, all posts and interactions thereof can be analyzed to evaluate the role(s) of various user accounts for example using method  500  and then the results of the evaluations can be filtered based on the request. Alternatively, only a subset of posts and interactions thereof may be analyzed to evaluate the role(s) of various user accounts for example using method  500  and then the results may be further filtered. For example the filter may retain only user accounts which own a minimum number of posts within the subset and/or user accounts which interacted a minimum number of times with posts in the subset. 
     In some embodiments, user accounts or profiles can be selected based on whether or not one or more roles were played and/or the extent of the role(s). For example, only user accounts/profiles which played a salesman role may be selected and/or that the hundred user accounts/profiles with the highest salesman role value may be selected. As another example, user accounts/profiles which played both contributor and salesman roles and/or whose contributor and salesman role values are both among the top hundred may be selected. Alternatively or additionally, user accounts or profiles can be sorted based on the magnitude of one or more role measurements. For example, the extent of the role played by a user account/profile may in some cases be used to determine the order of display, with user accounts/profiles who played one or more roles to a higher extent outputted in a more prominent position than user accounts/profiles who played those role(s) to a lower extent. Continuing with the example, in some cases if the consumer request was for the hundred user accounts/profiles with the highest salesman role value, the user account/profile out of the hundred with the highest value may be outputted in a more prominent position than the user/account profile out of the hundred with the lowest value. In embodiments with selection and/or sorting, the noted roles may serve as a primary selection and/or sorting factor, a secondary selection and/or sorting factor, or an even less important selection/sorting factor. 
     As mentioned above, more than one user account may correspond to the same profile. However in some cases it is challenging to determine whether or not user accounts correspond to the same profile.  FIG. 7  is an example of a social graph according to some embodiments of the invention.  FIG. 7  will be used to illustrate various aspects of the present invention. It is noted that a social graph is presented here as a visual tool to aid in the understanding of the reader, but that system  110  does not necessarily construct nor store such a graph. It is also noted that the number of user accounts and profiles included in the social graph of  FIG. 7  are not many so as to not confuse the reader. 
     For simplicity&#39;s sake it is assumed that the social graph in  FIG. 7  shows connections representing relationships between two user accounts which were explicitly defined by one or both user account (e.g. friends, following you, family connections, work relations, etc). The edges connecting user accounts may be one direction edges or two direction edges depending on the relationship.  FIG. 7  also shows weighted edges between user accounts and profiles. The weights represent the probability level that the user account and profile correspond (AKA the certainty level about the authenticity of the connection). It will now be explained how correspondence is established. 
     In some embodiments, it is initially assumed that user accounts do not correspond with one another. Data is analyzed relating to two online user accounts. The probability that two online user accounts both correspond to a common profile is increased as more data points to the user accounts corresponding to a common profile. If the probability that two online user accounts both correspond to a common profile is above a predetermined threshold, it is concluded that the two online user accounts correspond to a common profile. Depending on the embodiment, the profile for corresponding user accounts may or may not be provided to consumers  190 . For example, in some cases only profiles which were authenticated (and are therefore definite, 100% probability) are provided whereas other profiles are not provided. 
     One example of a method of identifying corresponding user accounts is described with reference to  FIG. 8 . This example is provided to aid in the understanding of the reader and should not be construed as restricting the invention. 
       FIG. 8  illustrates a flowchart of a method  800  for identifying corresponding user accounts, according to some embodiments of invention. In some cases, method  800  may include fewer, more and/or different stages than illustrated in  FIG. 8 , the stages may be executed in a different order than shown in  FIG. 8 , stages that are illustrated as being executed in parallel may be executed sequentially, and/or stages that are illustrated as being executed sequentially may be executed in parallel. 
     In the illustrated embodiments, in stage  802  it is initially assumed that there is zero probability that a certain user account corresponds to any other user account. 
     In the illustrated embodiments in stages  804  to  854 , system  110 , for example comparer  320 , analyzes data relating to the certain user account. If warranted based on the analysis, then system  110 , for example counter  302 , increases the probability that a certain user account corresponds to other user account(s). The analyzed data may have been collected by crawlers  115  and/or input by consumers  190 . The data may be analyzed using one or more techniques, and if a plurality of techniques are used then the different techniques may be applied sequentially and/or in parallel. In some embodiments, there may be a point when based on the analysis it is warranted that the probability of the certain user account corresponding to any other user account first increases above zero, or some other floor. In some of these embodiments, at this point there may be an automatic creation by system  110 , for example by profile processor  375 , of a profile corresponding to the user accounts and/or assignment of a probability to the edge connecting each corresponding user account to the profile. 
     In the illustrated embodiments, for example, in stage  804 , posts owned by the certain user account may be checked for uniform resource identifiers “URI”s to other posts. In some embodiments, the checking may be for URI&#39;s whose relationship attribute is defined as “me”, for example in an hypertext markup language “HTML” microformat such as XHTML friends network “XFN”. In some embodiments, third party tools  182  such as social graph services (e.g. Google Social Graph API) facilitate the performance of stage  804 . 
       FIG. 9  shows an example according to some embodiments of a page in Google Social Graph API, showing other possible user accounts belonging to a profile “Niv Singer” with known user account Flickr and Twitter accounts. In some cases, the other possible user accounts found by Google social graph API is stored and the potential owner (in this case Niv Singer) is given a choice to either confirm that the found user accounts belong to him or not. 
     In the illustrated embodiments in stage  808 , if warranted, the probability is increased that the user account which owns the post (whose URI was in a post belonging to the certain user account) and the certain user account correspond to the same profile. The decision of when raising the probability is or is not warranted and the decision by how much to raise the probability if warranted may vary depending on the embodiment. In one example, the probability is increased for each link with “me” relationship attribute between posts belonging to two user accounts, with the maximum possible increase due to links capped at 20%. 
     In the illustrated embodiments, another example of analysis is illustrated by stages  820  and  824 . In stage  820  it is determined if information associated with the certain user account is identical to information associated with one or more other user accounts. For instance, there may be one or more email addresses or email hashes (where the email hashes may be provided for example by applying the SHA1 mathematical functional to a ‘mailto:’ identifier (URI) for an Internet mailbox) associated with a certain user account. Other user accounts may be checked for one or more identical email address/hashes. In stage  824 , if warranted the probability is increased that the certain user account and the other user account with identical information correspond to the same profile. The decision of when raising the probability is or is not warranted and the decision by how much to raise the probability if warranted may vary depending on the embodiment. In one example the decision on whether or not to increase the probability and by how much depends on the quantity and/or quality of identical information, where the maximum possible increase due to identical information is capped at 30%. Continuing with the example, in one embodiment identical age may not affect the probability, but identical email address may cause an increase in probability. 
     In the illustrated embodiments, another example of analysis is illustrated by stages  830  and  834 , termed mutual connections. In stage  830  it is checked if any other user account that has a relationship with a certain user account also has a relationship with an additional user account. Similarly it may be checked if a user account corresponding to the other user account has a relationship with an additional user account. In stage  834 , if warranted the probability is increased that the certain user account and the additional user account with mutual connections correspond to the same profile. The decision of when raising the probability is or is not warranted and the decision by how much to raise the probability if warranted may vary depending on the embodiment. In one example, the probability is increased for each mutual connection, with the amount of increase depending on the quality of the relationship and the level of certainty that the connection is mutual, with the maximum possible increase in probability due to mutual connections capped at 20%. 
     In the example illustrated in  FIG. 7 , user accounts A and D correspond (100% probability due to authentication) to profile E 1 . Similarly, H and G correspond (100% probability due to authentication) to profile E 2 . Since A and G are both connected to B and also D and H are both connected to F, the probability of B and F both corresponding to profile E 3  is increased. In this example, because of the 100% probability that A and D correspond and H and G correspond, the probability that B and F correspond is increased by a relatively high 20%. It is assumed in the example shown that the initial probability was zero and therefore the total probability of B and F corresponding is 20%. In some embodiments, E 3  may be a new profile automatically created based on this scenario. If one or more of the levels of probability that A and D correspond and H and G correspond was lower (i.e. less certainty of a mutual connection), then in some cases of this example the increase in probability may have been less than 20%. 
     In the illustrated embodiments, another example of analysis is illustrated in stages  840  and  844 , termed crowd sourcing. In stage  840  it is checked if input was received from a consumer  190  (who is not the owner of the certain account) recording that another account corresponds to the certain account. In some cases, only input from a consumer  190  which is a member of system  110  is allowed in stage  840 . In stage  844 , if warranted, the probability is increased that the certain user account and the recorded other account correspond to the same profile. For example, system  110  may allow consumers  190  (or only certain consumers  190 ) to group corresponding user accounts (e.g. their contacts). Depending on the embodiment of this example, the grouping may be kept private (visible only to the consumer  190  which provided the grouping or to select consumers  190 ), or may be public. In some embodiments, consumers  190  (or only certain consumers) may also be able to ungroup and/or report invalid groupings. In some embodiments, consumers  190  (or only certain consumers  190 ) may be able to rate the trust level of other consumers  190  in terms of whether or not groupings provided by the other consumers are trustworthy. The decision of when raising the probability is or is not warranted and the decision by how much to raise the probability if warranted may vary depending on the embodiment. In one example, the decision and/or amount may be based on the rating of the consumer(s) which provided the grouping and/or based on the number of consumers which provided the same grouping, with the maximum possible increase in probability due to crowd sourcing capped at 20%. 
     It is noted that the maximum possible probability of correspondence from stages  804  to  844  is less than 100%, for example 90%. 
     In the illustrated embodiments 100% certainty of correspondence is achieved through the analysis illustrated in stages  850  and  854 . In stage  850 , it is determined if authentication has been received for the certain account, authenticating the certain account as corresponding to at least one other account. In stage  854 , if authentication is received then the probability is increased to 100% that the certain user account and the other user account correspond to the same profile. In some embodiments, authentication is allowed only by the possessor of the user account. In some of these embodiments, authentication is allowed only by the possessor of the user account who is a member of system  110 . For example, in some of these embodiments, a consumer  190  may be allowed to log in to system  110  and prove user account possession by validating credentials (e.g. username/password) and/or by using password less authentication schemes (such as OAuth, OpenID, AuthSub, etc). The credentials or authentication tokens may be stored by system  110  in order to query information regarding user accounts and/or to perform actions on behalf of members. Depending on the embodiment, a consumer  190  may or may not be able to authenticate more than one account per external source. 
     It should be evident that the invention is not restricted to the maximum percentages assigned to the various examples of analysis described above. It should also be evident that in some embodiments, additional, less, and/or different analysis than the examples described above may be executed in order to attempt to raise the probability of correspondence. 
     Assuming authentication has not occurred, then in the illustrated embodiments method  800  continues with stage  860  to  868  performed by worker  120 , for example comparer  320 . In stage  860  it is determined if there is more than one user account in any particular external source which has a probability above zero of corresponding to the certain user account. If yes, then in stage  864  the user account in the particular external source with the highest probability is designated to be considered in stage  868  for possible correspondence with the certain user account and the other user accounts are designated as eliminated from consideration (i.e. it is concluded that the other user accounts do not correspond). If for all external sources not more than one user account has a probability above zero then stage  864  is omitted. In stage  868 , for each user account with non-zero probability of corresponding to a common profile as the certain user account (and which was not eliminated from consideration in stage  864 ), it is determined if the probability is above a predetermined threshold. If yes, then in stage  872  the accounts are considered to correspond to a common profile with sufficient probability, although not authenticated. If no, then in stage  876  the accounts are considered to not correspond. In some embodiments, stage  860  to  864  are omitted and in stage  868  for each user account with a non-zero probability (even if more than one user account from an external source qualifies) it is determined if the probability is above a predetermined threshold. The invention does not restrict the value of the predetermined threshold. 
     In some embodiments, only user accounts which were authenticated as corresponding to a common profile are indicated to consumers as corresponding. In some embodiments, additionally or alternatively, user accounts which are considered to correspond to a common profile because the probability is above a predetermined threshold may be indicated to consumers as corresponding, optionally with the level of probability also indicated. In some embodiments, additionally or alternatively, user accounts which are considered unlikely to correspond to a common profile because the probability is below a predetermined threshold may be indicated to consumers as potentially corresponding, optionally with the level of probability also indicated. 
     In some embodiments, worker  120  additionally or alternatively processes the collected data in order to identify a vogue. For example, while collecting data the number of interactions and other information (such as Tag use) per post is counted. The delta value between the new value and the previous value is stored as well. Tag counts and delta values are aggregated and stored per Tag and per Tag/Interaction Type pair (e.g. number of comments for posts containing a specific Tag). In this example, by looking at posts or Tags with high delta values (negative and positive), in vogue topics (e.g. most commented Tags in the past week) may be identified. Posts with high, positive delta values are marked as popular or upcoming (becoming interesting). In some cases of this example, data from external sources (e.g. Flickr® interestingness, YouTube most popular, Twitter™ Search, etc) is additionally or alternatively taken into account. For instance, the following formula calculates the “interestingness” rank of a YouTube video, by calculating the ratio of passive viewers vs. interacting viewers: 
     
       
         
           
             
               
                 
                   
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     (p and q are constants used to adjust the ratio between a predetermined maximum rate, a comment and a favorite. For example in some cases p, q equal 5). 
     An example of application  145  will now be described. In this example, described with reference to  FIG. 10 , the application allows end to end campaign management. This example is provided to aid in the understanding of the reader and should not be construed as restricting the invention. 
     Refer to  FIG. 10  which shows a flowchart of a method  1000  of campaign management, according to some embodiments of the invention. Some of the stages are illustrated by screenshots, provided to aid in reader understanding. The format and content of the screenshots should not be construed as limiting the invention. In some cases, method  1000  may include fewer, more and/or different stages than illustrated in  FIG. 10 , the stages may be executed in a different order than shown in  FIG. 10 , and/or stages that are illustrated as being executed sequentially may be executed in parallel. 
     In the illustrated embodiments in stage  1002 , application  145  receives a definition of a campaign from consumer  190 . In some embodiments, each campaign is divided into scopes, each scope is optionally divided into topics, and each topic is defined by one or more queries of keywords separated by Boolean operators. For example, in some of these embodiments, there are four scopes, the first being brand content, the second being competition, the third being ecosystem and the fourth being campaign specific content. Continuing with the example, in some cases posts related to each scope and/or each topic comprise a separate group. Assume that the campaign is an advertisement campaign for a shoe chain named “gowear”, which competes with other shoe chains such as “slick”, and “footsies”, and that the current campaign slogan is “feel the walk”. Under these assumptions, the first scope on brand content may include inter-alia the keyword “gowear”, the second scope on competition may include inter-alia the keywords “slick” and “footsies” (with each one possibly a separate topic) the third scope may include inter-alia the keyword “shoe”, and the fourth scope may include inter-alia the keyword “feel the walk”. In some cases the first and fourth scope may also include spotlight definitions which are either URI&#39;s or user accounts of interest to the campaign. For example, if on a social network there is a page for the brand and certain user accounts communicated with the page, these user accounts may be of interest to the campaign even if owned posts of these user accounts are not captured by the keywords.  FIG. 11  is a screenshot illustrating some aspects of stage  1002  campaign definition, according to some embodiments of the invention. 
     Alternatively or additionally, in some embodiments the definition of a campaign from consumer  190  may include choosing of one or more categories (for example used in clustering posts) and/or choosing of one or more market verticals (for example used in clustering posts). 
     In stage  1004 , a subset of posts relevant to the campaign is selected, based on the definition from stage  1002 . For example worker  120 , for instance index searcher  340 , may search index  135  for posts which contain keyword(s) entered in stage  1002  for any of the scopes/topics or for one or more particular scope/topic. As another example, additionally or alternatively worker  120 , for instance index searcher  340 , may search index  135  for posts belonging to one or more categories and/or market verticals chosen in stage  1002 . As another example, index searcher  340  may search index  135  for posts which contain keyword(s) entered in stage  1002  and belong to one or more chosen categories and/or market verticals. The selected posts are then associated with the campaign and/or specific scope/topic of the campaign. 
     In stage  1006 , analysis results relevant to the defined campaign are reported to consumer  190 . Depending on the embodiment, the analysis may be performed on the subset of posts, the analysis may be performed on all posts but then filtered for only the subset of posts, the analysis may be performed for only the subset of posts but then further filtered, or any combination of the above. The analysis may be performed at any time prior to the reporting. The analysis may be performed for example by worker  120  with the reporting for example prepared by reporting module  145 . 
     In some embodiments, any of the following analysis results, inter-alia, may be reported to consumer  190 : (Refer to  FIGS. 12 through 17  which illustrate some of the various aspects of campaign reporting, according to various embodiments of the present invention). 
     Total posts count—For example section  1202  in  FIG. 12  shows the total number of posts relevant to the defined campaign. 
     Posts count over time—For example graph  1302  in  FIG. 13  shows the number of posts over time for a particular profile relevant to the defined campaign. 
     Total conversation count (where conversation count equals the number of posts with at least one interaction). For example section  1204  in  FIG. 12  shows the total number of conversations relevant to the defined campaign. 
     Conversations count over time—For example graph  1208  shows conversation count relevant to the defined campaign over the past month in various external sources. For example graph  1408  shows conversation count relevant to the defined campaign over a longer period of time (including both before the campaign began and after the campaign began) in various external sources. 
     Campaign engagement. The number of conversations on campaign generated content (e.g. content created by consumer  190  as campaign manager or in response to specific campaign keywords) can in some cases provide a measurement of campaign engagement. 
     Interactions count—Total and/or over time. For example section  1230  in  FIG. 12  shows the total interactions and the distribution by external source. 
     Number of participating user accounts, for example owners of posts and/or interacting owners. In some embodiments the user accounts may be grouped by demographics such as age, location, gender. For example, section  1220  in  FIG. 12  shows the distribution of user accounts by age and section  1224  in  FIG. 12  shows the distribution of user accounts by gender. The total number of participating owners and/or the number of participating owners over time may be provided. 
     A list of one or more ranked user accounts or profiles. For instance the rank may be determined as described above inter-alia with reference to  FIG. 4 . Depending on the embodiment, the rank may be reported to the consumer and/or used internally by system  110 , for example for optional selection and/or optional sorting. In some embodiments with selection, rank may be the only selection factor or one of the selection factors for determining which user accounts and/or profiles are provided. For example, in one embodiment only user accounts and/or profiles with rank above a predetermined threshold, and/or the in the top predefined percentile of ranks are reported to consumer  190 . Depending on the embodiment with sorting, the list of user accounts and/or profiles may be sorted primarily by rank or secondarily or even less importantly by rank. For example, in some cases the primary sort of profiles or user accounts may be based on activity (number of owned posts) or impact (number of conversations started by owned posts). In these cases the sort by activity would render the “most active” profiles or user accounts whereas the sort by impact would render the “most influential” profiles or user accounts. In this example, the secondary sort would be based on the rank. However in another example, in some cases, the primary sort is by rank. As mentioned above, ranking may have been performed using only a subset of posts relevant to the defined campaign, ranking may have been performed using all posts and then filtering based on the defined campaign, or ranking may have been performed on a subset of posts with further filtering then performed. Therefore, in various embodiments, none, any, or all of the following may be applied to user accounts and/or profiles after ranking: filtering, selecting, and/or sorting. In some embodiments, the presentation of the user accounts and/or profiles to the consumer reflects the rank in any one of various ways including inter-alia: explicit presentation of the rank, selection of user accounts/profiles to be presented based on rank, and/or sorting the user accounts/profiles to be presented based on the rank. Refer to  FIG. 15  which shows a list of user accounts/profiles primarily sorted by impact with a secondary sort by rank. 
     Profile-user account correspondence, for example as described above inter-alia with reference to  FIG. 8 . It is noted that  FIG. 15  shows user accounts unified to a profile, for example Jeremy Owyang&#39;s profile in section  1502  includes three user accounts. As described above, authenticated correspondence, non-authenticated correspondence but with a probability above a predetermined threshold, and/or correspondence below a predetermined threshold may be outputted to the consumer depending on the embodiment. 
     A list of one or more user accounts/profiles selected and/or sorted based on whether or not the user account/profile played one or more roles and/or based on the extent of the role(s) played as described above inter-alia with reference to  FIG. 5 . For example,  FIG. 16  shows a list of “contributors” relevant to the campaign sorted by the extent of the contributor role. 
     Related Tags—In some embodiments, a list of words and phrases is automatically built such that each word or phrase has a high-frequency in posts/interactions in a specific Scope/Topic, but low-frequency in the entire corpus (containing all collected posts). This measurement is known as frequency—inverse document frequency TF-IDF. For example section  1250  in  FIG. 12  shows a list of related tags. 
     Sentiment Analysis—In some embodiments, sentiment analysis is performed on associated posts and interactions and the number of neutral/positive/negative posts is counted in total and over time. For example graph  1702  in  FIG. 1702  shows the daily sentiments. In some embodiments, the “bag of words” model of natural language understanding is used for sentiment analysis. 
     Current buzz—In some embodiments, recent posts and interactions relevant to the campaign are shown in real time as the posts and interactions occur. 
     Unification of distributed interactions regarding post, user account, and or profile from a plurality of external sources. In some embodiments, all interactions relevant to the campaign are shown even those relating to a plurality of external sources (e.g. a Twitter™ tweet concerning a YouTube video). In some of these embodiments, metrics are displayed with regard to a post, user account, and/or profile regardless of the originating external source (e.g. the number of times the post was shared in a plurality of external sources, the number of followers for a specific user account in a plurality of external sources). 
     Campaign alternative cost calculation. The cost is estimated of funding in a traditional online campaign (e.g. which displays online advertisements, public notices, announcements, etc) equivalent exposure in the external sources that contains the posts. For example, the estimate may in some cases use 3 rd  party tools  182  such as comScore, Compete, Google AdSense API for web sites, published advertising cost for specific demographics in Facebook, number and type of followers in Twitter. The estimated cost for each post of funding equivalent online exposure in the external source, multiplied by the partiality of the post to the defined campaign is summed to calculate how much it would have cost to fund a traditional online campaign that would lead to equivalent exposure with equivalent partiality. 
     It should be evident that the reported analysis results listed above are not binding on the invention. In some embodiments less, more and/or different factors may be analyzed and/or reported than those shown above. 
     One example of a method of calculating the alternative cost will now be described with reference to  FIG. 19 . This example is provided to aid in the understanding of the reader and should not be construed as restricting the invention. 
       FIG. 19  is a flowchart of a method  1900  of calculating an alternative campaign cost, according to some embodiments of the invention. In some embodiments, method  1900  is performed by worker  120 . In some cases, method  1900  may include fewer, more and/or different stages than illustrated in  FIG. 19 , the stages may be executed in a different order than shown in  FIG. 19 , and/or stages that are illustrated as being executed sequentially may be executed in parallel. 
     It is assumed that stage  1004  has already been performed and therefore a subset of posts relevant to the campaign has been selected. In some embodiments, the subset of posts are from a time period defined by consumer  190  and/or by system  110 , for example from a time period beginning with the definition of the campaign and ending with the current date that method  1900  is executed. In other embodiments, the subset of posts may be from any time collected by system  110 . 
     It is also assumed that keywords were entered in stage  1002  at least relating to the brand and/or campaign, and to the competition. 
     In the illustrated embodiments, beginning in stage  1904  with the first post in the subset, in stage  1908  the number of keywords in the post relating to the brand and/or campaign (e.g. included in the first and/or fourth scopes) is counted, n rk     p   , for example by counter  302 . 
     In the illustrated embodiments, in stage  1912  the number of keywords in the post relating to the competition (e.g. included in the second scope) is counted, n ck     p   , for example by counter  302 . 
     In the illustrated embodiments, in stage  1916  the number of keywords in the post relating to any of the scopes (or alternatively relating to brand and/or campaign or relating to competition) is counted, n k     p   , for example by counter  302 . 
     In the illustrated embodiments, in stage  1920 , the sentiment score in the post towards the brand and/or campaign is determined, S p , for example by sentiment analyzer  330 . In some embodiments, the sentiment analysis includes the “bag of words” model of natural language understanding. In some embodiments S p  is a number between 0 and 1. 
     In the illustrated embodiments, beginning in stage  1924  with the first interaction with the post, in stage  1928  the number of keywords in the interaction relating to the brand and/or campaign (e.g. included in the first and/or fourth scopes) is counted, n rk     i   , for example by counter  302 . 
     In the illustrated embodiments in stage  1932  the number of keywords in the interaction relating to the competitions (e.g. included in the second scope) is counted, n ck     i   , for example by counter  302 . 
     In the illustrated embodiments in stage  1936  the number of keywords in the interaction relating to any of the scopes (or alternatively relating to brand and/or campaign or relating to competition) is counted, n k     i   , for example by counter  302 . 
     In stage  1940 , the sentiment score in the interaction towards the brand and/or campaign is determined, S i , for example by sentiment analyzer  330 . In some embodiments, the sentiment analysis includes the “bag of words” model of natural language understanding. In some embodiments S i  is a number between 0 and 1. 
     In  1944 , it is determined if there are any more interactions for the post. If yes (yes to stage  1944 ), then for the next interaction (stage  1948 ), method  1900  iterates to stage  1928 . 
     If there are no more interactions (no to stage  1944 ), then in stage  1950  a partiality score is determined, for example by partiality scorer  350 . In some cases, the partiality score will be higher for posts/interactions whose content is more about the brand/campaign than about the competition and whose sentiment score reflects a positive sentiment toward the brand/campaign. For example, in some embodiments, the partiality score is defined by the following equation 
     
       
         
           
             
               
                 
                   
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     where α is a configurable weight on the competition. 
     In some embodiments if the partiality score calculated in equation 5 is negative (for example due to more weighted keywords related to competitor than keywords related to campaign/brand) then the post is discarded as being irrelevant to the campaign. In other embodiments, the post is retained regardless of whether the partiality score calculated in equation 5 is positive or negative. In these embodiments if the partiality score of a post is negative, then the contribution of the post reduces the alternative cost (see equation 6 below) rather than increases the alternative cost. 
     It should be evident that equation 5 is just one example of an equation for determining a partiality score and that other equations for determining partiality scores may be used instead in some embodiments. 
     It is assumed that in the field of a traditional online campaign, compensation is based on the number of clicks on an advertisement, announcement, public notice, etc. and/or based on the number of thousand impressions. Therefore substitute parameters for number of thousand impressions and/or clicks are determined for the post. 
     In the illustrated embodiments, in stage  1952  the number of impressions [n i     p   ] of the post are counted, for example by traditional campaign cost estimator  390  counting a substitute parameter. Examples of substitute parameters include inter-alia any one of the following:
         number of views, for example available on some blogs, Youtube, Flickr®, etc   number of followers friends multiplied by a constant (e.g. 1), for example available on Twitter™, Facebook, etc   number of monthly unique visitors to the external source website, for example using third party tools  182  such as compete.com or comScore.com. In some cases, the number of monthly unique visitors may be adjusted by the number of days the post was visible on the website.       

     In some embodiments, the number of views is the preferred substitute parameter for the number of impressions, and if the number of views is not available then the number of friends/followers multiplied by a constant is the next preferred substitute parameter. If neither of these parameters is available then the number of monthly unique visitors possibly adjusted for number of days the post was visible, is used. 
     In the illustrated embodiments, in stage  1956 , the number of clicks [n c     p   ] for the post is counted, for example by traditional campaign cost estimator  390  counting a substitute parameter. An example of a substitute parameter includes inter-alia the number of interactions per post by unique interacting user accounts. The number of interactions in some cases may be a worthy substitute for clicks since both show engagement with the post content. 
     In the illustrated embodiments in stage  1960 , the cost per click CPC p  is estimated, for example by traditional campaign cost estimator  390 , for the external source website containing the post. In some embodiments, the cost per click can be determined for any website which displays advertisements, announcements, public notices, etc. (such as blogs, Youtube, Facebook, etc) using third party tools  182  such as Google Adwords API, Google Adwords Keyword tool, Facebook Ad manager, etc. The cost per click is what a payer would pay per click on the advertisement, announcement, public, notice, etc in a traditional online campaign. 
     In the illustrated embodiments in stage  1964 , the cost per one thousand impressions CPM p  is estimated, for example by traditional campaign cost estimator  390 , for the external source website containing the post. In some embodiments, the cost per click can be determined for any website which displays advertisements, announcements, public notices, etc (such as blogs, Youtube, Facebook, etc) using third party tools  182  such as Google Adwords API, Google Adwords Keyword tool, Facebook Ad manager, etc. The cost per one thousand impressions is what a payer would pay per one thousand impressions of the advertisement, announcement, public, notice, etc in a traditional online campaign. 
     In the illustrated embodiments in stage  1968 , it is determined if there are any more remaining posts in the subset. If yes (yes to stage  1968 ), then method  1900  iterates for the next post (stage  1972 ) beginning with stage  1908 . If there are no more posts (no to stage  1968 ), then method  1900  continues with stage  1972 . 
     In the illustrated embodiments in stage  1976 , the alternative cost of the subset of posts is calculated, for example by alternative cost calculator  355 . In some embodiments the alternative cost for each post depends on the partiality score determined above, the cost for all the impression(s), and the cost for all the click(s). Therefore in these embodiments the alternative cost for a post is higher for higher partiality, for higher number of clicks and/or impressions and/or for higher cost per click and/or per impression. In some embodiments the alternative cost for the subset of posts represents the cost of funding a traditional online campaign that would lead to equivalent exposure with equivalent partiality as the subset of posts. For example, an equation for determining alternative cost of all the posts in the subset is provided by equation 6: 
     
       
         
           
             
               
                 
                   
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     It should be evident that equation 6 is just one example of an equation for determining the alternative cost and that other equations for determining alternative costs may be used instead in some embodiments. 
     Method  1900  then ends. 
     It will also be understood that in some embodiments the system or part of the system according to the invention may be a suitably programmed computer. Likewise, some embodiments of the invention contemplate a computer program being readable by a computer for executing a method of the invention. Some embodiments of the invention further contemplate a machine-readable memory tangibly embodying a program of instructions executable by the machine for executing a method of the invention. 
     While the invention has been shown and described with respect to particular embodiments, it is not thus limited. Numerous modifications, changes and improvements within the scope of the invention will now occur to the reader.