WO2015000083A1

WO2015000083A1 - System and method for ranking online content

Info

Publication number: WO2015000083A1
Application number: PCT/CA2014/050646
Authority: WO
Inventors: Nadav Moyse PEREZ; Marc ALOUL
Original assignee: Anysolution, Inc.
Priority date: 2013-07-05
Filing date: 2014-07-07
Publication date: 2015-01-08

Abstract

A system and method for ranking online content from multiple Web platforms is disclosed, as well as to a system and method for presenting the ranked online content on a client device. The Web platforms are crawled to retrieve content data corresponding to content elements, and associated social metric data, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to associated respective content elements. The content data is ranked based on the social metric data by valuing the corresponding intent-driven interaction, to present on the client device at least a portion of the content data, sorted in order of relevance as attributed globally by user activity.

Description

SYSTEM AND METHOD FOR RANKING ONLINE CONTENT

Field of the invention: The present invention relates to a system and method for ranking online content. More particularly, the present invention relates to a system and method for ranking online content from multiple Web platforms, as well as to a system and method for presenting ranked online content on a client device. Background:

Known in the art are various social networking websites, such as Facebook™, Linkedln™, Twitter™, etc. allowing people to share comments and information. Also known in the art are search engines, for example Google Search™ or the like, for searching content on the web. Social networking sites generally offer search engines for searching within some of the content published on the particular social networking site.

Google Search™ executes a search engine optimization (SEO) algorithm. More particularly, Google PageRank™ ranks word search results based on the following criteria: (1 ) references appearing on other sources to or from the content (appropriate but causing a convergence problem) (2) clicks from the search engine to the site (intent unknown); (3) name of website (forcing locking-in to established domain names); and (4) age of Page (the relevance of the age of a page is questionable)..

Thus, Google Search™ ranks based on references from other sources to the content. The function attributing the number of references to and from the content causes a convergence in the information found. Content that has gained initial momentum and popularity or information that is frequently referenced may not be necessarily valuable or relevant information. Google Search™ also ranks search results based on the number of "clicks" which directed a user to a given site. Clicking is an "intent unknown" action, since the user must click to access the site before the user can determine his or her appreciation of the content of the site. In short, Google Search™ reinforces ranking based on how many times a given website was accessed.

In summary, a user will enter a search topic on the Google Search™ page. Google Search™ presents a list of content links using the above-described SEO Algorithm (namely, the number of clicks and references). The user is likely to click on one of the links appearing at the top of the result list in order to access its contents and assess its relevance, viewing part of text, namely the title and the body. Whether the information is valuable or not to the user having accessed the site, the content gains ranking value and tends to stay at the top of the list for the next searcher to search a same or similar topic, to click again unknowingly. Google Search™ does not take into account whether this search result was helpful to the user or not, when presenting results to the next searcher. Thus, the information retrievable on Google Search™ stagnates due to a perpetual clicking momentum. An example of search results obtained from Google Search™ is illustrated in FIG. 6. Furthermore Google PageRank™ increases the value of the link when a user links the content on his/her website, because it is the best he/she found, not necessarily because it is the most relevant. PageRank™ values referrals as links on other pages, but users often link the first website they find on Google Search™ when they look to reference. Psychologically this is known as "herding" behavior, which further causes a convergence in what a user finds.

Facebook™ shows feeds of friends whom a user has more interactivity with on the site, while omitting news feeds from other friends whom the user has less interactivity with and from the rest of the community. Twitter™ prevents a user to create an account without choosing other Twitter™ users to follow and supplies a list of high ranking Tweeters™ to follow. Also, Twitter™ values most recently published Tweets™. Thus Twitter™ posts decay with time and more recent posts are pushed to users, making it difficult for users to locate older posts, which in some cases may be more valuable irrespective of the time factor. This model further discourages users from creating valuable information, as the information is quickly digested and forgotten.

Google Search™ produces stagnant information. Conversely, social media sites have provided a wealth of information, which is however relatively unsearchable since time decay ranking causes information to be disposable.

Also known to the Applicant are United States patents Nos. 8,554,756 B2 (GEMMELL et al.) and 8,606,777 B1 (KRITT et al.); and United States patent applications published under Nos. 2012/0260165 A1 (MASSOULIE et al.); 2012/0296967 A1 (TAO et al.); 2013/0086057 A1 (HARRINGTON et al.); 2013/01 10823 A1 (SU et al.); 2013/0204871 A1 (WONG); 2013/0346404 A1 (BENNETT et al.); 2014/0040243 A1 (RUBINSTEIN et al.); 2014/0040256 A1 (WHITE-SULLIVAN et al.); and 2014/0052718 A1 (WAUPOTITSCH et al.). These also present some drawbacks.

Thus, the current models of online networking and online searches which value information based on intent unknown parameters such as references, frequency of clicking and of viewing, suffers from drawbacks, such as those described above. Ranked results have caused a convergence in what a user sees and who the user "meets" online. Indeed, the current ranking thereby reinforces existing relationships, therefor crowding out new, relevant and/or changing information.

Hence, in light of the aforementioned, there is a need for an improved system which, by virtue of its design and components, would be able to overcome some of the above-discussed prior art concerns. Summary:

The object of the present invention is to provide a system and method which, by virtue of its design and components, satisfies some of the above-mentioned needs and is thus an improvement over other related social networking and search platforms known in the prior art.

More particularly, an object of the present is to connect Internet users to more relevant information across multiple platforms.

In accordance with the present invention, the above mentioned object is achieved, as will be easily understood, by a system and method for ranking online content such as the one briefly described herein and such as the one exemplified in the accompanying drawings.

In accordance with an aspect, there is provided a method for ranking online content to be presented on a client device. The method comprises the steps of: a) crawling online content from multiple Web platforms, by means of a processor, said crawling comprising retrieving content data corresponding to a content element, and associated social metric data, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element;

b) storing, in a database, said content data and social metric data;

c) ranking, by means of a processor, the content data based on the ranking indicia by valuing the corresponding intent-driven interaction, to present on the client device at least a portion of the content data.

The online content may be a post, webpage content, and/or the like. The content data may include a reference (for example a link) to a particular element of online content or its content. Thus, the content data is sorted in order of relevance as attributed globally by user activity (i.e. non-normalized). According to embodiments, above method and/or steps thereof, is repeated periodically in order to frequently update the database.

According to embodiments, the "crawling" of step (a) may be performed by a server system and/or by a client device on which a client application is executed (i.e. "crowd sourcing"), as will be better explained further below. According to embodiments, the "crawling" of step (a) searches for the most popular online content overall, regardless of any particular topics, source, etc. The crawler may also crawl any content it receives during a crowd source, which may be by popularity or just occurrence based on the hybrid search when other platforms' search engines are used.

"Web platforms", in the context of the present, includes websites, web services and/or the like, generally accessible via the Internet. Websites includes static websites, as well as blogs, discussion forums, wikis and/or the like. Web services include social media sites such as Facebook™, Twitter™, Instagram™, Tumblr™, YouTube™, Linkedln™, Google Plus™, Pinterest™, MySpace™.

"Online content", in the context of the present, refers to published content accessible via a communication network, for example the Internet, a private or local network, and/or the like. Online content includes not only conventional web page content, but also RSS feeds, aggregated RSS feeds, social media content, documents, etc.

"Elements" of the online content ("content element"), in the context of the present, refers to individual pieces of content, such as articles, posts, webpages, locations on a webpage, or a webpage content such as a images, photos, video clips, sound clip, etc., which are referable by a link address.

"Intent-driven interaction", in the context of the present, refers to any willful action performed by a user in relation to a given piece of online content, for example "liking", commenting, marking as "favorite", sharing, "disliking", and which can be captured by the Web platform on which the piece of content is published. Thus, the "social metric data" representing intent-driven interaction, may include in some embodiments, the number of "likes" having been made on each piece of content, as well as the number of "comments", of "favorites", of "sharing", and/or of "dislikes" having been made in relation to the particular piece of content (i.e. on a post, webpage, photo, etc.), as will be better explained further below.

According to an embodiment, the "ranking" is performed at a server system, so as to index the database according to the ranking.

In accordance with another aspect, there is provided a system for ranking online content to be presented on a client device, the system comprising:

a crawler, integrated with a processor, for crawling online content from multiple Web platforms, said crawling comprising retrieving content data corresponding to a content element, and associated social metric data, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element;

- a database in communication with the processor for storing said content data and social metric data; and

a ranking module, integrated with the processor, for ranking the content data based on the social metric data by valuing the corresponding intent- driven interaction, to present on the client device at least a portion of the content data, sorted in order of relevance as attributed globally by user activity. In some embodiments, the system further comprises a Rich Site Summary (RSS) aggregator for aggregating feeds from the Web platforms into aggregated content to be crawled by the crawler. Alternatively or additionally, the crawler may be configured to collect at least one of the following information associated to each of said content element: date, timestamp, media type, description, author information, website information and meta data.

In some embodiments, the system may further comprise a parser for parsing words contained in each content element. The processor may be configured to determine words appearing most frequently in the content element, and to store in the storage, said words determined as most frequent and a corresponding word frequency. Alternatively or additionally, the parser may be configured for gathering at least one of: content, meta-content and social metrics dates associated with each of said content element; wherein the processor is configured to calculate post-processed data from information stored in the storage, and for storing, in said storage, the at least one of content, meta-content and social metrics dates, and said post-processed data. According to an embodiment, the above system is operable in a client-server context. More particularly, the above system comprises a server system which includes the search engine. The search engine in turn has embedded therein the crawler, the database, the ranking module and the parser. The server system is accessible from a plurality of client devices, via a communication network (for example the Internet), each client device having a client module executable thereon. The client module may for example be provided by an Internet browser or a dedicated client application which presents a user interface for allowing a user to interact with the above-described system. The server system may include one or more computer device or the like. It is to be understood also that the crawler, database, ranking module, parser and/or search engine may be located on different computers. The client device may be any suitable computer device, including a tablet computer, a smartphone, a pocket computer, a laptop, a conventional computer, and/or the like. The client device comprises a processor or other suitable controller to support the client module. The client device further comprises communication means to communicate with the system, for example via the Internet. According to some embodiments, the above-mentioned crawler, RSS aggregator, ranking module, parser and/or search engine may be located in part or in whole on any of the client devices.

The client device may be located remotely with respect to the above-described system or components thereof, or in some cases, the client device may be connected locally with the system or components thereof.

The database is provided by a computer-readable storage component. In accordance with another aspect, there is provided a processor-readable storage medium having stored thereon instructions for execution by a processor to perform the steps of:

a) crawling online content from multiple Web platforms, by means of the processor, said crawling comprising retrieving content data corresponding to a content element, and associated social metric data, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element;

b) storing, in a database, said content data and social metric data; and c) ranking, by means of the processor, the content data based on the social metric data by valuing the corresponding intent-driven interaction, to present on the client device at least a portion of the content data, sorted in order of relevance as attributed globally by user activity.

In accordance with another aspect, there is provided a method for presenting ranked online content on a client device. The method comprises:

providing in a database:

content data corresponding to content elements;

social metric data associated to each content element, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element; and

ranking information associated to each content element, the ranking information being calculated based on the social metric data by valuing the corresponding intent-driven interaction.

The method further comprises:

receiving a request from the client device;

retrieving from the database, by means of a search engine integrated in a processor, content data associated to the content elements corresponding to the request; and

returning to the client device, the retrieved content data, organized based on the corresponding ranking information in the database, for presentation of the corresponding elements on the client device, sorted in order of relevance as attributed globally by user activity.

In accordance with some embodiments, the retrieving may comprise selecting the content data among a predetermined number of content data having the highest ranking of intent-driven interaction in the database. In accordance with some embodiments, the request may comprise a search parameter. In accordance with some embodiments, the presenting may comprise displaying a display component in a client-application window, for each content element to be presented. Each display component may comprise a tile, the tiles being distributed in a moza^'ic format across a substantial portion of the client- application window. Alternatively or additionally, each display component may comprise a preview of the content element. The preview may comprise at least one of: text portion of the content element, an image portion of the content element and a video portion of the content element. Alternatively or additionally, each display component may comprise at least one: a link to the content element, a title, a source platform associated to the content element, a publishing date or time stamp. Each display component may comprise at least a portion of the social metric data associated with the content element, which may include a number of likes, a number of dislikes, a number of shares, a number of comments, and/or a number of favorites attributed globally by said users. Alternatively or additionally, each display component may comprises one or more user input component, which may comprise: a user input component to like the content element, a user input component to dislike the content element, a user input component to share the content element with other users, a user input component to comment on the content element and/or a user input component to indicate the content element as a favorite.

In accordance with another aspect, there is provided a system for presenting ranked online content on a client device, the system comprising a database and a processor being in communication with the database. The database is configured to store: content data corresponding to content elements; social metric data associated to each content element, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element; and ranking information associated to each content element, the ranking information being calculated based on the social metric data by valuing the corresponding intent-driven interaction. The processor is configured to: receive a request from the client device; retrieve from the database, by means of a search engine integrated in a processor, at least a portion of the content data associated to the content elements corresponding to the request; and return to the client device, the retrieved content data, organized based on the corresponding ranking information in the database, for presentation on the client device of said retrieved content data, sorted in order of relevance as attributed globally by user activity.

In accordance with another aspect, there is provided a processor-readable storage medium for a processor being in communication with a database. The database comprises content data corresponding to content elements, social metric data associated to each content element, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element, and ranking information associated to each content element. The ranking information is calculated based on the social metric data by valuing the corresponding intent-driven interaction. The processor-readable storage medium comprises stored thereon instructions for execution by a processor to perform the steps of:

receiving a request from the client device;

- retrieving from the database, by means of a search engine integrated in a processor, at least a portion of the content data associated to the content elements corresponding to the request;

returning to the client device, the retrieved content data, organized based on the corresponding ranking information in the database, for presenting on the client device said retrieved content data, sorted in order of relevance as attributed globally by user activity.

In accordance with a particular embodiment, there is provided a method for ranking online content to be presented on a client device, the method comprising the steps of: a) crawling online content from multiple Web platforms, by means of a processor, in order to retrieve links which reference elements of the online content, and to retrieve social metric data (i.e. ranking information) associated to each of said elements, the social metric data being representative of intent-driven interaction by users with the corresponding element of online content;

b) storing, in a database, said links and associated social metric data;

c) ranking the links based on the social metric data, by means of a processor, by valuing the corresponding intent-driven interaction, in order to present the corresponding elements of on-line content, sorted in order of relevance as attributed by user activity.

This ranking method thus allows sorting the online content to reflect user- attributed relevance level.

In accordance with another particular embodiment, there is provided a processor- readable storage medium comprising instructions for execution by a processor to perform the steps of the above-described method for ranking online content to be presented on a client device.

In accordance with another particular embodiment, there is provided a system for ranking online content to be presented on a client device, the system comprising:

- a crawler, integrated with a processor, for crawling online content from multiple Web platforms, in order to retrieve links which reference elements of the online content, and to retrieve social metric data associated to each of said elements, the social metric data being representative of intent- driven interaction by users with the corresponding element of online content;

- a database being in communication with the processor, for storing said links and associated social metric data; - a ranking module being in communication with the database, for ranking the links based on the social metric data, by valuing the corresponding intent-driven interaction, in order to present the corresponding elements of on-line content, sorted in order of relevance as attributed by user activity.

According to an embodiment, the system further comprises a parser for parsing the content of the elements associated to the links having been retrieved by the crawler and for storing in the database. According to an embodiment, the system further comprises a search engine for:

- receiving a request from a client device;

- retrieving, from the database, links associated to the elements of the online content corresponding to the request;

- returning to the client device, the retrieved links, organized based on the ranking information in the database, for presentation of the corresponding elements on the client device, sorted in order of relevance as attributed by user activity.

In accordance with yet another embodiment, there is provided a method for presenting online content on a client device, the online content being referenced in a database storing links which reference elements of online content, and further sorting ranking information for each link representing a level of intent- driven interaction by users with the corresponding element of online content, the method comprising the steps of:

a) receiving a request from a client device;

b) retrieving, from the database, links associated to the elements of the online content corresponding to the request;

c) returning to the client device, the retrieved links, organized based on the ranking information in the database, for presentation of the corresponding elements on the client device, sorted in order of relevance as attributed by user activity. According to embodiments, the ranking information in the database includes social metric data such as the number of "likes", "comments", "shares", "favorites", "dislikes", and/or the like having been performed in relation to the particular element of content. According to some embodiments, the ranking information further includes a ranking value calculated based on the social metric data for each link, and stored in the database. In alternate embodiments, the ranking value is calculated dynamically upon receiving a request from a client device.

According to embodiments, the "request" generates a query which may have parameters, for example filtering parameters.

According to embodiments, the "presentation" of step (c) in the above-defined method, comprises displaying the elements on a display screen of a client computer. It is to be understood that the elements of online content may be presented on the client device in any suitable manner. For example, an element may be graphically represented to show all or a portion of its content. As another example, an element may be presented by a hyperlink which may be graphically displayed as text, an image, an icon, etc.

According to embodiments, the above method for presenting online content on a client device is performed in the context of a client-server system, where the database is located within the server system. It is to be understood that some or all of the steps may be performed at the server system and/or on the client device.

In accordance with another embodiment, there is provided a processor-readable storage medium comprising instructions for execution by a processor to perform the steps of the above-described method for presenting online content on a client device. In accordance with yet another embodiment, there is provided a system for presenting online content on a client device, the system being in communication with a database storing links which reference elements of online content and further sorting ranking information for each link, the ranking information representing a level of intent-driven interaction by users with the corresponding element of online content, the system comprising:

- an input module for receiving a request from a client device;

- a search engine being in communication with the input module and the database for retrieving from the database, links associated to the elements of the online content corresponding to the request;

- a calculator being in communication with the search engine for returning to the client device, the retrieved links, organized based at least on the ranking information in the database, for presentation of the corresponding elements on the client device, sorted in order of relevance as attributed by user activity.

Advantageously, users are willfully and organically upvoting content and building the ranklist, allowing the users to reach a consensus on valuable and most relevant content. The consensus is reflected by the above-described system and method in accordance with global user activity (non-normalized). Another advantage concerns the continuous or periodic re-ranking which better reflect trends in contrast to absolute ranking. Other objects, advantages and features of the present invention will become more apparent upon reading of the following non-restrictive description of preferred embodiments thereof, given for the purpose of exemplification only, with reference to the accompanying drawings. Brief description of the drawings: FIG. 1 is a diagram showing a system according to an embodiment of the present invention.

FIG. 2 is a schematic diagram showing relationships between words, links and posts.

FIG. 3 is a flow chart of a method of ranking and of presenting online content according to an embodiment of the present invention. FIG. 4 is a screen shot of a user interface screen of a client application, according to an embodiment of the present invention, the user interface screen showing a default home page of the client application.

FIG. 5 is another screen shot of the user interface screen of the client application, according to an embodiment of the present invention, the user interface screen showing a search results.

FIG. 6 is a screen shot, showing search results obtained from a search engine, in accordance with prior art.

FIG. 7A to 7C are screen shots of the user interface screen of the client application, according to an embodiment of the present invention, showing a list of authors sorted by total aggregate shares. FIG. 8A to 8C are screen shots of consecutive screen portions of a window showing the articles referenced via hyper link of a particular list item shown in FIG. 7A.

FIG. 9A to 9C are screen shots of the user interface screen of the client application, according to an embodiment of the present invention, showing a list of sites sorted by total aggregate shares. Detailed description of embodiments:

In the following description, the same numerical references refer to similar elements. The embodiments mentioned and/or geometrical configurations and dimensions shown in the figures or described in the present description are embodiments of the present invention only, given for exemplification purposes only.

Broadly described, the system and method according to an embodiment of the present invention, as exemplified in the accompanying drawings, provides a public forum which bridges the gap between news and social media platforms, where information (words, links, posts, for example) is valued based on their interconnections on the dimension of social metrics (namely willful & known social metrics, such as likes, shares, comments, favorites and dislikes, for example), by continuously crawling and crowd sourcing online content. Thus, the online content is presented on a webpage, sorted in order of relevance based on social activity and trends.

According to an embodiment, as better illustrated in FIG. 1 , there is provided a system 110 for ranking online content 112 to be presented on a client device 114. The system 110 is provided by a server system 110 within a client-server configuration. The server system 110 comprises a processor 116 which embeds a search engine 128 having the following modules: a crawler 120, a Rich Site Summary (RSS) aggregator 122, a gathering module 121 , a ranking module 124, and a parser 126. The server system 110 further comprises a database 130. The server 110 is in communication with multiple Web platforms 131 (for example websites or web services such as Facebook™, Twitter™, etc.), via a communication network 132, namely the Internet 134. Web platforms 131 publish online content 112, which includes content elements 136, each being referable by a link 138, for example a uniform resource locator (URL) or the like. A content element 136 may include a post, a webpage, a portion of a webpage, an image, or the like. A post may in turn be published in the form of text, image, video, image title, video title and/or a combination thereof. A "post" is referable by a link. A link may refer to an element (for example a webpage) containing one or more posts. Furthermore, words have posts and links associated with them. Posts contain links and words, and links may be associated with words, and be contained within a post, or commented upon by a post. Thus, "posts" 140, "links" 138 and "words" 142 are all interconnected, as represented in FIG. 2.

Referring back to FIG 1 , and with further reference to FIG. 3, which represents steps of the method 200, the RSS aggregator 122 aggregates RSS feeds from multiple Web platforms 131 into aggregated content. The crawler 120 crawls (at 210) online content from websites 131 , namely application programming interface (API) of social media platforms considered to be the most accessed. The crawler further crawls (at 212) content from the aggregated content produced by the aggregator.

The crawler 120 indexes the elements (i.e. posts 140, etc.) 136 from the online content 112 (i.e. from the websites 131 and aggregated content). More particularly, the crawler 120 retrieves the links 138 associated to the elements 136 for storage in the database 130.

For each retrieved link 138, the crawler further gathers the associated publishing date/timestamp and media type (video, photo, post, etc.), which are stored in the database 130 via the search engine 128. The attributes stored in the database 130 include, but are not limited to the following fields: unique id, date, link, title, description, author information, site information, social metrics, meta data (ranging from geography to item categories, for example), media type information.

The parser 126 further parses the words 142 contained in each indexed element 136 and stores in the database 130, via the search engine 128, the words 144 appearing most frequently in the element 136. More particularly, the parser 126 gathers content, meta-content, word frequencies, social metrics dates, and calculates post-processed data, such as via natural language processing (NLP), and stores the gathered and calculated data into the indexed database, to allow the search engine 128 to subsequently access it. This information is thus useful for subsequent keyword searches. Each word 144 is further attributed a ranking value as will be explained further below. The social metrics data is re-evaluated substantially continuously and stored in a historic database in order to update the links' absolute ranking.

The above-mentioned natural language processing (NLP) is a method of analyzing groups of words and letters within a particular string and pulling out data associated with the relation between those string elements. In particular, it allows a program to extract meaning out of a phrase by way of examining interaction between, and context of, string subsections. Natural language processing also creates synsets, or groups of semantically equivalent data elements which can freely replace each other within a specific statement without changing its value. WordNet™ provides a vast compilation of synsets and relationships between synsets in the English language which allows phrase elements to be evaluated with respect to each other in order to derive a machine readable meaning from human language. It also includes a hierarchy of synsets i.e. kidney is an organ is a body part is a body etc. NLP also allows determination of moods and intents of sentences. For example, "I love lamp" means the user "likes" lamp, and "I hate lamp" means the user "dislikes" lamp. In the present embodiment, words contained in comments are read using WordNet™ NLP in order to determine whether a comment represents a positive or negative appreciation, as will be better explained further below. The above operations are executed continuously or at predetermined times.

Ranking information

Still referring to FIG. 1 and 3, the system 100 retrieves data 146, which may include metadata, such as social metric data 148 associated to each of the elements 136 retrieved, and which is representative of intent-driven interaction by users with the corresponding content element, i.e. willful action performed by a user in relation to a given piece of online content. This social metric data 148 forms part of the ranking information 150 stored in the database 130 that will be useful for ranking the elements 136 of online content 112. More particularly, the crawler 120 gathers "internal" social metrics, as will be better explained below, and the gathering module 121 gathers "external" social metric, as will be better explained below, as well as post-process NLP data 153. User interactions

Such intent-driven interactions includes "likes", "dislikes", "favorites", "shares", and "comments" in relation to content elements (i.e. posts, webpages, photos, videos, etc.). A "like" action corresponds to a user marking the content element to express a positive appreciation of the content element, while a "dislike" action reflects a negative appreciation of the content element. A "favorite" action generally corresponds to a user bookmarking the element in his/her list of favorite elements in order to easily and quickly return to this webpage, post, video, etc. in the future. A "comment" action corresponds to a user inputting a comment in response to a particular post, webpage, photo, or the like, which reflects a level of interest in the piece of content by the user, whether positive or negative. A "share" action corresponds to a user having referenced the element for other users to access it, generally showing a level of interest and appreciation by the user. Ranking information 150 may also include how many times an online element has been "viewed" or "clicked". "Clicks" represent a selection, whereas a "view" may come from a redirect, a site looked up in history, a URL or IP accessed directly, etc. A "viewing" or "clicking" action is generally referred to as an intent- unknown metric, but may further be included as part of the ranking information 150.

Sources of ranking information

Any of the above user interactions may be sourced from within a given web platform, referred to herein as "internal" ranking information, i.e. on-site biased ranking. For example, a video published on Youtube™ may be "liked", "disliked", "shared", etc. within the Youtube™ site. Another example is Reddit's vote count which provides an internal ranking (i.e. internal ranking information) of externally published content.

An intent-driven interaction may also be made from one web platform to another web platform, referred to herein as "external" ranking information, i.e. from a web platform which is external to the source web platform. For example, a Youtube video which is published on Facebook™ may be "liked", "disliked", "shared", etc. from Facebook™.

An intent-driven interaction may also be made within a web platform associated to the system described herein, namely on Worklink™, referred to herein as a "Wordlink" ranking information. For example, a user may comment on a newsfeed element viewed on Wordlink™. Another type of ranking information that may be used includes Google™ Search Engine Optimization (SEO) ranking, which generally centers around the value of links to and from each specific page. In the particular embodiment described herein and illustrated at FIG. 1 , the following metrics 148 (or ranking information 150) are collected, for each content element 136, during the crawling step:

- the number of "likes", of "dislikes", of "comments", of "favorites", of "shares" sourced externally, e.g. above -described "external ranking information";

- the number of "likes", of "dislikes", of "comments", of "favorites", of "shares", of "views" sourced internally, e.g. above-described "internal ranking information";

- the number of "likes", of "dislikes", of "comments", of "favorites", of "shares" sourced locally, e.g. above -described "Wordlink™ ranking information"; and

- the number of Google Search™ results (i.e. Google Search™'s ranking value). The crawler 120 also retrieves the content 149 of each content element 136, for post-processed data 153 (i.e. reads the text and understands the context to determine whether people liked or disliked the content in words) as well as for other analysis on the context of the words through NLP, as previously mentioned. The crawler 120 further retrieves metadata 151 relative to the content element 136.

The database 130 in the server system 110 is in communication with the above- mentioned processor 116 and stores therein, via the search engine 128, the links 138, parsed words 144 and the above-listed social metric data 148 having been collected by the crawler 120, as well as the NLP post processed data 153. Each link 138 is stored in a record within a table 152 of the database 130. For each record an identifier ("link id") is generated and stored in the table 152. A series of fields corresponding to the above-listed ranking information 150 is further associated to each record. The ranking module 124 in the server system 110 communicates with the database 130, via the search engine 128, for ranking the links based on the collected social metric data 148, by valuing the corresponding intent-driven interaction. More particularly, the ranking module 124 calculates a value in accordance with the following function:

rank value_{Word L} i_{nk Post}

function of

where AFWLP is an adjustment factor for the cross relationships between words, links and post ranks and a_Wi_P is a corresponding attenuation constant; where f_externai corresponds to the external ranking information, i.e. number of "Likes", "Dislikes", "Comments", "Favorites" and "Shares" sourced from an external platform, each multiplied by attenuation constants a_ext, b_ext, c_ext, d_ext and e_ext, respectively; where internal corresponds to the internal ranking information, i.e. number of "Likes", "Dislikes", "Comments", "Favorites", "Shares" and "Views" sourced within a same platform, each multiplied by attenuation constants a_int, b_int, c_int, d_int, e_int and fint, respectively; where f_wi corresponds to the Wordlink™ ranking information, i.e. number of "Likes", "Dislikes", "Comments", "Favorites" and "Shares" sourced in Wordlink™, each multiplied by attenuation constants a_wi, b_wi, Cwi, d_wi, e_wi and f_wi, respectively; where fGoogiePageRank corresponds to the Google™ ranking derived from its search engine; where the attenuation constants are chosen empirically, generally to the following rule based on action engagement:

(a | b | z) < c < d < e = 1 ; where k, h, i, j are attenuation constants; where AF_ext, AF_int, AF_Wi and AF_GoogiePageRank are adjustment factors for cross relationships between the different platform rankings (internal, external, Wordlink™ and SEO); where AF_word, AF|_ink and AF_post are adjustment factors for the cross relationships between words,, links and posts; and where TEST encapsulates other factors we may test in the ranking formula, such as source site ranking for normalized ranking, for example. The attenuation constants a, b, c, d, e and z provide a prioritization based on the user actions. Indeed certain user interactions may be considered more valuable than others for the purpose of ranking. Namely a "like", "dislike" and "view" are attributed a lower value in comparison to a "comment" which may show a greater level of interest than a "like" or "dislike". Similarly, a "share" may be attributed a greater value for the purpose of ranking in comparison a "comment", since sharing may show a greater level of interest and appreciation, and thus relevance, than a "comment". In some embodiments, a "view" which is generally intent-unknown may be attributed an even lower value than any of the above actions. It is to be understood that, according to alternate embodiments, any of the above attenuation constants may be set to 0 (in order to avoid taking a particular action into consideration) or to less than 0, in order to devalue an action. As an example, a "dislike" may be valued as -1 , in some embodiments. Thus the attenuation constants are chosen according to a pre-determined prioritization of actions. The attenuation constants may be modified in time based on empirical data collected by the system.

The attenuation constants k, h, i and j provides a prioritization based on the source of the actions. Namely, external ranking information is ranked higher than internal ranking information, which in turn is ranked higher than Wordlink™ ranking information, which in turn is ranked higher than Google™ SEO ranking information. In the present embodiment, the Google™ SEO ranking information is not taken into consideration (j = 0). In order to take the Google™ SEO ranking information into consideration, the value of attenuation constant j would be different from 0. AFext, AFint, AF_Wi and AF_GoogieSEo are adjustment factors for cross relationships between links and posts. The adjustment factors periodically update the rank value of a link or a post with newly recorded rank values attributed to associated posts and links. More particularly, the update is performed to adjust the rank value of a link based on newly and periodically calculated rank value of the post(s) associated with the link, and conversely to adjust the rank value of a post based on newly and periodically calculated rank value of the link(s) referencing the post. Periodic updates prevent causing an implicit function or infinite feedback loop between the two functions in a continuous update. As an example, if a link value increases by 10, and its associated post value increases by 10, which then causes the link value to increase by 10, the value of both link and post will tend towards infinity. Instead, the 'delta link' is periodically adjusted via the change in AFx (i.e. AF_ext, AF_int, AF_Wi or AF_GoogieSEo), and thus the reciprocally related variables tend toward a finite number.

It is to be understood that the attenuation constants and adjustment factors may be modified, and may further be re-evaluated, dynamically or manually, based on the collected information, as may be readily understood by the person skilled in the art. Indeed, the attenuation constants and adjustment factors are intended to be attenuated empirically. The resulting ranking value, for a given online element 136, is then stored in the table 152, via search engine 128, in association with the corresponding record of the table 152. The rankings are subsequently data-mined.

Each word 144 associated to an online element 136 in the database 130 inherits the rank value of the element 136 in which it appears.

User interface

Referring now to FIG. 4, with further reference to FIGs. 1 and 3, the operation of the system 100 will be described in terms of user operation. Upon accessing the Wordlink™ platform 100 via a client application 160 (web browser or a dedication local application, for example) from the client computer 114, the client application 160 presents a user interface 162, as exemplified in FIG. 4. The user may have an account with the system and log-in, or alternatively, the user may access the system 100 as a non-identified user. Upon accessing the platform 100, the client application 160 retrieves (at 216), via a search engine 164 and the search engine 128, default content elements (news posts, etc.) 136, corresponding to the records from the database 130 having been ranked as most recent (or according to another preference depending on user settings), and presents (at 220) the content elements 136 on the Wordlink™ home page 166, sorted at least in accordance with the ranking value stored in the database 150, i.e. according to default ranking metrics.

In addition, the user interface 162 presents (at 218) a word cloud 168, showing the most popular and frequent words 170 appearing among the most popular content elements 136. Also the size of each word 170 displayed is proportional to its frequency and the popularity of its parent content.

In addition, the client application 160 presents user interface components 172 for performing a search, as better seen in FIG. 4. A search field 174 allows a user to perform a keyword search. A first filter button 176 is provided for limiting the search (232) within a particular time period (at 240), and a second filter button

178 is provided for limiting the search (232) to one or more media types, i.e. press, blog, video, music (at 238). In additional a third button 180 allows a user to sort (230) the search results in terms of popularity, i.e. from highest ranking in the database 130 to lowest ranking, or vice versa (at 234) or based on time, i.e. from most recent to least recent or vice versa (at 236).

It is to be understood that any number of suitable filters, search options and/or result display options (such as sorting) may be provided, according to alternate embodiments.

The "word cloud" 168 provides another search component 172. Indeed, each word 170 displayed in the word cloud is a hyperlink which, when activated, prompts a keyword search corresponding to the particular word having been activated. In addition to user-input search criteria, other search criteria may be taken into consideration when a search is launched, for example, the geographical location of the client device, the user's gender or age group (some of which may require user login in order to retrieve information provided by the user in his/her account)

A user may thus enter a keyword and/or other search criteria (at 216), in order to obtain published content corresponding to the search criteria. Upon launching a search (at 226), the search engine 164 locates elements (posts, links, etc.) from different Web platforms 131 which correspond to the search criteria, and uses a crawler 165 on the client application 160 to crawl (at 228) the located elements, similarly to what was previously described for the server 110, for a limited amount of time (for example 7 seconds). In other words, the search engine "queries" these other platforms communicating/using their own internal search engine to find more data related to that word search. Therefore when a user searches a word, the system 100 provides a hybrid search engine. Thus, the system searches the data stored in the database 130, and further solicits search engines from other the platforms such as Twitter™, Instagram™, Tumblr™, Youtube™, Soundcloud™, etc... to further enhance the search results.

The crawler 165 retrieves links, and the associated ranking information 150 and a parser 163 further parses the words, all of which is then stored into the database 130 of the server system 110, via the search engine 128, in order to update the ranking information 150 of links which are already stored in the table 152, as well as to add new records for any link which was not yet entered in the table 152. The ranking module 124 calculates the ranking value for any changed or newly added record (at 214). The client's search engine 164 then queries (at 216) the database 130, via the server side search engine 128, based on the search criteria having been entered at 226. The server 110 returns the corresponding links and sorts them in accordance with the ranking value stored in the table 152, as well as based on any other additional sorting criteria the user may have entered at the client application 160 (for example, sort preferences 220, and filter 222), in order to present (at 220) the corresponding elements 136 of on-line content, sorted in order of relevance as attributed by user activity. An example search results 182, based on the search term "cancer", is illustrated in FIG. 4. When displaying search results, the client application 136 generates a new word cloud 168 (at 218) focused on the search criteria and presents the search results, i.e. the content elements 136, in the form of link content tiles 184 (at 222). Each link content tile 184 displays at least a portion of the content of the element 136. Each tile 184 is a hyperlink which links to the source of the content element 136. Each tile 184 further present summary information (name, the source platform 186, the number of "shares" 188 and publishing time/date 190) with respect to the content element 136, as well as buttons 192 allowing users to "like", "dislike", "share", "favorite" or "comment", the content element 136 within the Wordlink™ platform (at 224). Any user activity at 224 is further stored in the database 130, in association with the corresponding element 136.

A prior art example of search results obtained in Google Search™ for the search term "cancer" is depicted in FIG. 6.

With reference to FIG. 7A to 9C, it is possible to display a list 300 of authors or a list of sites 310 sorted by total aggregate shares having been made relative to the content produced by each author or site within a period of time (for example weekly or daily).

FIG. 7A to 7C are screen shots showing consecutive screen portions of a list 300 of authors sorted by total aggregate shares within a given week. As can be seen, each item of the list 300 contains an author 302 displayed together with an associated logo 304, as well as the number 306 of aggregate shares having been performed on articles of this author within the last week, and the number 308 of concerned articles (shared articles). Each item of the list 300 contains a hyperlink leading to a result page 320 showing the concerned articles 322 sorted based on highest numbers of shares, as illustrated in FIG. 8A, 8B, 8C which are screen shots of consecutive screen portions of a window showing the articles 322 of @TodayShow from FIG. 7A.

FIG. 9A to 9C are screen shots showing consecutive screen portions of a list 310 of sites sorted by total aggregate shares within a given week. As can be seen, each item of the list 310 contains a site 312 displayed together with an associated logo 314, as well as the number 316 of aggregate shares having been performed on articles of the particular site within the last week, and the number 318 of concerned articles (shared articles). Each item of the list 310 is a hyperlink leading to a result page showing the concerned articles sorted based on highest numbers of shares.

It is to be understood that the list may extend to include all authors/sites or it may be truncated, depending on particular embodiments.

Crowd Sourcing

As explained in the above-described scenario, each search performed at the client application 160, updates the database 130 (at 214) and more particularly, the ranking information 150 and associated ranking values, to maximize information gathering (considering API source IP usage restrictions) and further promoting organic content base. By clicking on words of the word cloud and running searches (at 226), users collaborate to asynchronously gather organically driven content from the social media platforms.

Crowd sourcing is particularly useful, in this embodiment, because of high volume on social media platforms (for example, Twitter™ reaches about 500 Million tweets in one day), as well as limitations in the number of inquiries that can be made from a given device. Indeed, most Web platforms limit the number of inquiries sourced from a particular IP address. Thus the system 100 crawls the most popular content and further gathers additional ranking and indexing information, through crowd sourcing, via user searches or actions performed from client devices 114, as previously described. In addition, upon initially launching the client application 160, the client crawler 165 performs a first crawling operation using the client devices Internet Protocol (IP) address. More particularly, a probability function is executed to obtain a net aggregate of what users have searched for from the system 100 (i.e. on the Wordlink™ site) and to automatically crowd source content associated to those specific search terms, as a priority. This operation may be prompted by the system server 110 or it may run independently from the client application 160. The information collected by the client device 114, i.e. links and ranking information, is forwarded to the search engine 128 of the server system 110, which stores the collected data in the database 130, and further ranks the links in accordance with the ranking operation described above (at 214).

In an alternate embodiment, the probability function is a known or experimental probability function, for example, in order to gather content related to words for users that may not be registered on the system, but which are targeted to access the system.

Several modifications could be made to the above-described system and method, without departing from the scope of the present invention. Indeed and for example, according to an alternate embodiment of the present, the ranking value may be calculated dynamically upon initiating a search request, without departing from the scope of the present invention as can be easily understood by a person skilled in the art. In one variation, users could be allowed to curate their own database of links sourced off of the platform, of which the value of the curated list would be assessed as a link as per the above ranking value formula. In another variation, content producers could receive analytic information on their users' interaction with their offerings. In additions, although the preferred embodiment of the present invention as illustrated in the accompanying drawings is directed to a public forum, it is to be understood that the system and method described herein may be adapted to particular communities, interest groups, organizations, etc. One example would be providing a service to determine the relative magnitude of specific conversations in a given political cycle. Another would be a portal for organizations to monitor its social media impact and terms that it has associated itself with. Another example involves an editor monitoring his magazine daily or for a certain word, chronologically or by popularity. Yet another example could be an author monitoring himself or his competitors. Yet another possible use is to provide a space on the site for scientific research content to gain social involvement. In additions, although the preferred embodiment of the present invention as illustrated in the accompanying drawings comprises components such as a crawler, a ranking module, as well as user interface elements such as link content tiles, word clouds, etc., and although the preferred embodiment of the above described system/method and corresponding parts thereof consists of certain configurations and steps as explained and illustrated herein, not all of these components, configurations and steps are essential to the invention and thus should not be taken in their restrictive sense, i.e. should not be taken as to limit the scope of the present invention. It is to be understood, as also apparent to a person skilled in the art, that other suitable components and cooperations thereinbetween, as well as other suitable configurations and architectures may be used for the system and method according to the present invention, as briefly explained herein and as can be easily inferred herefrom, by a person skilled in the art, without departing from the scope of the invention. Moreover, the order of the steps provided herein should not be taken as to limit the scope of the invention, as the sequence of the steps may vary in a number of ways, without affecting the scope or working of the invention, as can also be understood. Moreover, according to embodiments of the present invention, components or devices additional to those described herein, may be incorporated with the above-described system and/or components thereof, without departing from the scope of the invention, as can be understood by a person skilled in the art.

The above-described embodiments are considered in all respect only as illustrative and not restrictive, and the present application is intended to cover any adaptations or variations thereof, as apparent to a person skilled in the art. Of course, numerous other modifications could be made to the above-described embodiments without departing from the scope of the invention, as apparent to a person skilled in the art.

Claims

Claims:

1 . A method for ranking online content to be presented on a client device, the method comprising the steps of:

a) crawling online content from multiple Web platforms, by means of a processor, said crawling comprising retrieving content data corresponding to a content element, and associated social metric data, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element;

b) storing, in a database, said content data and social metric data;

c) ranking, by means of a processor, the content data based on the social metric data by valuing the corresponding intent-driven interaction, to present on the client device at least a portion of the content data, sorted in order of relevance as attributed globally by user activity.

2. A method according to claim 1 , wherein said content element comprises at least one of a post and webpage content.

3. A method according to claim 1 or 2, wherein the content data of the crawling step (a) is sourced from application programming interfaces (API) of social media platforms.

4. A method according to any one of claims 1 to 3, further comprising:

aggregating Rich Site Summary (RSS) feeds from multiple Web platforms into aggregated content,

wherein said online content crawled in step (a) comprises the aggregated content.

5. A method according to any one of claims 1 to 4, wherein the content data of step (a) comprises a reference to the associated content element.

6. A method according to any one of claims 1 to 5, wherein the crawling step further comprises:

collecting at least one of the following information associated to each of said content element: date, timestamp, media type, description, author information, website information and meta data.

7. A method according to any one of claims 1 to 6, further comprising:

parsing, by means of a parser, words contained in each content element; and

determining words appearing most frequently in the content element; and

storing in the storage, said words determined as most frequent and a corresponding word frequency.

8. A method according to claim 7, further comprising:

gathering at least one of content, meta-content and social metrics dates associated with each of said content element;

calculating post-processed data from information stored in the storage; and

storing, in the storage, said at least one of content, meta-content and social metrics dates, and said post-processed data.

9. A method according to claim 8, wherein the calculating of the post- processed data is made via natural language processing (NLP).

10. A method according to any one of claims 1 to 9, wherein said social metric data comprises:

user input data representing a user liking the content element; user input data representing a user marking the content element as a favorite; user input data representing a user sharing said content element with other users;

user input data representing a comment entered in relation to said content element; and

user input data representing a user disliking the content element.

1 1 . A method according to any one of claims 1 to 10, wherein the social metric data retrieved via the crawling step (a) comprises at least one of:

internal social metric data representing a portion of said social metric data having been generated within the Web platform from which the associated content element is sourced;

external social metric data representing a portion of said social metric data having been generated externally to the Web platform on which the associated content element is sourced; and

local social metric data representing a portion of said social metric data having been generated within the host media feed platform.

12. A method according to claim 1 1 , wherein the social metric data provides ranking information and wherein the crawling step (a) further comprises gathering additional ranking information comprising at least one of:

a number of times a content element has been viewed; and

a number of times a content element has been accessed via a hyperlink.

13. A method according to any one of claims 1 to 12, further comprising:

presenting on the client device said at least a portion of the content data, sorted in accordance with said ranking step (c).

14. A method according to claim 13, wherein said at least a portion of the content data presented is selected among a predetermined number of content data being having the highest ranking of intent-driven interaction in the database.

15. A method according to claim 13, further comprising:

receiving a user request, via the user device;

wherein said at least a portion of the content data presented is selected based on the user request.

16. A method according to claim 15, wherein the user request comprises a search parameter.

17. A method according to any one of claims 13 to 16, wherein the presenting comprises displaying a display component in a client-application window, for each content element to be presented.

18. A method according to claim 17, wherein each display component comprises a tile, the tiles being distributed in a moza^'ic format across a substantial portion of the client-application window.

19. A method according to claim 17 or 18, wherein each display component comprises a preview of the corresponding content element.

20. A method according to claim 18, wherein the preview comprises at least one of: text portion of the content element, an image portion of the content element and a video portion of the content element.

21 . A method according to any one of claims 17 to 20, wherein each display component comprises at least one: a link to the content element, a title, a source platform associated to the content element, and a publishing date or time stamp.

22. A method according to any one of claims 17 to 21 , wherein each display component comprises at least a portion of the social metric data associated with the content element.

23. A method according to claim 22, wherein said at least a portion of the social metric data comprises at least one of: a number of likes, a number of dislikes, a number of shares, a number of comments, and a number of favorites attributed globally by said users.

24. A method according to any one of claims 17 to 23, wherein each display component comprises one or more user input component.

25. A method according to claim 24, wherein said user input component comprises at least one of: a user input component to like the content element, a user input component to dislike the content element, a user input component to share the content element with other users, a user input component to comment on the content element and a user input component to indicate the content element as a favorite.

26. A system for ranking online content to be presented on a client device, the system comprising:

a database in communication with the processor for storing said content data and social metric data; and

a ranking module, integrated with the processor, for ranking the content data based on the social metric data by valuing the corresponding intent-driven interaction, to present on the client device at least a portion of the content data, sorted in order of relevance as attributed globally by user activity.

27. A system according to claim 26, further comprising a Rich Site Summary (RSS) aggregator for aggregating feeds from the Web platforms into aggregated content to be crawled by the crawler.

A system according to claim 26 or 27, wherein the crawler is configured to collect at least one of the following information associated to each of said content element: date, timestamp, media type, description, author information, website information and meta data.

A system according to any one of claims 26 to 28, further comprising a parser for parsing words contained in each content element; wherein the processor is configured to determine words appearing most frequently in the content element, and to store in the storage, said words determined as most frequent and a corresponding word frequency.

A system according to claim 29, wherein the parser is further configured for gathering at least one of: content, meta-content and social metrics dates associated with each of said content element; wherein the processor is configured to calculate post-processed data from information stored in the storage, and for storing, in said storage, the at least one of content, meta- content and social metrics dates, and said post-processed data.

A processor-readable storage medium having stored thereon instructions for execution by a processor to perform the steps of:

32. A method for presenting ranked online content on a client device, the method comprising:

providing in a database:

content data corresponding to content elements;

ranking information associated to each content element, the ranking information being calculated based on the social metric data by valuing the corresponding intent-driven interaction;

receiving a request from the client device;

retrieving from the database, by means of a search engine integrated in a processor, at least a portion of the content data associated to the content elements corresponding to the request;

returning to the client device, the retrieved content data, organized based on the corresponding ranking information in the database for presenting on the client device, said retrieved content data, sorted in order of relevance as attributed globally by user activity.

33. A method according to claim 32, wherein said retrieving comprises selecting the content data among a predetermined number of content data having the highest ranking of intent-driven interaction in the database.

34. A method according to claim 32 or 33, wherein the request comprises a search parameter.

35. A method according to any one of claims 32 to 34, further comprising presenting of the retrieved content data, said presenting comprising displaying a display component in a client-application window, for each content element to be presented.

36. A method according to claim 35, wherein each display component comprises a tile, the tiles being distributed in a moza^'ic format across a substantial portion of the client-application window.

37. A method according to claim 35 or 36, wherein each display component comprises a preview of the content element.

38. A method according to claim 37, wherein the preview comprises at least one of: text portion of the content element, an image portion of the content element and a video portion of the content element.

39. A method according to any one of claims 35 to 38, wherein each display component comprises at least one: a link to the content element, a title, a source platform associated to the content element, a publishing date or time stamp.

40. A method according to any one of claims 35 to 39, wherein each display component comprises at least a portion of the social metric data associated with the content element.

41 . A method according to claim 40, wherein said at least a portion of the social metric data comprises at least one of: a number of likes, a number of dislikes, a number of shares, a number of comments, and a number of favorites attributed globally by said users.

42. A method according to any one of claims 35 to 41 , wherein each display component comprises one or more user input component. A method according to claim 42, wherein said user input component comprises at least one of: a user input component to like the content element, a user input component to dislike the content element, a user input component to share the content element with other users, a user input component to comment on the content element and a user input component to indicate the content element as a favorite.

A system for presenting ranked online content on a client device, the system comprising:

a database for storing:

content data corresponding to content elements;

ranking information associated to each content element, the ranking information being calculated based on the social metric data by valuing the corresponding intent-driven interaction

a processor for:

receiving a request from the client device;

retrieving from the database, by means of a search engine integrated in a processor, at least a portion of the content data associated to the content elements corresponding to the request; and

returning to the client device, the retrieved content data, organized based on the corresponding ranking information in the database, for presentation on the client device, of said retrieved content data, sorted in order of relevance as attributed globally by user activity. A processor-readable storage medium for a processor being in communication with a database comprising content data corresponding to content elements, social metric data associated to each content element, the social metric data being representative of intent-driven interaction made globally by the community of users with respect to the associated content element, and ranking information associated to each content element, the ranking information being calculated based on the social metric data by valuing the corresponding intent-driven interaction, the processor-readable storage medium having stored thereon instructions for execution by a processor to perform the steps of:

receiving a request from the client device;