Method and system for content bias detection

The present teaching relates to a method, system, and programming for providing content. A plurality of content items and publication information related thereto are obtained. For each of the plurality of content items, one or more topics are determined in accordance with a model. The related publication information associated with each content item is analyzed to identify at least one source of a plurality of sources that published the content item. A distribution is generated of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias of a source with respect to publishing content is identified based on the distributions of the plurality of content items.

BACKGROUND

1. Technical Field

The present teaching generally relates to data processing. More specifically, the present teaching relates to identifying content sources that are consistently biased regarding certain content topics and identifying content topics which certain content sources avoid covering.

2. Technical Background

The Internet has made it possible for a person to electronically access virtually any content at any time and from any location. The Internet technology facilitates information publishing, information sharing, and data exchange in various spaces and among different persons. Typically, users issue a search query to a search engine to obtain desirable content. A search engine is one type of information retrieval system that is designed to help users search for and obtain access to information that is stored in a computer system or across a network of computers. In response to a query from a user, the search engine can search different content providers online to obtain search results matching the query. Content providers can be a publisher, a content portal, or any other sources from which content can be obtained.

Typically, content providers implement a process of selecting, and then filtering content items that can be consumed by audiences. For example, in a news medium, an editor plays the role of a gatekeeper. That is, the editor's role is to decide what kind of news items will be published and which news items will be dropped. The news channel may receive various news items from all over the world. The channel may have its own set of ethics, policies, and biases through which the editor decides the news items that will be published, aired, or dropped. In some cases, certain news items are rejected by the editor due the organizations policy or the news items which are not suitable for publishing.

The type of content consumed by the audiences can have a profound influence on their preferences. For example, in a media-related debate, people tend to support one side or the other depending on the type of content consumed. The same news item coming from different sources can be presented in different ways and trigger audiences preconceived notions about the agenda of a particular content source (e.g., news organization).

Accordingly, there is a requirement for providing audiences with additional information that identifies content sources that are biased regarding certain content topics and identifying content topics which certain content sources avoid covering.

SUMMARY

The teachings disclosed herein relate to methods, systems, and programming for providing content.

One aspect of the present disclosure provides for a method, implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for providing content. The method comprises the steps of: obtaining a plurality of content items and publication information related thereto, determining, in accordance with a model, one or more topics of each of the plurality of content items, analyzing the related publication information associated with each content item to identify at least one source of a plurality of sources that published the content item, generating a distribution of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and identifying, based on the distributions of the plurality of content items, a bias of a source with respect to publishing content.

By one aspect of the present disclosure, there is provided a system for providing content. The system comprises a content retrieval unit configured for obtaining a plurality of content items and publication information related thereto, a topic determining unit configured for determining, in accordance with a model, one or more topics of each of the plurality of content items, a content processing unit configured for analyzing the related publication information associated with each content item to identify at least one source of a plurality of sources that published the content item, a clustering unit configured for generating a distribution of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and a bias determining unit configured for identifying, based on the distributions of the plurality of content items, a bias of a source with respect to publishing content.

Other concepts relate to software for implementing the present teaching. A software product, in accord with this concept, includes at least one machine-readable non-transitory medium and information carried by the medium. The information carried by the medium may be executable program code data, parameters in association with the executable program code, and/or information related to a user, a request, content, or other additional information.

In one example, there is provided, a machine readable and non-transitory medium having information recorded thereon for providing content, wherein the information, when read by the machine, causes the machine to perform: obtaining a plurality of content items and publication information related thereto, determining, in accordance with a model, one or more topics of each of the plurality of content items, analyzing the related publication information associated with each content item to identify at least one source of a plurality of sources that published the content item, generating a distribution of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item, and identifying, based on the distributions of the plurality of content items, a bias of a source with respect to publishing content.

DETAILED DESCRIPTION

FIG.1illustrates an exemplary system configuration in which a bias determining engine140can be deployed in accordance with various embodiments of the present teaching. InFIG.1, the exemplary system100includes users110, a network120, a search engine130, the bias determining engine140, a bias information database150, and one or more content providers160, including content provider1160-a, content provider2160-b, . . . , content provider n160-c.

The network120may be a single network or a combination of different networks. For example, a network may be a local area network (LAN), a wide area network (WAN), a public network, a private network, a proprietary network, a Public Telephone Switched Network (PSTN), the Internet, a wireless network, a cellular network, a virtual network, or any combination thereof. A network may also include various network access points, e.g., wired or wireless access points such as base stations or Internet exchange points120-a, . . . ,120-b, through which a data source may connect to the network120in order to transmit information via the network and a network node may connect to the network120in order to receive information. In one embodiment, the network120may be a content distribution network, which connects users110to the search engine130, which provides the users with relevant content obtained from the content providers160.

Users110may be of different types such as users connected to the network via desktop connections (110-d), users connecting to the network120via wireless connections such as through a laptop (110-c), a handheld device (110-a), or a built-in device in a mobile vehicle such as a motor vehicle (110-b). In one embodiment, user(s)110may be connected to the network120and be able to access and interact with online content (provided by the content providers160), via wired or wireless means, through related operating systems and/or interfaces implemented within user-wearable devices (e.g., glasses, wrist watch, etc.). A user, e.g.,110-a, may send a request or a search query for content to the search engine130and receive content and additional information related to the content (identified by the bias determining engine140) via the network120. For instance, by one embodiment of the present disclosure, the user may receive a content item in response to the query. The content item may be labelled with a tag indicating a bias of the content source from which the content item was retrieved.

Content providers160, may correspond to an entity, whether an individual, a firm, or an organization, publishing or supplying content, including a blogger, television station, a newspaper issuer, a web page host, a content portal, an online service provider, or a game server. For example, in connection to an online or mobile ad network, content provider130may be an organization such as CNN.com, a content portal such as YouTube and Yahoo.com, or a content-soliciting/feeding source such as Twitter or blogs.

In operation, the user issues a search query, which is transmitted to the search engine130via the network120. In response to receiving the search query, the search engine130searches different content providers i.e., content provider1160-a, content provider2160-b, . . . , content provider n160-cto obtain relevant content items (i.e., a search result) matching the query. The search engine130may be configured to label each piece of content item provided to the user with information related to a bias of the content source from which the content item is obtained. The search engine130may retrieve the relevant bias information from the bias information database150and present the search result(s) to the user.

The bias determining engine140is configured to obtain the content items from the content providers160and identify at least one type of bias associated with the content source (i.e., publisher, portal etc.,) from which the content item is obtained. The bias information of the plurality of sources is stored in the bias information database150. The search engine may access the stored bias information to label the corresponding content item with information related to the bias of the source from which the content item was obtained.

By one embodiment, the bias determining engine140is configured to determine a coverage bias of a source with respect to a topic. The coverage bias may be determined based on a number of content items of the topic published by the source. In this manner, the bias determining engine140can identify whether a particular source completely avoids covering a certain topic. Additionally, the bias determining engine140may also be configured to determine a sentiment bias (of the content source) with respect to a certain topic. The sentiment based bias corresponds to a manner in which content items of the certain topic are published by the content source. Specifically, the bias determining engine140may be configured to identify whether a particular topic is covered by the source in a positive manner (i.e., positive sentiments), a negative manner (i.e., negative sentiments) or in a neutral manner. Details regarding the operation of the bias determining engine are described later with reference to at leastFIGS.4,6, and9.

In the embodiment illustrated inFIG.1, the bias determining engine140is a stand-alone service, and may provide its services to a plurality of search engines (not shown) and users. InFIG.2, an alternative configuration200is provided, in which the bias determining engine140is connected to the search engine130as its backend service engine. That is, in this embodiment, the bias determining engine140is a special module in the backend of the search engine130. When there are multiple search engines (not shown), each may have its own backend module for content bias determination.

FIG.3depicts bias information, in accordance with an embodiment of the present teaching. Specifically,FIG.3depicts bias information determined by the bias determining engine140and stored in the bias information database150. As shown inFIG.3, the bias determining engine140identifies bias information for a plurality of content sources. For each content source, the bias determining engine140identifies a plurality of topics covered by the content source. For each topic, the bias determining engine140maintains a number of content items related to the topic that are published by the content source, and bias information associated with the topic.

By one embodiment, the bias determining engine140determines two types of biases: a first bias (referred to herein as a coverage bias) and a second bias (referred to herein as a sentiment based bias). The coverage bias of a source with respect to a topic is determined based on a number of content items of the topic published by the source. The coverage bias is determined without extracting information related to sentiment features included in the content items. In contrast, the sentiment based bias of a source with respect to a topic is determined based on sentiment features (such as positive sentiments, negative sentiments, and neutral sentiments) extracted from each content item of the topic that is published by the source. As is described later with reference toFIG.9, the bias determining engine140of the present teaching is trained to extract positive sentiments such as incredible, amazing, praising, inspiring etc., and negative sentiments such as sad, criticize, belittle, etc., from the content items published by the source in order to determine an overall sentiment of the source with respect to the topic.

Turning now toFIG.4, there is depicted an exemplary high-level system diagram illustrating interactions between content providers160, a search engine130, and a bias determining engine140, according to an embodiment of the present teaching. By one embodiment, the bias determining engine140includes a content analyzer405, a topic based bias determiner410, a sentiment based bias determiner415, and a bias information generator420. The search engine130includes a query processing unit425, a content retrieving unit430, and a labelling unit435.

The content analyzer405of the bias determining engine140retrieves content items from the content providers160including content provider1160-a, content provider2160-b, . . . , content provider n160-cand analyzes each content item. For example, the content analyzer405may be configured to analyze metadata associated with each content item. The metadata may include information pertaining to a geolocation of a source where the content item is obtained from, a time of content creation, a format of the content item, a type of content included in the content item etc.

The analyzed content items are further processed by the topic based bias determiner410and the sentiment based bias determiner415in order to identify the coverage bias (i.e., topic based bias) and sentiment based bias, respectively. Details regarding the operations of the topic based bias determiner410and the sentiment based bias determiner415are described later with reference toFIGS.6and9, respectively. Further, the bias information generator420utilizes the bias information (determined by the topic based bias determiner410and the sentiment based bias determiner415) to generate and store bias information (e.g., bias information records) in the bias information database150, with respect to the plurality of content sources.

The query processing unit425included in the search engine130receives a query issued by a user. The query may include keywords (entered by the user) related to content items that the user wishes to obtain. The query processing unit425, in response to receiving the search query, may process for example, the keywords included in the query and instruct the content retrieving unit430to retrieve relevant content items from different content providers (i.e., content provider1160-a, content provider2160-b, . . . , content provider n160-c).

Additionally, the content retrieving unit430obtains from the bias information database150, bias information corresponding to the retrieved content items. For example, the bias information may correspond to coverage bias (and/or sentiment bias) associated with the content sources from which the content items are retrieved. By one embodiment, the labelling unit435labels each content item with the corresponding bias information and transmits the labelled content items to the query processing unit. The query processing unit425further transmits the labelled content items to the user in response to the received query.

FIG.5Ais a flowchart of an exemplary process performed by a bias determining engine, according to some embodiments of the present teaching. The process commences in step505wherein the bias determining engine obtains content items from content providers. In step510, metadata associated with each of the obtained content items is analyzed. The analyzed content items are processed by the topic based bias determining unit and the sentiment based bias determining unit of the bias determining engine. The process in step515determines a topic based bias of content sources from which the content items are obtained. The process in step520, determines a sentiment based bias of content sources from which the content items are obtained. Further, in step525, the process generates records of the determined bias information and stores the generated bias information in a bias information database.

FIG.5Bis a flowchart of an exemplary process performed by a search engine, according to some embodiments of the present teaching. The process commences in step540, wherein a query is received from a user. In step545, the search engine retrieves one or more content items relevant to the query. The process then moves to step550, wherein the search engine retrieves bias information associated with the retrieved content items. For example, the bias information may correspond to a coverage bias and/or sentiment based bias of the content sources from which the content items are retrieved. In step555, each of the content items is labeled with the corresponding bias information. In step560, the labeled content items are provided to the user in response to the query.

Turning now toFIG.6, there is depicted an exemplary high-level system diagram of a topic based bias determiner410included in a bias determining engine140, according to an embodiment of the present teaching. The topic based bias determiner410includes a content retrieving and processing unit605, a source based clustering unit610, a topic determining unit620, and a topic bias decision unit640.

The content retrieving and processing unit605obtains a plurality of content items and processes each content item to extract publication information related thereto. Specifically, the content retrieving and processing unit605analyzes the related publication information associated with each content item to identify at least one source of a plurality of sources that published the content item. The topic determining unit620is configured to process each content item and determine, in accordance with a topic model635, one or more topics of each of the plurality of content items. It must be appreciated that a topic of a content item corresponds to a theme that characterizes the overall content included in the content item. Each content item and its associated one or more topics are stored in a content database630.

The identified at least one source of a plurality of sources that published a content item, and the one or more topics determined to be associated with the content item are utilized by the source based clustering unit610to generate a source-topic archive625. Specifically, by one embodiment, the source based clustering unit610utilizes a clustering model615to generate a distribution of each of the plurality of content items with respect to the plurality of sources and the one or more topics of the content item. An exemplary illustration of a source-topic archive generated by the topic based bias determiner410is illustrated inFIG.8.

The topic bias decision unit640is configured to identify, in accordance with a bias model645, whether each source has a bias with respect to publishing content based on the generated source-topic archive. Details pertaining to the different mechanisms utilized by the topic bias decision unit640in identifying whether a particular source has a bias with respect to publishing content of a certain topic are described later with reference toFIG.8. Further, the topic bias decision unit640stores the identified topic based biases for the plurality of sources along with the generated source-topic archive625in a topic bias information database650.

FIG.7depicts a flowchart of an exemplary process performed by a topic based bias determiner, according to some embodiments of the present teaching. The process commences in step710, wherein the topic based bias determiner obtains a plurality of content items. In step720, the topic based bias determiner analyzes meta-information (e.g., publication information) associated with each content item to identify at least one source that published the content item.

In step730, a topic determining unit included in the topic based bias determiner, determines, in accordance with a topic model, at least one topic of each of the plurality of content items. In step740, the topic based bias determiner clusters the content items with respect to the plurality of sources and the one or more topics of the content item. In other words, the topic based bias determiner generates a source-topic archive as depicted inFIG.8. Further, in step750, the topic based bias determiner identifies a topic based bias for each source based on a bias model. The identified bias information along with the generated source-topic archive are stored in a bias information database in step760.

FIG.8is a schematic illustrating a source-topic archive used for determining a topic based bias of a plurality of sources, according to an embodiment of the present teaching. For sake of simplicity,FIG.8illustrates the source-topic archive for three sources (Source1, Source2, and Source3) and three topics (Topic1, Topic2, and Topic3). The integers depicted under each topic correspond to a number of content items of the topic published by the corresponding source. For example, Source1publishes a total of 1000 content items of Topic1, 2 content items of Topic2and 90 content items of Topic3. Similarly, Source2publishes a total of 100 content items of Topic1, 75 content items of Topic2and no content items of Topic3, and Source3publishes 25 content items of Topic1, 50 content items of Topic2, and 75 content items of Topic3.

Upon generating the source-topic archive, the topic bias decision unit640included in the topic based bias determiner410, utilizes the bias model645to determine a coverage bias (i.e., a topic based bias) for the plurality of sources. For instance, referring toFIG.8, the topic bias decision unit640may identify that Source2has a coverage bias with respect to Topic3as Source2completely avoids covering i.e., publishing content items of Topic3.

Additionally, by one embodiment, a source may be deemed to have a coverage bias with respect to a topic, if the source publishes substantially less (or alternatively, substantially more) number of content items of the topic with respect to content items of the topic published by other sources. For example, referring toFIG.8, it can be observed that with regard to Topic2, Source1publishes 2 content items, whereas Source2and Source3respectively publish 75 and 50 content items. Accordingly, the topic bias decision unit may identify Source1to have a coverage bias with respect to Topic2, as it publishes substantially fewer content items of the topic as compared to other sources. In a similar manner, Source1may be deemed to have a coverage bias with regard to Topic1as it publishes substantially a greater number of content items (e.g., 1000 content items) as compared to the number of content items of Topic1published by Source2and Source3(i.e., 100 and 25 content items, respectively).

By one embodiment, the topic bias decision unit640may compare the number of content items of a topic published by a source to an average number of content items of the topic published by other sources. Based on a difference between the number of content items of the topic published by the source and the average number of content items of the topic published by other sources being above a predetermined threshold, the bias decision unit may be configured to identify the source to have a coverage bias with respect to the topic.

By one embodiment of the present disclosure, the topic bias decision unit640may identify coverage bias of a source with respect to a topic based on a biasing function (B). The function B may incorporate the following two parameters: Counti, jand Pi, j. The parameter Counti, jcorresponds to a number of content items of topic j currently published by Source i. The parameter Pi, jcorresponds to a probability of Source i publishing content items of Topic j. In other words, the parameter Pi, jcorresponds to an expectation of Source i to publish content items of Topic j. It must be appreciated that Pi, jmay be computed based on prior publishing information of Source i within a predetermined time period. Accordingly, in this embodiment, a source is identified to have a coverage bias with respect to a topic based on both, the count parameter and the probability parameter. For example, referring toFIG.8with regard to Source2, only if P23is greater than zero i.e., Source2is expected to publish content items of Topic3and the Count2, 3being zero, the bias decision unit may identify Source2to have a coverage bias with respect to Topic3.

FIG.9depicts an exemplary high-level system diagram of a sentiment based bias determiner415included in a bias determining engine140, according to an embodiment of the present teaching. In this illustrated embodiment, the sentiment based bias determiner415includes a training engine905for generating sentiment feature models903to be used for extracting sentiment features from content items. In what follows, there is first provided a description of the operation of the training engine905followed by a description of the operation of the sentiment based bias determiner.

For generating the sentiment feature models903, the training engine905may correspond to an offline mechanism which comprises a labeled content processor901, a sentiment feature model training unit902, and sentiment feature models903.FIG.10Ais a flowchart of an exemplary process for generating sentiment feature models903. In operation, this offline portion receives, at step1005, training data (labeled with sentiment features) and processes, at step1010, the received training data. The processed training data are then used by the sentiment feature model training unit902to train, at step1015, and obtain the sentiment feature models903. Such derived models may then be saved, at step1020, so that they may be used in operation to extract sentiment features from received content items.

Referring back toFIG.9, the sentiment based bias determiner415includes a feature extractor910, a sentiment feature extractor915, a source-topic-sentiment generator920, and a sentiment based bias analyzer930. By one embodiment, the sentiment based bias determiner415receives as input, the content items stored (along with their respective one or more topics) in a content database630and the source-topic archive625from the topic based bias determiner410. The feature extractor910extracts, in accordance with a contextual feature model925, one or more contextual features from each content item. The extracted contextual features from the content item enable determining a context of the content included in the content item. The sentiment feature extractor utilizes a trained sentiment feature model903to extract sentiment features from the content item. By one embodiment, sentiment features are extracted with respect to a context of the information included in the content item. The extracted sentiment features are sent to the source-topic-sentiment generator920.

The source-topic-sentiment generator920determines whether the extracted sentiment features are one of positive sentiments or negative sentiments. Further, the source-topic sentiment generator920receives the source-topic archive625(generated by the topic based bias determiner) and updates the source-topic archive625with information pertaining to the determined sentiments of content items. For example, by one embodiment, the source-topic sentiment generator920aggregates all the content items of a particular topic published by a certain source and determines a distribution of the sentiments (positive or negative) among the content items. In this manner, the source-topic sentiment generator920generates a source-topic-sentiment archive935. An exemplary source-topic sentiment archive935is illustrated inFIG.11.

Upon generating the source-topic-sentiment archive935, the sentiment based bias analyzer930determines, in accordance with a sentiment analysis model940, a sentiment based bias of each source with respect to publishing content based on the generated source-topic-sentiment archive935. Details pertaining to the different mechanisms utilized by the sentiment based bias analyzer930in identifying whether a particular source has a positive sentiment bias, a negative sentiment bias, or a neutral sentiment bias with respect to publishing content of a certain topic are described later with reference toFIG.11. Further, the sentiment based bias analyzer930stores the identified sentiment based biases for the plurality of sources along with the generated source-topic-sentiment archive935in a sentiment bias information database950.

FIG.10Bdepicts a flowchart of an exemplary process performed by a sentiment based bias determiner415, according to some embodiments of the present teaching. The process commences in step1030wherein the sentiment based bias determiner receives content items that are to be analyzed. In step1035, contextual features are extracted from each content item. In step1040, sentiment features are extracted from each content item in accordance with a trained sentiment feature model.

The process then moves to step1045, wherein the sentiment based bias determiner415generates source-topic-sentiment archive based on the extracted sentiment features. Further, in step1050, the sentiment based bias determiner415determines, for each source, a sentiment based bias based on an analysis model. The determined sentiment based bias information is stored in a sentiment bias database in step1055.

FIG.11is a schematic illustrating a source-topic-sentiment archive used for determining sentiment based bias of a plurality of sources, according to an embodiment of the present teaching. For sake of simplicity,FIG.11illustrates the source-topic-sentiment archive for three sources (Source1, Source2, and Source3) and two topics (Topic1and Topic2). As shown inFIG.11, Source1publishes 100 content items of Topic1and 2000 content items of Topic2, whereas Source2publishes 35 content items of Topic2and Source3publishes 100 content items of Topic1and 2000 content items of Topic2.

With regard to Source1, it can be observed that it publishes all 100 content items of Topic1with a positive sentiment (and no content items with a negative sentiment) and publishes all 2000 content item of Topic2with a negative sentiment (no content items with a positive sentiment). It must be appreciated that the sentiment based bias determiner of the present teaching determines that Source1publishes all content items of Topic1 in a positive sentiment by extracting sentiment features from each one of the hundred content items and determining that each of the hundred content items includes positive sentiment features.

In this manner, the sentiment based bias determiner identifies Source1 as having a bias (i.e., positive sentiment bias) with respect to content items of Topic1. In a similar manner, the sentiment based bias determiner identifies Source1as having a bias with respect to content items of Topic2(i.e., a negative sentiment bias), as it publishes all content items of Topic2in a negative manner. Similarly, Source2can be identified as having a bias with respect to Topic2(i.e., a negative sentiment bias) and Source3can be identified as having a positive bias with respect to content items of Topic1.

However, with regard to Topic2, it can be observed that the Source3publishes 35 content items having a positive sentiment and 33 content items having a negative sentiment. Accordingly, the sentiment based bias determiner identifies Source3as having a neutral bias with respect to Topic2as it publishes substantially similar number of content items having a positive and negative sentiments. By one embodiment, the sentiment based bias determiner may compute a difference between a number of content items published with positive sentiments and a number of content items published with negative sentiments. Based on the difference being less than a predetermined threshold value, the sentiment based bias determiner may identify the source as having a neutral bias with respect to the corresponding topic. In contrast, if the computed difference is greater than the predetermined threshold value, the sentiment based bias determiner may identify the source as having one of a positive sentiment (i.e., if the number of content items published with positive sentiments being greater than those published with negative sentiments) and negative sentiment (i.e., if the number of content items published with negative sentiments being greater than those published with positive sentiments).

Turning now toFIG.12, there is depicted an architecture of a mobile device1200, which can be used to realize a specialized system implementing the present teaching. In this example, a user device on which the functionalities of the various embodiments described herein can be implemented is a mobile device1200, including, but not limited to, a smart phone, a tablet, a music player, a handled gaming console, a global positioning system (GPS) receiver, and a wearable computing device (e.g., eyeglasses, wrist watch, etc.), or in any other form factor.

The mobile device1200in this example includes one or more central processing units (CPUs)1240, one or more graphic processing units (GPUs)1230, a display1220, a memory1260, a communication platform1210, such as a wireless communication module, storage1290, and one or more input/output (I/O) devices1250. Any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device1200. As shown inFIG.12, a mobile operating system1270, e.g., iOS, Android, Windows Phone, etc., and one or more applications1280may be loaded into the memory1260from the storage1290in order to be executed by the CPU1240. The applications1280may include a browser or any other suitable mobile apps for performing the various functionalities on the mobile device1200. User interactions with the content displayed on the display panel1220may be achieved via the I/O devices1250.

FIG.13is an illustrative diagram of an exemplary computer system architecture, in accordance with various embodiments of the present teaching. Such a specialized system incorporating the present teaching has a functional block diagram illustration of a hardware platform which includes user interface elements. Computer1300may be a general-purpose computer or a special purpose computer. Both can be used to implement a specialized system for the present teaching. Computer1300may be used to implement any component(s) described herein. For example, the present teaching may be implemented on a computer such as computer1300via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the present teaching as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.

Computer1300, for example, may include communication ports1350connected to and from a network connected thereto to facilitate data communications. Computer1300also includes a central processing unit (CPU)1320, in the form of one or more processors, for executing program instructions. The exemplary computer platform may also include an internal communication bus1310, program storage and data storage of different forms (e.g., disk1370, read only memory (ROM)1330, or random access memory (RAM)1340), for various data files to be processed and/or communicated by computer1300, as well as possibly program instructions to be executed by CPU1320. Computer1300may also include an I/O component1360supporting input/output flows between the computer and other components therein such as user interface elements1380. Computer1300may also receive programming and data via network communications.

Those skilled in the art will recognize that the present teachings are amenable to a variety of modifications and/or enhancements. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution—e.g., an installation on an existing server. In addition, the bias determining engine, as disclosed herein, may be implemented as a firmware, firmware/software combination, firmware/hardware combination, or a hardware/firmware/software combination.

While the foregoing has described what are considered to constitute the present teachings and/or other examples, it is understood that various modifications may be made thereto and that the subject matter disclosed herein may be implemented in various forms and examples, and that the teachings may be applied in numerous applications, only some of which have been described herein. It is intended by the following claims to claim any and all applications, modifications and variations that fall within the true scope of the present teachings.