CN104641314A - Computerized internet search system and method - Google Patents

Abstract

The present invention provides a system and method that can search social media and Internet websites and analyze and display the results based on various criteria, including the virality of social media websites. The results are presented in a user-friendly format such as a magazine, newsletter, newspaper, or scrapbook.

Description

Computerized internet search system and method

Cross References to Related Applications

This application claims priority to US Provisional Patent Application Serial No. 61/614,163, filed March 22, 2012, the entire contents of which are hereby incorporated by reference.

technical field

The present invention relates to Internet search systems or engines, and more particularly to Internet search engines that search social media, as well as news sites, e-commerce, websites, blogs, and blog site information to create and present search results.

Background technique

The Internet provides an almost limitless amount of information to Internet-connected computer, tablet, and phone users, but an effective search tool is needed to retrieve, organize, and present the retrieved information in a form that is useful to the searcher. Various search engines have been created using different methods and algorithms to obtain and organize data, as well as present advertisements relevant to the subject of search. As a result, entire industries have grown around these various search engines.

One of the first successful search engines was Netscape, which used keywords to search for items of interest on the Internet. Netscape includes algorithms to rank and display results based on their relevance to the search term.

Later, Google launched another, more successful search engine. Like Netscape, Google searches for keywords based on keywords or phrases entered into the search window, using algorithms that help retrieve and rank search results. Google uses algorithms that rank results in various ways, including by the number of times a keyword appears in the retrieved item or website (including in metadata and links to and from other websites). This makes Google vulnerable to manipulation by those who understand the algorithms Google uses to rank and display information retrieved in searches. Additionally, Google introduced the ability to display ads next to or above search results by advertisers who pay for that display by purchasing Google "adwords," so that each time a user enters a search term, the ad or announcement will appear next to the search results or above, and the Advertiser's account will be charged accordingly for the advertisement. The price of an ad term fluctuates according to its popularity, such that a very popular search term (meaning a search term that is frequently used) will cost much more than a less popular or less frequently used search term. Google has developed algorithms that price ad words based on popularity.

Facebook has launched the well-known social media site where users can create Facebook pages that include photos, comments, links, etc. posted on the subscriber's personal website. The Site offers Subscribers the opportunity to connect with other Subscribers by contacting or "friending" other Subscribers, allowing them to message, post messages, photos, etc. on each other's Facebook pages. Facebook also announced that it would launch a search engine using Facebook, but exactly how it would operate and how it would differ from existing search engines was unclear.

In addition, Google has launched a social media site, similar in some respects to Facebook, discussed below, but it is unclear how widely this site will be used, or if and how it will be linked to the basic Google search engine, and How Google makes this site available to its advertisers. Google is primarily page-based because it displays search results for pages from other websites, such as articles from newspapers or entries on Wikipedia (the famous online encyclopedia with entries contributed by users).

Twitter offers another newer social media model of communication over the Internet, which allows users and subscribers to "tweet" messages to "followers" who have signed up to receive messages tweeted by a particular individual. These messages can be anything from a few words to a link to a rather long "blog" or a comment about a particular event or an item of interest to a "tweeter" or "fan". Even though these messages have become popular through use by many individuals, including celebrities, actors, politicians, news reporters, journalists, and others, most of these messages do not appear to be indexed by existing search engines. Twitter is also used by various businesses to provide product announcements and special offers to customers who have signed up to receive such information.

There remains a need for a search engine that can search more traditional items such as newspapers, magazines, and Internet sites, as well as social networking sites and push messages, reviews, and other user-generated messages, in an easy-to-use, organized, and user-friendly These items are organized in a manner that presents the information so retrieved or located. There is also a need for a search engine that can analyze and test responses on the fly, and report on activity (including sociodemographic and other salient features) involving a particular item or person of interest. This information allows sponsors to follow specific groups and will also allow advertisers to pick specific topics or search results to advertise alongside. In addition, the information would allow opinion polls and other types of analysis to test public reactions to events of political significance.

Contents of the invention

The present invention achieves the above objects and overcomes the disadvantages of previous or other systems. The present invention provides a search engine and system, including a non-transitory computer-readable storage medium storing executable instructions of a computer system, the non-transitory computer The readable storage medium includes instructions to: at the computer system: receive a specified search topic from a user's computing device, such as a laptop computer, personal computer, tablet computer, mobile phone, smartphone; for the specified search topic, Search one or more social media networks, news or other sites, blogs or blog sites, and/or e-commerce sites to identify messages posted to said one or more social media or other sites that are relevant to a specified topic; Receive from a remote computer a selection of one or more attributes potentially associated with each of the identified messages; classify each of the identified messages according to the one or more selected attributes; generate an indication a visual presentation of the number of identified messages categorized according to one or more selected attributes; and causing the visual presentation to be provided to the remote computer.

Description of drawings

Further objects, features and advantages of the present invention will become clearer to those of ordinary skill in the art by reviewing the following detailed description of preferred embodiments in conjunction with the following drawings, in which:

FIG. 1 is a schematic diagram of a system and method for implementing an embodiment of the present Internet search system invention;

Figure 2 is a schematic diagram illustrating the operation of the Ad Actions feature of the system of Figure 1;

Figures 3 to 20 are schematic screenshots of various features of the embodiment of the invention shown in Figure 1;

FIG. 21 is a graph showing the amount of social media activity surrounding an object of interest.

Figure 22 is a graph showing the relative polarization (positive and negative) of analyzed social media users for a particular topic; and

Figure 23 is a map depicting social media users' tendency to support or disapprove of a politician.

Detailed ways

As used herein, "Content Rank" refers to a weighted average favorability score. It normalizes positive sentiment on any topic (people, places, events or things), making it possible to compare and rank Content Rank against any other topic (even of a different type). Trends in content ranking for topics can be tracked over time. The algorithm that does this involves collecting and recording the number of pieces of content returned by a search on a particular topic; determining whether the sentiment expressed in each piece of content is positive, negative, or neutral for that topic.

"Content Pulse" refers to the virality score; the average speed at which news on a topic spreads. Algorithms to achieve this include:

1. Record the number of entries of content (text, image, video, or any other form of content) returned by a search on a specific topic;

2. Determine the interval between the timestamps of each piece of content;

3. Add the interval of the timestamp;

4. Divide the sum of the timestamp intervals by the number of returned content items; and

5. Create a table in which the values of the timestamps fit the corresponding non-numerical scores (eg high spread may be represented by a score "A+" and low spread may be represented by a score "F").

Herein, the propagation direction refers to a score indicative of increasing or decreasing the propagation of a topic.

Algorithms used to determine the direction of transmission include:

1. Create a chart with an "X" axis and a "Y" axis; where:

a) The X-axis will mark the positions of multiple pieces of content in reverse order, so that the latest piece of content is at the "1" position on the X-axis, and the earliest piece of content is given the "N" position on the X-axis, where N is the content's position Total number of items (eg, if 100 items are returned in the search, N is 100, and the oldest item has the 100th position on the X-axis);

b) The Y axis will indicate time values from 0 to infinity;

2. Draw the time value (Y coordinate) for the interval between the timestamp of each content and the previous content (X coordinate);

3. Draw the least squares line passing through all the drawn points;

4. Determine the slope of the resulting line (if the slope of the resulting line is positive and the intervals are progressively shorter, the topic is increasing in transmissibility; if the slope of the resulting line is negative and the interval gets progressively longer, the spread of the topic decreases); and

5. Indicate with "+" when transmissibility is increasing and with "-" when transmissibility is decreasing.

Turning to the drawings, Figure 1 shows a schematic diagram of the operation of the social content and media search engine 40 of the present invention. A user 42 logs into the system using a login screen 44 (such as may be the login page of Facebook or other social networking site), and enters a search term. The system processes this search using a server 48 included in the system 40 of the present invention that retrieves original content from a combined content 50 of monitored websites, including social media sites, e-commerce sites, and blog sites, among others. Raw content 50 is processed through the system's servers 48 and passed through content cleaning and enhancement algorithms 52 (including socio-demographic databases, and U.S. and world census databases, computational linguistics engines, polarity aggregation engines, knowledge extraction engines, geographic location extraction engines , an engine for extracting the user's socio-demographic and educational background, and a proxy server "meta" identity with each social networking site's user) to filter the original content 50.

After the cleansing and enhancement process, the processed and enhanced content 54 is further processed by a content correlation process 56, which includes a content ranking algorithm and a content pulse algorithm to rank the content according to its popularity and dissemination, primarily on social media sites. order to place the content. The enhanced and ranked content 58 is then subjected to a trend prediction process 60 in which a user profile is created using its fluency or spread, momentum, and other measures of financial engineering and technical analysis known in the art. Probability ranking based on estimating the likelihood that a keyword's ranking will rise or fall. The results 62 of this predictive process are forwarded to part of the system for calculating a real-time mark 64 on the market value of the content. The results 62 and the probability rankings are also forwarded to the ad bid/ask platform (FIG. 2), where potential advertisers receive news about keywords of interest and can bid in an auction for what they are willing to pay to own in the Ads that appear next to specific items included in a content collection. The content collection is further formatted into, for example, a selected format, which may be a magazine, newsletter, newspaper, periodical, scrapbook, etc., depending on the subject matter of the search, user preferences, and the like.

Figures 1 and 2 further illustrate the business-to-business scheme of the present invention, which allows the sale of advertisements through auctions to advertisers who may have expressed interest in, for example, a particular object, keyword, topic or celebrity. In addition, advertisements may be triggered upon reaching certain predetermined numerical levels of certain selected metrics (eg, virality score, popularity score, polarization score, etc.). The trigger can also be restricted by geography or demographics, for example. Previously, the system offered potential advertisers the opportunity to bid on specific keywords, then received and issued bids to multiple bidders, allowing them to respond with a higher bid if desired, until a winning bidder emerged and others Bidders withdraw. Then, as described above, when that keyword reaches a predefined level, the successful bidder's ad is inserted in the appropriate position in the search result summary. In the example discussed in this article, the search results showed a video of President Obama on a skateboard that received significant social media attention (views, likes, dislikes, comments, and shares), creating an opportunity for skateboard manufacturers to make their ads follow Videos being "broadcasted" appear on the same or different "pages".

The search engine system of the present invention will now be described with reference to specific examples including social and other Internet and website searches. Fig. 3 shows a tablet computer 300 with an icon to start the search engine of the present invention. run on computing devices. The user activates the icon, thereby opening an application for the search engine of the present invention. As shown in Figure 4, a login screen appears asking the user to log in using a Facebook ID and password (for example), or other social media login 320 or a dedicated login 330 for the system of the present invention, referred to as "ttwick Meta Social ID"

After a successful login, the search engine calls or collects information about the user from the server operated by the present system (see Figure 1) or from the social media sites the user subscribes to or belongs to. These sites may be identified by the user in a preliminary questionnaire or pulled from the Internet using the search and storage functions of the present invention. Next, the search engine provides the user with a search bar 340 in which the user types or otherwise enters (e.g., using Siri or other voice-activated systems) the search term for the information the user wishes to collect, and presses "Enter" Or "return" key 350 to start the search.

While a search (see FIG. 6 ) is being conducted and social media and other content collected by the system 300, the system 300 may display an intermediate screen 360 that displays a user's profile summary 370, including previous searches in the form of a previous live magazine Roughly 380 libraries. In the event that accumulated usage and reward points are redeemable or that the buyer places or pays for an ad that is of interest to the user (which is actually a short commercial that is displayed during the search), the screen 360 Clickable icons may also be displayed for strategic partners 390 .

Once the search engine of the present invention completes the search and compiles and organizes the collected results, the search engine displays the results in a user-friendly real-time magazine format 395 (FIG. 7) that the user can view. The magazine includes a cover 400 featuring a photograph, drawing or other image representing the subject of the search, in this example President Obama. The user finds that a real-time score 410 reflecting the popularity of the search term is attached or superimposed on the cover 400, reflecting that other social media users tend to have more or less positive perceptions of the topic compared to other topics of current interest Degree. Cover 400 also includes "bars" 420 indicating the source of social media content via a website or other online application (e.g., Tumblr, YouTube, WordPress, Google plus (G+), Twitter, and Facebook) or any other platform that Sources of media content are arranged in order of content volume and displayed under the monitor "Real Time Buzz". The cover also includes a tab 430 that retrieves information such as a Wikipedia page or navigates to a specific section of the "magazine" that is relevant to the search topic.

When the user starts to browse the contents of the magazine 400, the screen behind the cover 400 is displayed. While the particular order of display may vary based on user or originator preference, in a preferred embodiment, the search engine first displays social media content on screen 440 with icons 450 showing the source of the content (in this example for WordPress blogs), and other buttons that allow users to comment, retweet, "like," "dislike," or share that content via social media sites. The "dislike" feature is unique and provides valuable information to analysts and marketers.

The user opens the "page" (FIG. 9), and the search engine displays additional social media content 470. On this page, the user chooses to comment on the previous page via (for example) Facebook 480, but of course other websites or social media sites could also be used to post comments (not shown).

Figure 10 illustrates other social media in response to a search query, here displayed as a split screen to simultaneously present several additional information items from different websites, while limiting the page turning or scrolling necessary for a user to view multiple responses for a particular search the number of pages. Preferably, the search results also present "communication alerts" that indicate content items such as articles, comments, images, tweets, videos, or other forms of social media entries that have received significant attention from viewers, and are included in the present invention ’s algorithm collates demographic and geographic information associated with the alert. In this example, the search engine targets a video of President Obama using a skateboard on stage at a conference where he spoke, which has received significant attention from the general public, as well as from a young male demographic group interested in skateboarding . This demographic and geographic information can be displayed as part of the dissemination alert 500 .

In an important aspect of the invention, the system has the functionality to invoke and display advertisements, announcements, or other items of interest in response to "spread alerts" that occur in response to a search. Thus, as shown in FIG. 11 , in response to a social media entry about President Obama's previous page on a skateboard, the screen, or a portion of the screen, displays an advertisement 510 for, for example, a skateboard. This feature appears to be significant because the entity placing the ad, whether it be the advertiser or its agency, can choose an inflection point, meaning, only if there is a certain degree of circulation, dissemination, popularity, or other The ad will appear when tested, or when that certain degree of circulation, spread, popularity, or other test meets other predetermined demographic or geographic criteria. Alternatively, using a communication system that allows participants to bid for the same ad space in real time, the system can notify multiple potential advertisers and place the ad to the highest bidder.

12-14 continue to show additional media content and provide some or all of the previously discussed features, including virality alerts, source icons, retweet or comment buttons, like or dislike buttons, and advertisements. Using these features to connect with others or create or retweet social media content (including likes and dislikes) can earn user loyalty points or credits that can be redeemed for various businesses offered by strategic partners.

Figure 14 also shows additional features and methods by which the system may use GPS locators or triangulation locators used by cell phone carriers to locate users to attract advertisers as discussed above. Advertisements and notices can then be published as advertisements in magazines to provide users with specific products or services. In Figure 14, based on the proximity between the user and the store, and possibly other factors such as previous percentages or previously expressed "likes" for a particular brand or one of its products, or any other factor determined by the advertiser, A chain of coffee shops offers "cents-off" coupons to users. The ad could also use a QR or barcode 530 to connect the user to a social media site, or the ad could be a traditional ad for a store or other supplier displaying the product, its price and sale 540.

An additional feature can also utilize transmitted user location, which can be seen in FIG. 15 , which shows a map 550 showing the user's location and any nearby sources located in search 555 of the social media content. Clicking on the locator pin 560 shown on the map retrieves information about the source and links of content located nearby. Users may consult this content and communicate with this source. Merchants can insert advertisements or messages on the pins along with a special or multiple specials generated in real time. Based on zoning (eg, city, state, province, country, etc.), the map can be zoomed in or out to show more or fewer locations and specials, either manually or by selecting a preset level of detail.

Figure 16 shows a "knowledge cloud" 570, which is an important feature of the present invention. The knowledge cloud includes words arranged by a portion of the language and weighted by frequency and importance relative to the search term. Words are extracted from the content returned by search and displayed in real time. Thus, if a particular word representing a topic receives significant social media attention, its position, relative size color or intensity in the cloud may change over time.

Figure 17 shows a subtopic breakdown of social media content 580 organized by a topic classification algorithm that displays results in real time. Figure 18 shows icons of key social media influencers on a search topic, also highlighting in real time the demographic segment generating the greatest influence. In this example, the screen displays two icons 590 showing the most influential or most frequently occurring social media content in the results. FIG. 19 shows some suggested related search topics 600 . The system 300 suggests other search topics by analyzing other users' search patterns. In the example discussed in this article, the system prompts Michelle Obama, Joseph Biden, and George W. Bush, as possible other searches of interest, and presents their photos, which the user can view to initiate additional search.

Figure 20 illustrates another feature of the present invention. Screen 610 displays reminders that the user has set for a particular topic. Themes and levels are set by the user, which can reflect a rise or fall in social media circulation, and/or rise or fall in popularity.

Figure 21 shows an open high low close chart used in the present invention to determine the amount of social media activity surrounding an object. The open high low close (OHLC) graph shows the first stage in the transformation of social media messages into metrics indicating the amount of social media activity surrounding an object. This is basically a message counter that also shows the rate at which messages are being sent about this object. This metric shows spikes and dips in messaging activity. This same format is used in a trading environment to display a security's opening, high, low and closing prices for any given day. The present invention defines its "trading day" as a set number of hours during which the system monitors messages that are converted into metrics and tracks patterns that are input to the algorithms used in the present invention. The graph below shows the total number of messages returned by the search.

Figure 22 shows the polarization of social media users (positive and negative) about the specific object analyzed and the polarization of social media users for all topics about the object in the past 14 days, showing the positive versus negative news. Relative proportion. Similarly, Figure 24 shows another feature of the present invention, a "heat map". This map shows, on a 1 to 100 scale, what social media users in the United States tend to like or dislike across all topics, covering millions of data points collected by the system of the present invention over several months. The number 1 indicates states with a very negative sentiment toward objects such as politicians, and is even displayed in red, for example. The number 100 indicates a state that holds a very positive sentiment towards a subject, and this state would be displayed eg in green. States with a balance sentiment value close to 50, or with insufficient data, are shown (for example) with a yellow tint.

Thus, the present invention provides a search engine capable of searching for topics on a variety of social media, such as blogs, tweets, in addition to more traditional websites, and in formats such as magazines, newsletters, scrapbooks, photo albums or Present results in a user-friendly format like newspaper. Various modifications and alternatives may occur to those skilled in the art after reading this specification. All such modifications are intended to be included within the scope and spirit of the invention as defined by the following claims.

Claims (80)

1. A non-transitory computer-readable storage medium storing computer system-executable instructions, the non-transitory computer-readable storage medium comprising instructions to: In a computer system: Receive specified search topics from a user's personal computer, laptop, tablet, smartphone or other computing device or terminal; For the specified search topic, search one or more social media networks, news or other sites, blogs or blog sites, and/or e-commerce sites to identify posts to one or more social media or other sites related to the specified news on the subject of receiving from a remote computer a selection of one or more attributes potentially associated with each of the identified messages; classifying each of the identified messages according to one or more selected attributes; generating a visual presentation indicating the number of identified messages categorized according to the one or more selected attributes; and The visual presentation is caused to be provided to the remote computer. 2. The non-transitory computer-readable storage medium of claim 1, wherein the one or more selected attributes include a time when each of the identified messages was posted. 3. The non-transitory computer readable storage medium of claim 2, wherein selection of the one or more attributes enables a further selection regarding a time frame in which each of the identified messages was published. 4. The non-transitory computer-readable storage medium of claim 2, wherein the visual presentation includes a graph indicating a number of identified messages posted during each of a plurality of time periods. 5. The non-transitory computer readable storage medium of claim 4, wherein the graph comprises a histogram. 6. The non-transitory computer readable storage medium of claim 1, wherein the one or more selected attributes include a geographic location where each of the identified messages was posted. 7. The non-transitory computer readable storage medium of claim 6, wherein selection of one or more selected attributes enables a further selection of a plurality of geographic locations to be included. 8. The non-transitory computer-readable storage medium of claim 6, wherein the visual presentation includes a map indicating a number of identified messages posted from each of a plurality of geographic locations. 9. The non-transitory computer-readable storage medium of claim 6, wherein the visual presentation includes a graph indicating a number of identified messages posted from each of a plurality of geographic locations. 10. The non-transitory computer readable storage medium of claim 6, wherein the geographic location comprises one of: a local community, city, state, province, region, country, and continent. 11. The non-transitory computer-readable storage medium of claim 1, wherein the selected attributes include one of relationships and correspondence between entities participating in one or more social media networks. 12. The non-transitory computer-readable storage medium of claim 11, wherein the visual presentation comprises a social network visual presentation presenting one of relationships and correspondence between entities participating in one or more social media networks. 13. The non-transitory computer readable storage medium of claim 1, further comprising: The content of each of the identified messages is parsed to determine one or more of the attributes from the content of each of the identified messages. 14. The non-transitory computer-readable storage medium of claim 13, wherein one or more of the attributes includes a sentiment indicative of one of the following attitudes: supportive attitudes towards a specified topic, neutrality and opposition to the assigned topic. 15. The non-transitory computer-readable storage medium of claim 14 , wherein the visual presentation includes a graph indicating the number of identified messages representing one of the following attitudes: a supportive attitude towards the specified topic, Neutral attitudes on assigned topics and opposing attitudes on assigned topics. 16. The non-transitory computer readable storage medium of claim 14, further comprising: Computing a subjectivity index indicating the identified messages for expressing support for or against the specified topic, and for expressing support for the specified topic, for the specified topic The ratio of neutral to the total number of identified messages with any of the negative attitudes on the specified topic. 17. The non-transitory computer readable storage medium of claim 16, wherein the subjectivity index I is calculated according to the following equation, comprising: I=(total number of support+total number of opposition)/(total number of support+total number of opposition+total number of neutrality), Wherein, the total number of supports includes the number of identified messages expressing support for the specified topic; The total number of objections includes the number of identified messages expressing objection to the specified topic; and The total number of neutrals includes the number of identified messages expressing a neutral attitude towards the specified topic. 18. The non-transitory computer readable storage medium of claim 16, further comprising: The subjectivity index is included in the visual presentation. 19. The non-transitory computer-readable storage medium of claim 13, wherein one or more of the attributes includes a language in which each of the identified messages was posted. 20. The non-transitory computer-readable storage medium of claim 19, wherein the visual presentation includes a graphic indicating the language in which each of the identified messages was posted. 21. The non-transitory computer-readable storage medium of claim 13, wherein one or more of the attributes includes a reference to a future time. 22. The non-transitory computer readable storage medium of claim 21, wherein the future time comprises at least one of: a future time, a future date, or one of a plurality of terms indicating a later time. 23. The non-transitory computer-readable storage medium of claim 22 , wherein the plurality of terms indicating a later time includes one or more of the following terms: soon, later, tonight, tomorrow, next week , next month, and next year. 24. The non-transitory computer readable storage medium of claim 13, further comprising: Semantic analysis is performed on the content of the identified messages based on parsing the content of each of the identified messages. 25. The non-transitory computer-readable storage medium of claim 24, wherein said semantically analyzing the content of the identified message includes identifying a list of words most commonly included in the identified message. 26. The non-transitory computer readable storage medium of claim 24, wherein the list of words most frequently included in recognized messages includes a predetermined number of terms. 27. The non-transitory computer readable storage medium of claim 25, further comprising: Frequently used conjunctions are omitted from the list of words most frequently included in recognized messages. 28. The non-transitory computer-readable storage medium of claim 27 , wherein the common conjunctions to omit from the list of words most commonly included in recognized messages include one of or more: and, but, one, one, or, and the. 29. The non-transitory computer readable storage medium of claim 24, further comprising: The words included in the list of words most frequently included in recognized messages are sorted according to the portion of the language represented by the words included in the list of words most frequently included in recognized messages. 30. The non-transitory computer readable storage medium of claim 29, wherein the portion of the language includes two or more of the following words: noun, verb, adjective, adverb, place name, proper name, and words expressing time. 31. The non-transitory computer readable storage medium of claim 29, further comprising: A quantity of each part of the language represented in the list of words most frequently included in recognized messages is determined. 32. The non-transitory computer-readable storage medium of claim 31 , wherein the number of each part of the language comprises: the number of languages represented in the list of words most commonly included in recognized messages The proportion of the part. 33. The non-transitory computer readable storage medium of claim 31 , further comprising: Included in the visual presentation are the number of words assigned a predetermined proportion of the plurality of parts of the language represented in the list of words most commonly included in recognized messages. 34. The non-transitory computer readable storage medium of claim 32, further comprising: Included in the visual presentation are numbers of words assigned according to predetermined proportions of parts of the language represented in the list of words most frequently included in recognized messages. 35. The non-transitory computer readable storage medium of claim 1, wherein the number of identified messages categorized according to one or more selected attributes is expressed as a total. 36. The non-transitory computer readable storage medium of claim 1, wherein the number of identified messages classified according to one or more selected attributes is expressed as a score. 37. The non-transitory computer readable storage medium of claim 1, wherein the number of identified messages categorized according to one or more selected attributes is expressed as a percentage. 38. The non-transitory computer readable storage medium of claim 1, wherein the visual presentation comprises one of: a table, a map, a histogram, a bar graph, a line graph, and a pie chart. 39. The non-transitory computer readable storage medium of claim 38, further comprising: A selection of a desired type of visual presentation is received from the remote computer. 40. The non-transitory computer readable storage medium of claim 1, further comprising: Advertisements are caused to be provided to the remote computer based on the specified topic. 41. The non-transitory computer readable storage medium of claim 40, wherein the advertisement is thematically related to the specified topic. 42. The non-transitory computer readable storage medium of claim 40, wherein the advertisement is selectively associated by an advertiser with the designated topic. 43. The non-transitory computer readable storage medium of claim 1, further comprising: Advertisements are caused to be provided to the remote computer based on information accessible about a user of the remote computer or other computing device. 44. The non-transitory computer readable storage medium of claim 1, further comprising: Remuneration is withdrawn from a user of the remote computer or other computing device. 45. A computer implemented method comprising: In a server computer system capable of communicating with the Internet to allow communications with server computers and remote computers supporting one or more social media networks: receive designations of subjects from a remote personal computer, laptop, tablet, smartphone or other computing device; For the specified topic, searching the one or more social media networks to identify messages posted to the one or more social media sites related to the specified topic; receiving from the remote computer a selection of one or more attributes potentially associated with each of the identified messages; automatically classifying each of the identified messages according to one or more selected attributes; generating a visual presentation indicating the number of identified messages categorized according to the one or more selected attributes; and The visual presentation is caused to be provided to the remote computer. 46. The computer-implemented method of claim 45, wherein the one or more selected attributes include a time when each of the identified messages was published. 47. The computer-implemented method of claim 45, wherein the one or more selected attributes include a geographic location where each of the identified messages was posted. 48. The computer-implemented method of claim 47, wherein the selected attributes include one of relationships and correspondence between entities participating in one or more social media networks. 49. The computer-implemented method of claim 45, further comprising: The content of each of the identified messages is parsed to determine the one or more attributes from the content of each of the identified messages. 50. The computer-implemented method of claim 49, wherein the one or more attributes include a sentiment indicative of one of the following attitudes: a supportive attitude toward a specified topic, a neutral attitude toward a specified topic, and a Objection to the subject. 51. The computer-implemented method of claim 50, wherein when the sentiment is not determined to represent a supportive attitude for the specified topic or a negative attitude for the specified topic, the sentiment is determined to include neutral attitude. 52. The computer-implemented method of claim 50, further comprising: Computing a subjectivity index indicating the identified messages for expressing support for or against the specified topic, and for expressing support for the specified topic, for the specified topic The ratio of neutral to the total number of identified messages with any of the negative attitudes on the specified topic. 53. The computer-implemented method of claim 52, wherein the subjectivity index I is calculated according to the following equation, comprising: I=(total number of support+total number of opposition)/(total number of support+total number of opposition+total number of neutrality), Wherein, the total number of supports includes the number of identified messages used to express support for the specified topic; The total number of disapprovals includes the number of identified messages expressing disapproval on the specified topic; and The total number of neutrals includes the number of identified messages expressing a neutral attitude towards the specified topic. 54. The computer-implemented method of claim 52, further comprising: The subjectivity index is included in the visual presentation. 55. The computer-implemented method of claim 49, wherein one or more of the attributes includes a language in which each of the identified messages was published. 56. The computer-implemented method of claim 55, wherein the visual presentation includes a graphic indicating the language in which each of the identified messages was posted. 57. The computer-implemented method of claim 49, wherein one or more of the attributes includes a reference to a future time. 58. The computer-implemented method of claim 57, wherein the future time comprises at least one of: a future time, a future date, or one of a plurality of terms indicating a later time. 59. The computer-implemented method of claim 58, wherein the plurality of terms indicating a later time includes one or more of the following terms: soon, later, tonight, tomorrow, next week, next month and next year, or a specific date, point in time, or time frame. 60. The computer-implemented method of claim 49, further comprising: Semantic analysis is performed on the content of the identified messages based on parsing the content of each of the identified messages. 61. The computer-implemented method of claim 60, wherein said semantically analyzing the content of the identified message comprises identifying a list of words most frequently included in the identified message. 62. The computer-implemented method of claim 61, wherein the list of words most frequently included in recognized messages includes a predetermined number of terms. 63. The computer-implemented method of claim 61 , further comprising: Frequently used conjunctions are omitted from the list of words most frequently included in recognized messages. 64. The computer-implemented method of claim 63 , wherein the common conjunctions to omit from the list of words most frequently included in recognized messages include one or more of the following words: and , but, one, one, or, and the. 65. The computer-implemented method of claim 61 , further comprising: The words included in the list of words most frequently included in recognized messages are sorted according to the portion of the language represented by the words included in the list of words most frequently included in recognized messages. 66. The computer-implemented method of claim 65, wherein the portion of the language includes two or more of the following words: nouns, verbs, adjectives, adverbs, place names, proper names, and words of time . 67. The computer-implemented method of claim 65, further comprising: A quantity of each part of the language represented in the list of words most frequently included in recognized messages is determined. 68. The computer-implemented method of claim 67, wherein the number of each part of the language comprises a proportion of that part of the language represented in the list of words most frequently included in recognized messages. 69. The computer-implemented method of claim 67, further comprising: Including in the visual presentation the number of parts of the language represented in the list of words most commonly included in recognized messages. 70. The computer-implemented method of claim 45, wherein the number of identified messages categorized according to one or more selected attributes is expressed as a total. 71. The computer-implemented method of claim 45, wherein the number of identified messages classified according to one or more selected attributes is expressed as a score. 72. The computer-implemented method of claim 45, wherein the number of identified messages categorized according to one or more selected attributes is expressed as a percentage. 73. The computer-implemented method of claim 45, wherein the visual presentation comprises one of: a table, a map, a histogram, a bar graph, a line graph, and a pie chart. 74. The computer-implemented method of claim 45, further comprising: Advertisements are caused to be provided to the remote computer based on the specified topic. 75. The computer-implemented method of claim 74, wherein the advertisement is thematically related to the specified topic. 76. The computer-implemented method of claim 74, wherein the advertisement is selectively associated by an advertiser with the specified topic. 77. The computer-implemented method of claim 45, further comprising: Advertisements are caused to be provided to the remote computer based on the accessible information about the user of the remote computer. 78. The computer-implemented method of claim 45, further comprising: Remuneration is withdrawn from a user of the remote computer or other computing device. 79. The computer-implemented method of claim 45, wherein the advertisement is selectively displayed when the popularity, popularity, and/or polarity of a particular topic reaches a predetermined level. 80. The computer-implemented method of claim 45, wherein users are able to earn loyalty points or points by using the system.