Patent Publication Number: US-8983924-B2

Title: Sharing public search queries and interactions

Description:
BACKGROUND 
     Social network sites are currently popular. Many social network sites basically attempt to capture a social graph of connections among users. The users are often family members, classmates, and other prior acquaintances. 
     Current information retrieval systems allow individual users to employ search engines to explore various areas of knowledge stored in a data corpus, or a variety of different corpora, and accessible either directly, over a network. For instance, some information retrieval search engines allow a user to submit a query to search for information over a wide area network, such as the Internet. Conventionally, a user may submit queries that represent topics of interest to that user. 
     Searches using these conventional types of search engines are private, in that the originator of the query, and in fact the queries themselves along with their search results, are not automatically shared with anyone else. If the user does wish to share this type of information, it is currently done by a manual, user-initiated, process which can be fairly cumbersome and error prone. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     Current social network sites encounter problems in allowing users to learn more about (and construct deeper relationships based on) shared interests with other people. Because information retrieval search systems are often used by users to search for information which is of interest to them, the searches, and the results that those users selected in response to the searches, often yield a great deal of knowledge about the current interests of the individuals using the information retrieval system. By making the information retrieval process public, so that it can be followed by others, an interest graph can be captured that allows people to learn more about shared interests with other people. 
     Therefore, when a user enters an information retrieval query, a topic feed generator automatically generates a topic feed for publication or distribution to a set of followers or recipients who have elected to receive the topic feed from either the user or based on the subject matter content of the query. A feed distributor component automatically distributes the topic feed, which includes the query, to the identified recipients. A search component executes the search and returns results, corresponding to the query, to the user. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a simplified block diagram of a public search system, in accordance with one embodiment. 
         FIG. 2  is a simplified flow diagram illustrating one embodiment of the operation of the system shown in  FIG. 1 . 
         FIGS. 3A-3C  are exemplary embodiments of user interface displays. 
         FIG. 4A  is a flow diagram showing one embodiment of processing click data. 
         FIGS. 4B and 5  show exemplary embodiments of user interface displays. 
         FIG. 6  is a more detailed block diagram of a public search system, in accordance with one embodiment. 
         FIG. 7  is a more detailed flow diagram illustrating one embodiment of the operation of the system shown in  FIG. 6 . 
         FIG. 8  is a flow diagram illustrating one embodiment for processing a query. 
         FIG. 8A  illustrates one embodiment of information stored in a topic and statistics data store. 
         FIG. 8B  illustrates one embodiment of information items contained in an exemplary record for a post 
         FIG. 9  is a more detailed flow diagram showing one embodiment for processing click data. 
         FIG. 10  is a simplified block diagram of an interest tracking component, in accordance with one embodiment. 
         FIG. 11  is a simplified flow diagram illustrating one embodiment of the operation of the interest tracking component shown in  FIG. 10 . 
         FIG. 12  illustrates one embodiment for processing a message input. 
         FIG. 13  is a block diagram of one illustrative computing environment in which the public search system can be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     General Operation 
       FIG. 1  is a simplified block diagram of one embodiment of a social network  8  that includes public search system  10 . Public search system  10  illustratively includes topic feed generator  12 , feed distributor component  14 , search component  16  and processor  18 . Public search system  10  is also shown connected to a topic and statistics data store  20 . In the embodiment shown in  FIG. 1 , public search system  10  is also illustratively connected to user interface component  22  which resides on a client device. The client device can be any suitable computing device, such as a laptop computer, a cellular telephone, any other type of personal digital assistant (PDA), other mobile device, or other computing device (such as a desktop computer). 
     In the embodiment shown in  FIG. 1 , public search system  10  is shown connected to user interface component  22  through network  24 . Network  24  can be a local area network, a wide-area network (such as the Internet) or any other desired network. Of course, user interface component  22  could also be directly connected to, or reside on, public search system  10 .  FIG. 1  also shows that public search system  10  is connected to search engine  26  which, itself, is connected either through a network  28 , or directly, to a corpus  30  that is to be searched. 
     It will be appreciated that the block diagram shown in  FIG. 1  is exemplary only. The functions associated with the elements to be described can be combined into a single component, or further divided into more discrete components. Similarly, the connections shown in  FIG. 1  can be through networks, or direct connections, and those shown are for exemplary purposes only. 
       FIG. 2  is a simplified flow diagram illustrating one embodiment of the operation of social network  8  shown in  FIG. 1 .  FIGS. 3A-3C  show illustrative user interface displays corresponding to the operation of the system described with respect to  FIG. 2 .  FIGS. 1-3C  will be described in conjunction with one another. 
     User interface component  22  illustratively resides on a user&#39;s system, which may be a client device. In one embodiment, in order to use system  8 , a user first engages user interface component  22  to set up an account which includes, for example, a user name and password. The user inputs these items through interface component  22 , and they are stored in topic and statistics data store  20 . The user is illustratively able to identify topics of interest which the user wishes to follow, or individual users or groups of users that the user wishes to follow as well. This information is also stored in data store  20 . This can all be done through user interface displays generated by component  22 . 
     Once this is done, and the user wishes to use system  8 , the user illustratively logs on to system  8 , through an authentication component (which is described in greater detail below), and user interface component  22  generates a user interface display  40  such as that shown in  FIG. 3A . In the illustrative user interface display  40 , the user&#39;s user name is John Doe and that is displayed generally at  42 , along with an image  44  which can be selected by John Doe to represent his user name. The display also presents a search box  46 , which is a text box that allows the user to enter text (such as by using a keyboard) that represents a search query that the user wishes to have executed. Interface display  40  also illustratively displays the user names or topics that user  42  is following. This is generally indicated at  48 . User interface display  40  may also illustratively list other users that are following user  42 . This is generally indicated at  50 . In addition, user interface display  40  displays a public stream of information  52 , which has already been generated. The public stream  52  illustratively includes a plurality of posts  54 , corresponding to received topic feeds  70  which will be described in greater detail below. Further, user interface display  40  illustratively includes a set of actuable elements generally shown at  200 . By actuable (or actuatable) elements, it is meant that the elements can be actuated through a suitable user interface operation, such as by clicking on an element using a pointing device (like a mouse) or double-clicking or otherwise. These are described in greater detail below as well. 
     When the interface display  40  is displayed by user interface component  22 , the user can enter a desired query into textbox  46 . In the example shown in  FIG. 3A , the user has typed in “stories about Paul Bunyan”. This corresponds to query  60  shown in  FIG. 1 . The query is sent from user interface component  22  to public search component  10 , and specifically to topic feed generator  12 . Receipt of query  60  by public search system  10  is illustrated by block  62  in  FIG. 2 . 
     Topic feed generator  12 , in response to receiving query  60 , generates a topic feed that includes query  60  and that is to be output in the public stream  52  as a topic feed  70 . Generating the topic feed  70 , including the query  60 , is indicated by block  72  in  FIG. 2 . 
     Feed distributor component  14  then accesses data store  20  to identify the followers of both John Doe (the user that submitted query  60 ) and the followers of the subject matter content of the query  60 , itself. For instance, the subject matter content of query  60  is illustratively “Paul Bunyan”. Therefore, if any users have indicated that they wish to follow the topic category (or subject matter category) “Paul Bunyan”, then they would be identified by feed distributor component  14  as a recipient of topic feed  70  as well. Feed distributor component  14  then distributes or publishes the topic feed  70  to those recipients that were identified. Identifying recipients is indicated by block  73  in  FIG. 2 , and distributing the topic feed  70  to the recipients is indicated by block  74  in  FIG. 2 . It can thus be seen that upon submission of query  60 , system  8  automatically publishes that query in a topic feed to all relevant recipients, without any further input from the user. 
     The distribution or publication can be done in other ways as well. For instance, feed distribution component  14  can wait to update the system of a recipient until the recipient logs on to the system or otherwise engages the system. Similarly, the feed distribution component  14  can wait to distribute topic feed  70  to recipients until after the user has interacted with the results from the query (as described below). 
     It should be noted that, in  FIG. 3A , a wide variety of other embodiments can be used. For instance, public stream  52  may be divided into two streams, one which reflects posts from people that the user is following and the other that reflects posts from topic areas that the user is following. Of course, a wide variety of other changes can be made to the display shown in  FIG. 3A , as well. 
     Once the topic feed  70  has been distributed and published to the identified recipients, a user interface component  22  (corresponding to the recipients) illustratively generates a display for those recipients, such as shown in  FIG. 3B .  FIG. 3B  is similar to that shown in  FIG. 3A , except that the user  42  is indicated as Jane Deer. It can be seen from  FIG. 3A  that Jane Deer is one of the followers of John Doe. Therefore, the topic feed  70  generated from any activity of John Doe will be distributed to, and published at, a user interface component  22  residing at Jane Deer&#39;s device. 
     The topic feed  70  is posted as a post  54  on the public stream  52  of the user interface display shown in  FIG. 3B . It can be seen in  FIG. 3B  that the public stream  52  includes the post “John Doe searched for stories about Paul Bunyan”.  FIG. 3B  shows that both the source of the post and the search which is the subject matter of the post are actuable links, and this is indicated by boxes  90  and  92  in  FIG. 3B . Therefore, the term “John Doe” is included in box  90  and the query “stories about Paul Bunyan” is included in box  92 . If the user of the system that generated the display in  FIG. 3B  (that is, Jane Deer) clicks on the text in either box  90  or  92 , then the user&#39;s system takes action. If the user clicks on box  90 , which contains the source of the post, then the user&#39;s system links the user to the home page of the person identified in box  90  (John Doe). Therefore, if Jane Deer clicks on box  90  that includes “John Doe”, then Jane Deer&#39;s system navigates to the home page for John Doe, and presents Jane Deer with a user interface display such as that shown in  FIG. 3A . If Jane Deer clicks on box  92 , the results for that query will be returned to Jane Deer. This will be described in more detail below. 
     At the same time that feed distributor component  14  is distributing the topic feed generated by generator  12 , search component  16  is also providing query  60  to search engine  26  for execution against corpus  30 . Search engine  26  may illustratively be a conventional information retrieval search engine that searches the web for content associated with the query that was input. Search engine  26  can alternatively be implemented in search component  16 . Search engine  26  executes the search against corpus  30  and returns search results  80  to search component  16  in public search system  10 . Search component  16  then returns results  80  to user interface component  22  corresponding to the author of the query  60  (that is, corresponding to John Doe). 
     Not only does search component  16  pass query  60  on to search engine  26  for execution against corpus  30 , but search component  16  also searches the records stored in data store  20  for any other posts that are relevant to the subject matter of query  60 . It may be that John Doe or other users of public search system  10  have submitted similar queries, and therefore topic feeds  70  may have already been generated for those similar queries. Thus, search component  16  searches data store  20  for posts from previously generated topic feeds  70  that are relevant to query  60 . These are returned to the user through user interface component  22  as stream results  81 . In other embodiments, the records returned from searching data store  20  can be used to re-order search results  80  returned from search engine  26  or a search engine other than search engine  26 . 
     User interface component  22  then generates a display  98  for the user (who submitted the query) such as that shown in  FIG. 3C . The display shown in  FIG. 3C  is similar to that shown in  FIG. 3A , and similar items are similarly numbered. However, there are a number of differences. It can be seen that  FIG. 3C  shows that the search results are presented in two separate categories. The first is stream results section  100  and the second is web results section  102 . Under web results section  102 , the search results  80  generated by search engine  26  are presented to the user as user actuable links. By way of example, one of results  80  is a URL entitled “Paul and Babe in Bemidji, Minn.”. It is shown in a box  103  to indicate that it is actuable on display  98 . That is, if the user clicks on one of the results  80 , the user will be taken to the web page, or other corpus entry, that spawned that search result. 
     Under stream results section  100 , user interface display  98  lists all posts which contain search results  81  relevant to query  60 . That is, if data store  20  included posts that were relevant to the query  60 , those posts are also displayed in the stream results  81 , along with the web results  80 . Again, to the extent that there are any actuable links in stream results  81 , posted in stream results section  100 , the user can simply click on those actuable links and be taken to the underlying source that spawned the link. 
       FIG. 3C  also shows that system  8  can suggest additional search strategies. This is shown generally at  105 . 
     Sharing Activity 
       FIGS. 4A-5  illustrate yet another embodiment. In the embodiment shown in  FIGS. 4A-5 , not only is the public stream  52  filled with topic feeds  70  that contain queries, but it also contains other search activities by users, such as whether the user clicked on one of the results  80  or  81  returned in response to a query  60 , or whether the user actuated any of the links in the public stream  52 .  FIG. 4A  is a flow diagram illustrating one embodiment of the operation of the system shown in  FIG. 1 , where a user (e.g., Jane Deer) that has received topic feed  70  actuates one of the links in one of the posts in topic feed  70 . 
     By way of example, assume that John Doe had clicked on one of the search results, such as result  103 , that was presented in response to the query  60 . In that case, the user interface display  120  generated at Jane Deer&#39;s device is updated to look like that shown in  FIG. 4B . That is, it would not only show that John Doe had searched for stories about Paul Bunyan, but it would also indicate that John Doe clicked on (or actuated a link for) one of the search results  103 . In the embodiment shown in  FIG. 4B , display  120  also shows that the public stream  52  has been updated to indicate that John Doe clicked on the particular URL “Paul and Babe in Bemidji, Minn.” that is highlighted by box  122  to indicate that it is also actuable by Jane Deer. 
     One embodiment of the operation of system  8  in generating this type of post is shown in  FIG. 4A . First,  FIG. 4A  shows that public search system  10  receives either a click on a query or a result that was previously displayed in public stream  52  by user interface component  22 . That is, assume that John Doe clicked either on a query in his public stream  52  or (in this case) one of the search results  103  displayed in  FIG. 3C . This information is conveyed to public search system  10  as illustrated by block  150  in  FIG. 4 . 
     Topic feed generator  12  then generates a topic feed that includes either the query clicked on by John Doe, or, in this case, the result  103  from web results  80  that was clicked on by John Doe. Generating the topic feed, including the actuated result, is indicated by block  152  in  FIG. 4A . 
     Feed distributor component  14  then identifies recipients of the topic feed just generated, and distributes or publishes the topic feed generated in block  152  to those recipients. This is indicated by blocks  154  and  156 . Therefore, as shown in  FIG. 4B , Jane Deer&#39;s user interface display  120  is updated with an additional post to the public stream  52  which shows that not only has John Doe  90  searched for “stories about Paul Bunyan”, but he actually clicked on one of the results  80  returned in response to that query, namely a URL entitled “Paul and Babe in Bemidji, Minn.”  103 , shown in block  122  in user interface display  120 . 
     In response to John Doe clicking on that result, search component  16  and search engine  26  are used to return the document or page that spawned the link in box  122 , to John Doe over user interface component  22 , for viewing. This is indicated by block  158  in  FIG. 4A . 
     While  FIG. 4A  has been described with respect to John Doe clicking on one of the search results  80  that was returned in response to the query  60 , the same action is taken if any other user clicked on an actuable link in their public stream  52 . For instance, if Jane Deer is presented with the user interface display  120  shown in  FIG. 4B , Jane Deer can then click on the query “stories about Paul Bunyan”  92  or on the result “Paul and Babe in Bemidji, Minn.” shown in box  122 , and public search system  10  will generate a topic feed  70  for that activity as well. That is, assuming that Jane Deer has clicked on the query in box  92 , topic feed generator  12  will generate a topic feed that includes that query, and feed distributor component  14  will distribute the topic feed to all identified recipients for that topic feed. Similarly, search component  16  and search engine  26  will return the results  80  of the actuated query to the user interface component  22  used by Jane Deer and that will be displayed to John Doe, in a similar fashion to that shown in  FIG. 3C  (where they were displayed for John Doe) in the first instance. 
     Similarly, if Jane Deer were to instead click on the result in box  122 , then John Doe&#39;s user interface display would be updated to show that as well. This is because John Doe is a follower of Jane Deer and would therefore be the recipient of any topic feeds generated by Jane Deer&#39;s search activity. 
     Other Features 
     User interface displays  3 A- 3 C and  4 B show a number of additional features as well. First, the user interface displays include a number of navigation buttons generally indicated at  200 . These buttons illustratively include a “home” button, a “web” button, a “news” button, an “images” button, a “videos” button, a “stream” button, a “people” button, and an “about” button. Of course, these are exemplary buttons only and different buttons, additional buttons, or fewer buttons could be used as well. In the embodiment shown, the “home” button takes the user to the user&#39;s home page showing the public stream  52  generated using topic feeds  70  that were received by that user. The “web” button takes the user to a web browser and the “news” button takes the user to a news site that displays news that may be relevant to the user. The “images” and “videos” buttons allow a user to easily confine submitted queries to look for either images or videos that are relevant to the search terms in the query, and the “stream” button allows the user to search the user&#39;s own public stream  52  for posts relevant to the query. The “people” button allows the user to identify people of interest, that the user may wish to follow. The system can also automatically suggest experts and other people to follow even if the user does not actuate the “people” button. The “about” button describes the functionality of the system. 
     A number of the user interface displays also include additional features on the bottom of the posts, generally indicated by arrow  204 . They include a “time of post” feature, a “like” feature and a “comment” feature. The “time of post” feature simply indicates the time that a post was posted on the user&#39;s public stream  52 . The “like” button allows the user to indicate that he or she likes the post, and the “comment” button allows the user to comment on the post. This may be done, for instance, by exposing a text box within which the user can comment on the post and have that comment published to other recipients. One embodiment of this is shown in  FIG. 5 .  FIG. 5  shows part of a post that includes the result  103  discussed above.  FIG. 5  also shows that, once the user has actuated the “comment” button, a dropdown text box  220  appears, which allows the user to enter a textual comment related to the post  103 . The textual comment in box  220  is then distributed to identified recipients. 
     More Detailed Embodiment 
       FIG. 6  illustrates a more detailed block diagram of system  8 , and particularly a more detailed block diagram of one embodiment of public search system  10 . Items in  FIG. 6  which are similar to those shown in  FIG. 1  are similarly numbered. However,  FIG. 6  shows that public search system  10  includes a variety of other components as well. 
     The input from user interface component  22  to public search system  10  is shown not simply as query  60 , but as a topic input  210 . Topic input  210  can be a query, a click, an administrative input, such as the input of a user name or password to log on to the system, an explicit indication of a topic or person of interest that is to be followed, or a wide variety of other inputs. 
     Public search system  10  also includes additional components such as user authentication component  212  which is used to authenticate user&#39;s logging on to the system. Public search system  10  also includes topic data collection component  214  which collects various items of data (described below) that are stored in data store  20 . System  10  also includes query/result analyzer  216  that can be used to both identify the subject matter content of queries and results, and to analyze whether they should more properly be pursued in a private venue. 
     Messaging and notification system  218 , also included in system  10 , is used for receiving and transmitting messages among users of system  10 , and also for providing notifications to users in system  10 . The messages and notifications are indicated by block  220 . 
     System  10  also includes topic statistics generator  222  that generates a variety of statistics which will be described below, as well as interest tracking component  224  and suggestion component  226 . Interest tracking component  224  processes the various queries and search results that a user interacts with on system  10  to implicitly determine a user&#39;s interests. These are included, along with interests explicitly input by a user, to not only suggest topics or people to follow, but to also suggest changes to search queries that might be input by a user. These suggestions are generated by suggestion component  226 . 
       FIG. 6  also shows that data store  20  has its own index  203 . Index  203  indexes the information in data store  20  for ease of searching. 
       FIG. 7  is a flow diagram illustrating one embodiment of the operation of the system shown in  FIG. 6 .  FIGS. 6 and 7  will be described in conjunction with one another. It should be noted, of course, that the features described in  FIGS. 6 and 7  can be in addition to, or instead of, those shown in the previous figures. Also, the particular flow of operation described with respect to  FIGS. 6 and 7  is illustrative only. In other words, certain steps could be reversed or performed in different orders. Similarly, the functions of the various components shown in  FIG. 6  could either be combined or split even more finely, using other components. Those shown are shown for exemplary purpose only. 
     During operation, a user first logs on to system  8 , through user interface component  22 , by illustratively performing some type of user authentication steps. This is managed by user authentication component  212  and indicated by block  300  in  FIG. 7 . In one embodiment, user authentication simply requires the user to input a user name and associated password. User authentication component  212  then compares the user name and password with profile records stored in data store  20  (or another data store) to determine that the user is entering a valid user name and password. If so, processing continues. If not, the user is prohibited from accessing system  10 , until a valid user name and password have been entered. Of course, other authentication components could be used, such as any type of biometric recognition system, voice recognition, etc. 
     Once user authentication has been performed, the user can provide a topic input  210  to public search system  10 . The topic input can be a query, a click on a query, a comment, a click on a query result or a person, an indication that the user likes a particular post, an explicit indication that the user is interested in a given topic or a person, etc. Any type of input which reflects this type of search activity is received by processor  18  and routed to the appropriate components for analysis and processing. Receiving the topic input is indicated by block  302  in  FIG. 7 . 
       FIG. 7  shows that there are a number of different possibilities for the topic input  210 . For instance, the topic input may be a query, or it may be a click (either on another person&#39;s query in a user&#39;s public stream, or on a search result that shows up in the user&#39;s public stream), it may be an explicit interest indication by the user indicating that the user is specifically interested in a topic area (such as a person or a subject matter area), or it could be another input. This is indicated by blocks  320 ,  322 ,  324 ,  326  and  328  in  FIG. 7 . 
     Processing a Query 
     If, at blocks  320  and  322 , it is determined that the input is a query, then query processing is performed as shown in  FIG. 8 . This is indicated by block  330  in  FIG. 7 . 
     If the input is a query, such as query  60 , then the processing described above with respect to  FIG. 2  is performed. This is indicated by block  340  in  FIG. 8 . That is, a topic feed  70  is generated for the query  60  and recipients of the topic feed are identified and the topic feed  70  is automatically distributed to those recipients. The query  60  is then executed against a data store  30  and against posts in data store  20  and the results  80  and  81  are returned to the user. Embodiments of the user interfaces generated to show this were also described above with respect to  FIGS. 3A-5 . 
     However,  FIG. 8  shows that, in another embodiment, additional processing can be performed as well. For instance, the query  60  can be provided to query/results analyzer  216  where a linguistic analysis is performed on the query  60  to identify the topics of interest reflected in the query. In one embodiment, keyword recognition is performed on the query to identify keywords, that are associated with topics of interest, that occur in the query. Of course, more advanced natural language processing and statistical analysis can be performed as well, to identify topics of interest. Performing linguistic analysis on the query is indicated by block  342  in  FIG. 8 . 
     The topics of interest identified in the linguistic analysis are then output to interest tracking component  224  (shown in  FIG. 6 ). Interest tracking component  224  is described in greater detail below, with respect to  FIGS. 11 and 12 . Suffice it to say, for now, that interest tracking component  224  receives various items of information based on a user&#39;s activity (such as topics of interest reflected in queries or search results that the user has interacted with) and identifies areas of interest for the user based on all the information that the user is generating, or interacting with. Outputting the results of the linguistic analysis to the interest tracking component  224  is indicated by block  344  in  FIG. 8 . 
       FIG. 8  also shows that query/result analyzer  216  can perform additional processing as well. For instance, when using public search system  8 , a user may forget that the user&#39;s queries are actually being published. Therefore, in one embodiment, query/results analyzer  216  analyzes the query, and possibly the query results, to determine whether the query might more appropriately be conducted in private. For instance, the user may not wish the public to know that he or she is looking for a new job. If the user posts a query such as “where can I automatically update my resume?”, this may give the user&#39;s co-workers, and even supervisors, information that the user does not yet wish to be made public. Of course, there are a variety of other subject matter areas that a user may wish to search, but which the user does not wish to be made public. Therefore, query/results analyzer  216  is illustratively set up to analyze the text of a query, and the text of results, to determine whether they are related to subject matter areas that may best be kept private. This is indicated by block  346  in  FIG. 8 . If not, then processing simply continues at block  354 , which is discussed below. 
     However, if, at block  346 , query/results analyzer  216  determines that the query or results relate to a subject matter area that the user may wish to be kept private, then query/results analyzer  216  provides an output to user interface component  22  that suggests to the user that the query be pursued privately. This can take the form of a cautionary message that is in bold letters, in colored letters, or otherwise. The output may also allow the user to simply click “yes” or “no” to direct the system to a private search forum. Suggesting that the query be pursued privately is indicated by block  348  in  FIG. 8 . 
     If the user does not desire that the query be pursued privately, then processing again simply reverts to block  354 . However, if, at block  348 , it is determined that the user does wish to have the query pursued privately, then processor  18  simply redirects the user to a private search environment, such as by opening a web browser using a private search engine. Determining whether a user wishes to proceed privately and, if so, directing the user to a private search environment, is indicated by blocks  350  and  352  in  FIG. 8 . 
     At block  354 , data collection component  214  and topic statistics generator  224  collect various items of information from the query (and optionally the results) and generate desired statistics from that information and update and store the topic and statistics data generated, in data store  20 . The information is illustratively indexed and the index entries are stored in index  203  as well. 
     Processing Clicks 
     Referring again to  FIG. 7 , if it is determined at block  324  that the topic input  210  is not a query, but is instead a click on a query or a click on a result, then click processing is performed, as indicated at block  332 . One embodiment of click processing is described, in more detail, in  FIG. 9 . 
     Processor  18  first determines whether the click received as topic input  210  was on another user&#39;s query. This is indicated by block  550  in  FIG. 9 . If the input was a click on another user&#39;s query, then system  8  performs query processing as shown in  FIG. 8 , except that it is performed for the present user (who just clicked on the query) instead of for the user that previously input the query. For instance, if John Doe generates the query “stories about Paul Bunyan” and this is posted to the public stream  52  of Jane Deer, and Jane Deer clicks on that query, then query processing is performed in the same way as if Jane Deer had input the query originally, except that topic data collection component  214  and topic statistics generator  222  generate information and statistics for Jane Deer that show that she clicked on someone else&#39;s query, instead of input it herself. Analyzing the text of the query and returning results, etc., is performed in the same way as shown in  FIG. 8 . This is indicated by block  552  in  FIG. 9 . 
     If, at block  550 , it is determined that the click was not on another&#39;s query, then processor  18  determines whether the click was on a search result input by another. This is indicated by block  554  in  FIG. 9 . If so, then system  8  performs the same processing as shown in  FIG. 4A , for a click on a result. This is indicated by block  556  in  FIG. 9 . 
       FIG. 9  also shows that system  8  can illustratively perform additional processing, based on clicks, as well. It is not only queries input by users that indicate the interests of the users, but the results that the user interacts with (e.g., clicks on) also indicate the interests of a given user. Therefore, query/results analyzer  216  can perform linguistic analysis on the text of a result that was clicked on to identify the subject matter corresponding to that result. This is indicated by block  558 . Those subject matter areas are output to interest tracking component  224  to assist in tracking the interests of the present user. This is indicated by block  560  in  FIG. 9 . The operation of interest tracking component  224  is discussed in greater detail below with respect to  FIGS. 10 and 11 . 
     If, at block  554 , it is determined that the click was on some other portion of the user interface display, then processing proceeds with respect to block  328  in  FIG. 7 . This is indicated by block  562  in  FIG. 9 . 
     Processing Other Inputs 
     Referring again to  FIG. 7 , if, at block  320 , it is determined that the input  210  is some other type of input, the appropriate action is simply taken, as indicated by block  336  in  FIG. 7 . For instance,  FIG. 12  shows a flow diagram illustrating the operation of system  8  when the input  210  is a message. In that case, the message is sent to messaging and notification system  218  and output to the desired recipient. This is indicated by blocks  312 ,  314  and  316 . 
     Appropriate processing is performed for any other input  210  as well. For instance, if the user clicks on the “comment” button and inputs a textual comment, then processor  18  controls system  8  to receive the textual input, as the comment, through user interface component  22  and identify recipients that are to receive it and then distribute it to those recipients. 
     It should also be noted that system  8  can include other things as well. For instance, though the description has proceeded with respect to system  8  receiving mouse clicks, textual inputs, etc., other input and output modes could also be used. User interface component  22  can receive speech input from the user and perform speech recognition, and system  8  can be controlled in that way as well. Alternatively, the speech recognition can be performed in public search system  10 . Similarly, user interface component  22  can include text synthesis components that synthesize text into speech and communicate audibly with the user. A wide variety of other changes can also be made to the system. 
     Data Store  20   
       FIG. 8A  illustrates one embodiment of a number of different items of information that can be stored in topic and statistics data store  20 . Of course, the items of information shown in  FIG. 8A  are all related to an individual user. Therefore, it can be seen that data store  20  illustratively stores all of the queries  60  input by a given user, the clicks on other person&#39;s queries and clicks on search results as indicated by  400  in  FIG. 8A , all of a user&#39;s followers  402 , all of the comments  404  posted by the user, any friends  406  of the user (if friends are separately designated from followers) the user&#39;s interests, both explicitly indicated by the user, and implicitly derived by interest tracking component  224 , as indicated by block  408  in  FIG. 8A , post thread statistics associated with posts that were generated by the user, and the user&#39;s status. This is indicated by block  410  in  FIG. 8A . Data store  20  also indicates a user&#39;s status as an expert or a guru as indicated by blocks  412  and  416  in  FIG. 8A . Data store  20  is shown for exemplary purposes only and other types of data can be stored as well. 
     Post Thread Statistics 
     Topic statistics generator  222  illustratively generates post thread statistics which indicate the number of times that the user&#39;s posts have been interacted with (such as clicked on or re-posted) by others. For instance, if John Doe submits a query  60  which is posted to the public stream  52  of his followers, and one of the followers (such as Jane Deer) clicks on the query  60 , then the query will also be posted on the public stream  52  of all of the followers of Jane Deer. Thread statistics  410 , which are generated by topic statistics generator  222 , track how many times the user&#39;s posts have been posted and re-posted in system  8 . 
     In order to do this, each of the queries (or posts) is stored in data store  20 , in one exemplary embodiment, according to a data structure such as that shown in  FIG. 8B . It can be seen that the post itself,  500 , has an associated root identifier (ID)  502 , a relative identifier (ID)  504 , and a path if relative identifiers (IDs)  506 . The root identifier (ID)  502  for the post is a unique identifier associated with the author, or originator, of the post. In the example being discussed, the root (ID)  502  is that associated with John Doe. 
     The relative ID for this post  504  is associated with someone downstream of John Doe who re-posted John Doe&#39;s original post. In the example being discussed, the relative ID  504  corresponds to Jane Deer. The path of relative IDs  506  extends from the relative ID (the most recent poster) for this post to the root ID  502 . For instance, assume that Jane Deer&#39;s relative ID is 14. Then the path of relative ID&#39;s  506  is 14, 1. If one of Jane Deer&#39;s followers then re-posts the query, the root ID for the re-posted query stays the same (1), the relative ID belongs to the follower of Jane Deer (say the relative ID for that follower is 28) and the path of relative ID&#39;s is 28, 14, 1. In this way, statistics generator  224  not only keeps track of who originated the posts, but it keeps track of the number of times the post has been re-posted. It also keeps track of the path of followers through which the post traveled. 
     These types of post thread statistics are of interest for a number of reasons. For instance, on some social networking sites, when a post of an individual is widely disseminated, it is referred to as “going viral.” There can be some prestige associated with a post that has gone viral. However, it can be difficult to identify the originator of the post. Therefore, using statistics generator  222  and the data structure shown in  FIG. 8B  (or some similar data structure) system  8  can easily track the originator of viral posts, and give the originator credit for the post threads. 
     Expert and Guru Status 
     Expert status  412  and guru status  416  are illustratively assigned to user&#39;s that are widely followed in a given topic area. For instance, if John Doe is widely followed in the topic area of “Paul Bunyan” and, in fact, has a sufficient number of followers, then John Doe may be awarded the expert status  412  in the topic area of Paul Bunyan. If John Doe happens to be the most followed user in that subject matter area, then John Doe is illustratively awarded the most followed (e.g., guru) status  416 . This is indicated in data store  20  as well. 
     Particular ways in which expert status  412  and guru status  416  are conveyed can vary widely. As discussed above they may simply have to do with the number of followers a given user has on a given subject. Of course, they may also be determined based on the post thread statistics (the number of posts or re-posts attributed to that user) on that given topic or subject matter area. Other techniques can be used as well, in order to recognize someone as an expert or guru. For instance, a community of users can vote on that status by entering appropriate inputs on interface  22 , or the status can be awarded in other ways as well. 
     In any case, data collection component  214  and topic statistics generator  222  can illustratively collect or generate the information necessary to award any desired status (for a topic or subject matter area) to one or more users, based on popularity, or other statistics. 
     Interest Tracking 
     To discuss interest tracking reference is again made to  FIG. 7 . Recall that a user can provide an input that explicitly identifies that the user is interested in something or someone. This is referred to as an explicit interest indication. If, at block  320 , it is determined that the input  210  is an explicit interest indication (shown at block  326 ) then explicit interest tracking is performed as indicated by block  334 .  FIG. 10  shows a simplified block diagram of one embodiment of interest tracking component  224 , and  FIG. 11  shows one embodiment of its operation.  FIG. 10  shows that interest tracking component  224  includes an implicit interest tracking component  580  and an explicit interest tracking component  582 . Explicit interest tracking is discussed below, while the operation of implicit tracking component  580  is described first. 
     As briefly discussed above with respect to  FIG. 6 , interest tracking component  224  receives a variety of information and operates on that information to implicitly identify interests of a given user. By implicitly identifying interests, it is meant that the user has not made an explicit interest indication indicating that the user is interested in a certain subject matter area or person but instead component  518  implicitly derives that information based on analysis of a user&#39;s activity. 
     For instance, a user may explicitly indicate that he or she is interested in a topic by providing an appropriate input through user interface component  222 . However, implicit interest tracking component  580  takes other inputs by the user and analyzes them to implicitly define the interests of the user. The information shown in  FIG. 10 , that is considered by component  518 , is exemplary only, and other or different information can be used as well. However, the exemplary information shown in  FIG. 10  includes textual information from queries  584 , textual information derived from posts that the user has clicked on  586 , and textual information from subject matter that the user has “liked” or indicated a preference for  588 . The textual information from queries  584  can be the results of a grammatical analysis performed on the queries posted by the user, and may include (by way of example) keywords or predefined topics or people of interest to which the queries relate. Similarly, the information from clicks  586  can be grammatical information derived from queries that have been clicked on by the user, or results that have been clicked on by the user. In addition, the information from likes  588  can be generated from posts which the user has “liked” as discussed above with respect to  FIGS. 3A-5 . Alternatively, of course, tracking component  224  can receive the raw text from those sources and submit it to query/results analyzer  216  (or another component) for grammatical analysis as well. This is indicated by optional block  602  in  FIG. 11 . 
     Once implicit interest tracking component  580  receives grammatically analyzed text (as indicated by blocks  600  and  602  in  FIG. 11 ), it, or another component, illustratively performs statistical analysis on content words of that text to identify implicit topics of interest. This is indicated by block  604 . For instance, if implicit interest tracking component  580  simply receives a set of keywords that have been grammatically extracted from the textual sources, then implicit interest tracking component  580  illustratively counts and stores the frequency of occurrence of those words in the textual inputs. By identifying the content words that are most used or interacted with by a given user, implicit interest tracking component  580  can map those words to topics of interest that are recognized in system  8 , or it can generate new topics of interest. For instance, if keywords that correspond to a particular subject matter (such as the words “Paul Bunyan”) are frequently used, implicit component  518  can identify “Paul Bunyan” as a particular subject matter area of interest for the user In addition, if the analyzed text includes the name of another user (with sufficient frequency) then that user may be identified as an interest of the current user. Similarly because data store  20  stores data that identifies other users that have similar interests to the present user, interest tracking component  224  can implicitly identify those other users as possible people for the current user to “follow”. Performing the statistical analysis on the content words and other users is indicated by block  604 . The same type of analysis can be performed on topics of interest (as opposed to content words) if the topics of interest are provided instead of just the content words. 
     Interest tracking component  224  also includes explicit interest tracking component  582 . In one illustrative embodiment, a user can input an explicit interest indication by marking certain textual items, explicitly, as being items of interest to the user. For instance, the user can use the # tag before, or after, or surrounding, textual words to explicitly indicate that the user is interested in topics that correspond to those words. 
     This can also be used to remove certain textual items from the implicit interest tracking analysis. For instance, if the user inputs a query which includes the term “White House”, the user may be referring to president&#39;s residence in Washington D.C., or to houses that are white in color, generally. If the text is not explicitly marked by the user, then implicit interest tracking component  580  may either analyze the text and believe that the user is interested in the president&#39;s residence, or in white houses in general. However, if the user explicitly marks the text as follows “#white# #house#” then the term “White House” will be removed from the implicit tracking analysis performed by component  580 , and the terms “white” and “house” will be input as specifically, and explicitly, marked interests  584  to explicit component  582 . Explicit component  582  can correlate the marked interest  584  to already defined topics of interest, or it can use that information to define a new topic of interest that the user can follow. 
     After it has received the textual inputs and performed the linguistic and statistical processing, interest tracking component  224  generates a list of the top N interests  585  which have been derived for the given user. The top N interests will, of course, include all of those interests which have been explicitly indicated by the user. However, they may also include a number of topics of interest that have been implicitly derived by component  580 . The number, N, of topics of interest that are output and stored for a given user can be empirically set, or it can be chosen by the user, or it can simply be selected at random or any other way. For instance, in one embodiment, interest tracking component  224  keeps track of the top 50 topics of interest for a given user, whether they are implicitly derived or explicitly input. 
     Once all the inputs have been analyzed, interest tracking component  224  combines the implicit topics of interest with the explicit topics of interest, as indicated by block  606 , and updates data store  20  to indicate the new or revised topics of interest, and also outputs them for review by the user. This illustratively includes a separate list of other users who share the same topics of interest. This is indicated by block  608 . Interest tracking component  224  can do this in a number of different ways. For instance, interest tracking component  224  can automatically update the “Following” list on the user&#39;s home page to include any newly identified topics of interest (subject matter areas or people), and to delete old topics of interest, which no longer fall within the top N topics of interest  585  output by component  224 . In this way, system  8  will automatically begin posting new posts to the public stream  52  of the user, to reflect the new implicitly derived and explicitly indicated topics of interest. Of course, the user may not wish the system to automatically update his or her topics of interest in the “Following” list. Therefore, alternatively, interest tracking component  224  may simply provide an output that indicates to the user that certain changes in the user&#39;s topic of interest are suggested, and allow the user to accept or reject those changes, either individually, or as a group. This is indicated by block  610  in  FIG. 11 . Component  224  illustratively keeps updating the top N list  585  as the user uses system  8 . In this way, the user can easily ensure that the public stream  52  contains posts that are of current interest to the user. 
     Enterprise Search 
     It should be noted that while system  10  is described above as being completely public, it can also be public within a given context. For instance, system  10  can be deployed behind a firewall so only potential recipients that also reside behind the firewall will receive topic feed  70 . This allows those in, for example, an organization to share search activity but keep that information behind the firewall. Thus, employees of a company can collaborate and have frank discussions and conduct shared search activity about competitors without providing the competitors with access to sensitive information. System  10  can also be deployed on even a smaller scale, such as within a work group. 
     Illustrative Computing Environment 
       FIG. 13  shows one illustrative computing environment where system  8  can be employed. The computing environment can be employed as public search system  10 , user interface component  22 , or both. Similarly, those components can be deployed on other type of computing devices, such as handheld devices, mobile devices, laptop devices, cellular telephones, personal digital assistants (PDA), etc. 
     With reference to  FIG. 13 , an exemplary system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820  (which can act as processor  18 ) a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
     Computer  810  typically, but not always, includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  110  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media (which is not included in computer storage media) typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 13  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 13  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  851  that reads from or writes to a removable, nonvolatile magnetic disk  852 , and an optical disk drive  155  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and magnetic disk drive  851  and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 13 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 13 , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . Operating system  844 , application programs  845 , other program modules  846 , and program data  847  are given different numbers here to illustrate that, at a minimum, they are different copies. 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  can be operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG. 13  include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. Computer  810  can be used in many different applications. For instance, by way of example, and without limitation, it can be used for general purpose computing, data communication applications, in avionics, military applications or electronics, or shipping electronics. Of course, computer  810 , or portions thereof, can be used in many other applications as well. 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 13  illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     Computer  810  may also act as one of the servers or server computers discussed with respect to  FIG. 13 . Also, it should be noted that many of the components shown in  FIG. 13  can be fully implemented in silicon, or partially implemented in silicon. The particular configuration shown in  FIG. 13  is exemplary only. The embodiments described above in  FIGS. 1-12  can also be implemented by the processor and using memory and other components in  FIG. 13 . 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.