WO2016069725A1

WO2016069725A1 - Apparatus and method for analyzing content posted in social media

Info

Publication number: WO2016069725A1
Application number: PCT/US2015/057778
Authority: WO
Inventors: Adam Cohen
Original assignee: Hashtracking, Inc.
Priority date: 2014-10-28
Filing date: 2015-10-28
Publication date: 2016-05-06

Abstract

A method for analyzing content associated with social media posts may include obtaining, by a processing device (2001) over a communication network (2000), a plurality of social media posts posted from at least one communication device (110) and indexed by a selected indexing element; determining, by the processing device (2001), whether any of the plurality of social media posts include content, wherein the content includes at least one of an image, a video, a sticker, an emoji, text or a background in which text is presented; performing, by the processing device (2001), a color analysis on the content; generating, by the processing device (2001), color data as a result of the color analysis; and reporting, via the communication network (2000) to another communication device (120), the color data.

Description

APPARATUS AND METHOD FOR ANALYZING CONTENT POSTED IN SOCIAL

MEDIA

CROSS-REFERENCE TO RELATED APPLICATIONS

[ 0001 ] The present application claims the benefit of the filing date of U.S. Provisional Patent Application No. 62/069,592 filed October 28, 2014, the disclosure of which is hereby incorporated herein by reference .

BACKGROUND

[ 0002 ] As social media continues to grow and expand via the Internet and mobile applications, metrics have been developed to track what is trending on social media. Determining what is trending provides statistical data exhibiting the rise and fall of a word, phrase, or topic. For example, analytics services can track a word, phrase, or topic to indicate the rise and fall of the word, phrase, or topic; determine the location of where the word, phrase or topic is occurring most frequently, etc.

[ 0003 ] However, there is still a need for improved techniques to determine a characteristic or feature of content which is part of or associated with what is trending on or posted to social media sites.

SUMMARY

[ 0004 ] In accordance with an aspect of the present disclosure, a method for analyzing content associated with social media posts is described. The method may include: obtaining, by a processing device over a communication network, a plurality of social media posts posted from at least one communication device and indexed by a selected indexing element; determining, by the processing device, whether any of the plurality of social media posts include content, wherein the content includes at least one of an image, a video, a sticker, an emoji, text or a background in which text is presented; performing, by the processing device, a color analysis on the content; generating, by the processing device, color data as a result of the color analysis; and reporting, via the communication network to another communication device, the color data.

[0005] In some examples, the color analysis may include determining a color composition of the content. According to this example, a color may be filtered based on its association with the post. For example, red and yellow may be filtered from posts related to McDonalds.

[0006] In other examples, the color analysis may determine a geographic location associated with each of the social media posts. Further, the color analysis may determine a color preference associated with a topic of the social media posts.

[0007] In accordance with an aspect of the present disclosure, a non-transitory computer-readable medium may be configured to store instructions for analyzing content associated with social media posts, that when executed by one or more processors, perform a method. The method may include obtaining, over a communication network, a plurality of social media posts posted from at least one communication device and related to a topic, wherein the topic is selected based on at least one of a subject that is trending, a hashtag, a cashtag, or a username; determining whether any of the plurality of social media posts include content, wherein the content is selected from at least one of an images, a video, a sticker, an emoji, text, or a background in which text is presented; filtering the plurality of social media posts that include the content; performing a color analysis of the content associated with the plurality of social media posts; generating data as a result of color analysis; and reporting, via the communication network to another communication device, the data. [0008] In accordance with an aspect of the present disclosure, an apparatus for analyzing content associated with social media posts may include circuitry configured to control: acquiring a plurality of social media posts related to a topic, wherein the topic is selected based on at least one of a subject that is trending, a hashtag, a cashtag, or a username; determining whether any of the plurality of social media posts includes content, wherein the content is selected from at least one of an image, a video, stickers, an emoji, text, or a background in which text is presented; performing a color analysis of the content associated with the plurality of social media posts; generating a report based on a result of the color analysis; and communicating, via a communication network, the report to a communication device.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figures 1A and IB illustrate systems according to examples described herein;

[0010] Figure 2 shows an apparatus according to one example ;

[0011] Figure 3 illustrates an example of a social media feed;

[0012] Figure 4 illustrates another example of a social media feed;

[0013] Figure 5 shows a flowchart according to one example described herein for analyzing trending data;

[0014] Figure 6 shows a flowchart according to an example of the present disclosure for obtaining analytic data related to a trending topic;

[0015] Figure 7 shows a flowchart according to another example of the present disclosure for performing color analysis of content associated with social media posts; and

[0016] Figures 8A and 8B show examples of reports generated from the analytic data and color analysis . DETAILED DESCRIPTION

[ 0017 ] Social networking has exploded as people have been able to connect to social networking sites from their mobile devices, such as smart phones and tablets. In this regard, social networking users can provide instantaneous updates with respect to news updates, their general well-being, or even what they are eating (e.g., "#foodporn") . As a result of social networking and social media posts, analytics have been developed to track popular keywords, phrases, or topics.

[ 0018 ] A common technique used to highlight popular keywords, phrases, or topics is the hashtag (#) . Hashtags allow posts or messages to be categorized or, otherwise, indexed. While the hashtag is one common technique, other indexing elements, similar to or in the form of tags, such as cashtags ($), may be used. For the purposes of this disclosure, reference to hashtags may be interpreted as referring to any type of indexing technique used in social media posts or messaging.

[ 0019 ] In this regard, hashtags may be used to search and subsequently filter social media posts or messages. For example, a user may enter "#Yankees" to filter social media posts so that those posts related to the Yankees may be received. In other examples, the hashtag may be used by the user so as to continuously receive updates about the New York Yankees baseball team. Additionally, analytics can be determined related to the hashtag #Yankees. These analytics may include who posts the most with the #Yankees hashtag, how many followers a user who posts with the #Yankees hashtag has, where the most #Yankees hashtags come from, what is being stated in posts and messages with the #Yankees hashtag, and when posts and messages with the #Yankees hashtag occur.

[ 0020 ] According to the examples described herein, the apparatus and method according to the disclosure may further analyze social media posts and messages that include content, such as images, pictures, animated gifs, video, stickers, illustrations, or emojis. Alternatively, posts and messages may include colored text (i.e., text in a color other than black) or text with colored background (i.e., a non-white background) . Specifically, a color analysis may be performed on the content. In this regard, additional color information separate from the analytics obtained with respect to the hashtag may be gathered. This color information may indicate the color composition of the content; colors associated with the hashtag itself or the content associated with the hashtag; linking of the colors of the content to the hashtag; association of a color with a geo-location ; and determination of a color preference associated with the hashtag. The color information may be aggregated into a report, which is subsequently transmitted via a communication network to the user for review at a user ' s communication device .

[ 0021 ] Based on the information in the report relying upon color information obtained from color analysis, the user may be able to discern additional information from the analytics gleaned from the social media post. For example, the color information may provide a recommendation for images to include for targeted advertising or to reach a greater number of users . Additionally, the color information may provide insight into images that users find aesthetically pleasing.

[ 0022 ] Turning to FIG. 1A, a social media system 100 may include a first user device 110, a second user device 120, a network 2000, a first server 2001, and a second server 2002.

[ 0023 ] A user may use the first user device 110 to post to a social media site, where the device 110 may be a mobile device, such as a smart phone, tablet, MP3 player, etc. Posting to a social media site may include status updates, images, video clips, or a combination thereof. These posts and messages may include hashtags, keywords, phrases, or topics common to categorize or index the post. Accordingly, the first user device 110 may communicate with a social media site via a wireless connection, such as Wi-Fi or a communication channel (e.g., 3G, GSM, CDMA, LTE, etc.) Additionally, the mobile device may have a camera. In this regard, the first user device 110 may capture images and upload the images to the social media site.

[ 0024 ] Another user may use the second user device 120 to post to a social media site, where the device 120 may be a computing device, such as a laptop, a desktop, a smart TV, etc. Similar to the first user device 110, the second user device 120 may be configured to be operable to post status updates, images, video clips, or a combination thereof. These posts and messages may also include hashtags, keywords, phrases, or topics common to categorize or index the post or messages. The second user device 120 may communicate with the social media site via a wired or wireless connection. Additionally, the second user device may include a camera or a port to upload images or video to the device. In this regard, the second user device 120 may be configured to post uploaded images or video to the social media site.

[ 0025 ] The network 2000 may include any number of selected configurations. Further, the network 2000 may employ various protocols including, but not limited to, the Internet, intranets, virtual private networks, local Ethernet networks, cellular and wireless networks (e.g., Wi-Fi) HTTP and SMTP, and various combinations of the foregoing. Accordingly, the network 2000 may be any communication network that allows the first user device 110 or the second user device 120 to connect to and communicate with a social networking site, the first server 2001, or the second server 2002. While FIG. 1 only depicts two user devices and two servers, it will be appreciated by one of ordinary skill in the art a typical system may include more servers and a larger number of user devices .

[0026] The first server 2001 may be a server or group of servers that hosts a social networking site, such as Facebook®, Twitter®, Instagram®, etc. The first user device 110 may be operated to create an account on the social networking site hosted on the first server 2001. The user of the first user device 110 may then, using the device 110, log into his social networking account, via network 2000, to share status updates, pictures, images, stickers, videos, music, or any combination thereof or send messages, such as instant messages or direct messages (collectively "posts")- The first user device 110 may be configured to, based on input from the user via an input device of the device 110, such as a keyboard, mouse or the like, or automatically, add at least one hashtag, keyword, phrase, topic, or any combination thereof to a post, in order to categorize or index the post such that other users can search and view posts with the attached hashtag, keyword, phrase, or topic. This indexing feature will be discussed in greater detail below in connection with text accompanying the description of FIGS. 3 and 4.

[0027] The second server 2002 may be a server or group of servers that hosts a social media analytics service. A user, such as via the device 110, may access the second server 2002, via network 2000, to acquire statistical data with respect to hashtags, keywords, phrases, or topics inputted by the user. The second server 2002 may then access the first server 2001 to obtain the requested statistical data. The data obtained by the second server 2002 may be returned to the user device 110 for review by the user. [ 0028 ] Although FIG. 1A shows the first server 2001 and the second server 2002 as separate servers, one of ordinary skill in the art would recognize that the first server 2001 and the second server 2002 may be co-located at a single location. Further, a skilled artisan would recognize that the functions described above may be embodied on a single machine. That is, a single server or cluster of servers may host both the social networking site and the analytics server.

[ 0029 ] FIG. IB shows another example of a social media analytics system according to another embodiment. FIG. IB includes a first social media site 1010, a second social media site 1020, a third social media site 1030, a network 2000, and a social media analytic service 2012. The social media analytic service 2012 may include at least a plumbing server 2010, a node server 2020, a database server 2030, and a web server 2040.

[ 0030 ] The social media sites 1010, 1020, and 1030 may be a server or group of servers that hosts a social networking site, such as Facebook®, Twitter®, Instagram®, etc. As discussed above, users, via respective user devices, may create accounts to share status updates, pictures, images, stickers, videos, music, or any combination thereof. Further, the user device may be operated to add at least one hashtag, keyword, phrase, topic, or any combination thereof to a post in order to categorize or index the post such that other users can search and view posts with the attached hashtag, keyword, phrase, or topic.

[ 0031 ] The network 2000 may include any number of selected configurations. Further, the network 2000 may employ various protocols including, but not limited to, the Internet, intranets, virtual private networks, local Ethernet networks, cellular and wireless networks (e.g., Wi-Fi) HTTP and SMTP, and various combinations of the foregoing. Accordingly, the network 2000 may be any communication network that allows the social media analytics service 2012 to access social media sites 1010, 1020, and/or 1030.

[ 0032 ] As noted above, the social media analytics service may include a plumbing server 2010, a node server 2020, a database server 2030, and a web server 2040. One of ordinary skill in the art would recognize that these servers are merely illustrative. The social media analytics service 2012 may include more or fewer servers as necessary. Moreover, the functions of the servers may be combined on a single computing device .

[ 0033 ] The plumbing server 2010 may be configured to connect to the social media sites 1010, 1020, and/or 1030. The plumbing server 2010 may be configured to collect data from the social media sites 1010, 1020, and/or 1030. The data collected may be based on trending topics or in response to a query from a user supplied via a user device. The collected data may include status updates, pictures, images, stickers, videos, music, or any combination thereof. This collected data may be transmitted (i.e., pushed) to the node server 2020 for further analysis .

[ 0034 ] The node server 2020 may be configured to receive the collected data from the plumbing server 2010. The node server 2020 may perform analysis on the collected data received from the plumbing server 2010 to obtain analytic data. The analysis performed by server 2020 is discussed in greater detail below in connection with the text accompanying the description of FIGs. 5-7.

[ 0035 ] After analyzing the social media posts, the node server 2020 may transmit the analytic data to the database server 2030 for storage. The database server 2030 may store analytic data related to trending topics for a predetermined amount of time. Additionally, the database server 2030 may catalog hashtags, topics, keywords, and phrases. According to some examples, the node server 2020 and the database server 2030 may be on the same physical machine.

[0036] The web server 2040 may provide an interface for user devices to access the social media analytics service 2012. For example, a user device may be used by the user thereof to access a website hosted on the web server 2040 to perform analytics on a hashtag, keyword, phrase, or topic. Accordingly, the user may specify criteria for the analysis at the user device. The web server 2040 may connect to the node server 2020 via an application programming interface (API) with the criteria. The node server 2020 may provide the criteria to the plumbing server 2010 to collect data, such as posts, that corresponds to the criteria specified by the user. After collecting the data, the plumbing server 2010 may transmit the collected data to the node server 2020. The node server 2020 may perform analysis on the collected data and return analytic data obtained from the analysis to the web server 2040 to be presented to the user. Additionally, or in the alternative, the node server 2020 may provide the analytic data to the database server 2030 for storage.

[0037] Referring to FIG. 2, an apparatus for analyzing content associated with social media posts may be configured as a server 200. The server 200 may be representative of any of the servers of the social media analytics service 2012 shown in FIG. IB. The server 200 may include a processor 210, a memory 220, a network interface controller 240, and an analytics engine 250 all communicatively coupled together via bus 230.

[0038] The processor 210 may be any conventional processor, multiprocessor, or multi-core processor. Additionally, the processor 210 may be a dedicated controller or semiconductor device, such as an Application-Specific Integrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA) .

[ 0039 ] The memory 220 may be any type of computer-readable media capable of storing information accessible by the processor. In this regard, computer-readable media may include a hard-drive, a network storage device, a memory card, flash drive, ROM, RAM, DVD or other optical disks, as well as other write-capable and read-only memories.

[ 0040 ] Further, one of ordinary skill in the art will recognize that the memory 220 may be a hard drive or other storage media located in a server farm of a data center . Additionally, the memory 220 may be located in a storage area network (SAN) that is accessible by the processor 210. References to a processor, a server, or a memory may be understood to include references to a collection of processors, servers, or memories that may or may not operate in parallel.

[ 0041 ] The information stored in the memory 220 may include data and instructions. The instructions may be directly executed by a processor or indirectly executed by the processor, such as via a script or add-in module. The instructions may be stored as computer code in the memory 220. Further, the terms "modules," "instructions," and "programs" may be used in place of each other. The functions, methods and routines of the instructions are explained in greater detail below .

[ 0042 ] The bus 230 may include any data communication bus that provides a communication pathway for the components of the server 200. For example, the bus 230 may include a PCI bus or any other known bus for connecting the peripherals of a computing device. In alternative examples, the bus 230 may be a network interconnecting a plurality of nodes in a server cluster. One of ordinary skill in the art would understand the types of buses from the examples described herein and will not be discussed in further detail.

[0043] A network interface controller (NIC) 240 may include any hardware component that connects the server 200 to a computer network. In this regard, the network interface controller 240 may provide a wired or wireless connection to the network 2000. According to some examples, the NIC 240 may be an Ethernet adapter, optical adapter, or any other known component to provide a wired connection to a network. According to other examples, the NIC 240 may be a Wi-Fi adapter, a near-field communication adapter, a short-range wireless connection or any other known device for providing wireless connectivity.

[0044] The analytics engine 250 may be software, hardware, or a combination thereof configured to provide a statistical analysis of the social media posts. The analytics engine 250 may be available at a social media site, such as Facebook®, Twitter®, or Instagram®. In other examples, the analytics engine 250 may be an add-in module to a social media site or a web browser. Alternatively, the analytics engine 250 may be located at an analytics server that accesses various social media sites to obtain statistics related to trending social media posts. One of ordinary skill in the art would recognize the capabilities of the analytics engine 250 from the examples described herein and they are not described in greater detail herein .

[0045] The analytics engine 250 may provide a search function that allows input from a user device of a keyword, phrase, topic, hashtag, username, or any combination thereof. The analytics engine 250 may also allow input of additional information, such as geographic locations, time restrictions, etc. In some examples, the analytics engine 250 may allow specifying which social media sites to search. The analytics engine 250 may allow a combination of terms to be used in a Boolean search. Alternatively, the analytics engine 250 may use proximity connectors to perform a search.

[0046] According to other examples, the analytics engine 250 may be further configured to provide a statistical analysis of the social media posts. In this regard, the analytics engine 250 may generate analytic data indicating pattern detection, how often a user posts a hashtag, the number of users a follower may have, the location or locations where the hashtag is occurring most frequently, dates and times that the hashtag is frequently posted, etc.

[0047] In other examples, the analytics engine 250 may also be configured to perform and provide results of a color analysis of images. In operation, the analytics engine 250 may provide color analysis of images. That is, the analytics engine 250 may determine the color composition of an image. Additionally, the analytics engine 250 may provide additional analysis of the image, such as filtering certain colors; linking the colors to a hashtag, keyword, phrase, topic, user, or username; linking the image to a hashtag, keyword, phrase, topic, username, or user; linking the colors to a geographic location; determining a color preference associated with the hashtag, keyword, phrase, topic, username, or user; or any combination thereof.

[0048] In operation, the analytics engine 250 may receive a hashtag, username, word, phrase or topic as input from a user device, based on entry of same by the user at the user device. The analytics engine 250 may then retrieve posts from a search having, as a search term, one or more of the inputs received from the user device, where the posts contain the search term or a formative thereof. These posts may be compiled into a file by the engine 250. The analytics engine 250 may then perform further analysis, including color analysis, on the posts compiled in the file. The analytics engine 250 may provide real-time statistics related to the social media posts that contain the search terms .

[0049] Turning to FIG. 3, an exemplary social media feed is shown. In this regard, four social media posts are shown all with the common hashtag "#re2pect." The first post 310 is merely a status post that tags another user (@AboveSam) . The second post 320 includes a status post that includes content, specifically a composite image of a New York Yankee hat from the front and the back. The third post 330 includes a status post referring to the image posted.

[0050] Referring to FIG. 4, another social media feed is illustrated, specifically, the "What's Trending" page from Twitter®. According to FIG. 4, three social media posts, namely, a first post 410, a second post 420, and a third post 430, and a "What's Trending" sidebar 440 are shown with different hashtags . The first post 410 is a status post with the hashtag " #RIPRobinWilliams . " The second post 420 is a status update that includes content, specifically an image of Benedict Cumberbatch and Shere Khan from the Disney's Jungle Book. The second post 420 includes the hashtag

" #JungleBookOrigins . " The third post 430 is a status post with the hashtag "RIPRobin Williams." The "What's Trending" sidebar 440 lists the top trending subjects (i.e. "ICE BUCKET CHALLENGE by Rubius") and hashtags (e.g.

"#OneYearSinceThisIsUsPremiere" ) . In this regard, social media sites and current analytic tools provide resources for tracking hashtags, topics, keywords, and phrases. However, current social media sites and analytic tools do not provide a color analysis of the images posted to a social media feed.

[0051] FIG. 5 illustrates a flowchart of a process 500 according to one example of the present disclosure. At block 510 of the process 500, a user may input at a user device, such as device 110, a hashtag, username, word, phrase or topic, and the device 110 may communicate, via a communication network, the input information to a search function provided by the analytics engine discussed above. As discussed above, the search function may be provided at an analytics server that accesses various social media sites to obtain statistics related to the hashtag, username, word, phrase or topic inputted by the user. While the examples described herein refer to hashtags, one of ordinary skill in the art would recognize that a user may choose to enter a keyword, phrase, topic, or username.

[ 0052 ] In addition to the input of a keyword, phrase, topic, hashtag, or username, the user may also input additional information to be provided via the user device to the analytics server. For example, the user may be able to set additional limitations for the search function, such as time, geographic location, posts with links, posts with content, etc. One of ordinary skill in the art would recognize the additional parameters that could be included in the search function and they are not discussed in greater detail herein.

[ 0053 ] After receiving input from the user device, a search function, which may be implemented at the service 2012, may proceed to obtain social media posts from at least one social media site in block 520. For example, an analytics service may receive a hashtag from a user device. The analytics service may access several social media sites, such as Facebook®, Twitter®, or Instagram®, to retrieve posts that include the hashtag entered by the user at the user device.

[ 0054 ] In block 530, the service 2012 may generate analytic data representative of analytics performed on the social media posts obtained by the search function. The analytic data may indicate pattern detection, how often a user posts a hashtag, the number of users a follower may have, the location or locations where the hashtag is occurring most frequently, dates and times that the hashtag is frequently posted, etc. The analytic data obtained in block 530 will be discussed in greater detail in the text accompanying the description of FIG. 6.

[0055] In block 540, an analytics engine at the service 2012 may perform a color analysis of the content associated with each of the social media posts to generate color analysis data. As noted above, the analytics engine may analyze the content in posts to determine: the color composition; filter certain colors; linking of colors to a hashtag; linking of colors to a geographic location; a color preference associated with the hashtag; or any combination thereof. The color analysis and color analysis data generated therefrom will be discussed in greater detail below.

[0056] In block 550, the service 2012 may aggregate the analytic data and the color analysis data and generate a report from the aggregated data. The report may contain information that is unobtainable from the analytic data by itself. For example, the report may indicate that certain users post images of a particular color, thereby having a preference for the particular color. In another example, the report may indicate that a certain hashtag is associated with a color or a set of colors . Alternatively, the report may indicate that separate posts with the same hashtag may have content with different color compositions. Accordingly, the report may indicate which color composition has been shared more. Furthermore, the report may indicate that a color composition for a particular hashtag is preferred in certain geographic locations over other color compositions. The examples described herein are merely illustrative and one of ordinary skill in the art would understand other embodiments from the examples described herein.

[0057] In block 560, the report may be provided to the user device from the service 2102 over a communication network. For example, the service 2012 may be configured to email the report to the user, who may retrieve the email at the user device. Alternatively, the service 2012 may communicate to the user a link to the report, such as via email or text to the user device. The link may permit the user device to connect to the analytics service 2012 and obtain from the service 2012 a report of the results of the analytics and color analysis. According to this example, the report may provide real time tracking of certain analytic data so that the user at the user device can tailor posts and content so that they are viewed by more social media users.

[0058] FIG. 6 illustrates a flowchart of a process 600 for determining analytic data related to social media posts. In block 610, the service 2012 may receive, at a search function thereof, search terms from a user device over a communication network. As discussed above, these search terms may include a hashtag, keyword, phrase, topic, username or any combination thereof. Additionally, the user of the user device may specify from which social media sites to retrieve posts. The search function may then proceed to obtain posts with the relevant search terms from the selected social media sites.

[0059] In block 620, the service 2012 may perform analytics on the social media posts to detect any patterns. These patterns detected may include community information. Further, these patterns detected may indicate the dissemination of information. For example, a news headline may be posted by Twitter® user "@BBCNews" that includes the hashtag "#Obama." According to this example, information about who, where, and when the news headline is reposted by other Twitter® users may help to establish patterns that would be useful in ensuring posts are seen by a large number of users .

[ 0060 ] In block 630, the service 2012 may determine which users have the most posts for a trending hashtag, keyword, phrase, or topic. This information may help to determine which users are supporting or propping up a trending topic. Alternatively, this information may help to determine which users may have a larger influence on their followers. For example, the hashtag "#re2pect" may be homage to New York Yankees shortstop Derek Jeter's last year. Accordingly, Twitter® users "@MLB" and "@Yankees" may have a large number of posts with the "#re2pect" hashtag from posts that each of those users posted, in addition to reposts from other users. Therefore, according to the process 600, the service 2012 may determine that "@MLB" and "@Yankees" may have a larger influence on baseball fans and Derek Jeter fans.

[ 0061 ] In block 640, the service 2012 may determine which users have the most followers. This determined information may help to determine popular and influential users . In this regard, these users may be targeted more frequently so that posts and content may reach a larger number of followers.

[ 0062 ] In block 650, the service 2012 may obtain location- information related to the social media posts . In this regard, a network address, such as an IP address, from a user device where the user created the post may be recorded. Accordingly, the network address may be used to provide an approximate location of where the post was created. Alternatively, the user device may include a GPS receiver. In this regard, latitudinal and longitudinal coordinates may be recorded and uploaded from the user device with the social media post. Accordingly, this information may be used to determine if a certain topic is trending in a certain location . [0063] For example, when NBA basketball player LeBron James announced his return to the Cleveland Cavaliers, the hashtag " #ComingHome " was trending in Miami and throughout Ohio, and in particular in Cleveland. According to this example, the statistical analysis may determine that basketball and LeBron James are important to Cleveland, Ohio and Miami.

[0064] In another example, the hashtag "#NYFW, " associated with New York City's Fashion Week, has historically been a trending topic in New York City, as well as Milan, London, Paris, Tokyo, and Los Angeles. In this regard, the statistical analysis may indicate that people in New York City, Los Angeles, London, Milan, Paris, and Tokyo have a shared interest in fashion.

[0065] In block 660, the service 2012 may generate a report from aggregating the analytic data described above. Alternatively, the analytic data may be translated by the service 2012 into a graph or map to show to the user in realtime what is trending, where it is trending, and who is influencing the trend. This graphical illustration may help the user determine why the trend is occurring and how it is spreading. Additionally, this information may be used to forecast future trends and improve the success and reach of trending topics .

[0066] In block 670, the service 2012 may provide the analytic data to the user device via a communication network. In some examples, the analytic data may be provided in a report and emailed to the user, similarly as described in block 560. Alternatively, similarly as in block 560, the user may be provided with a link to review the report on-line. In other examples, the analytic data may be provided to the user as a graph or map displaying the trending topics as they progress in real-time. [0067] Turning to FIG. 7, a flowchart is depicted illustrating a process 700 for color analysis of content included in social media posts. In block 710, a server, such as the server 2020, may receive social media posts.

[0068] In block 720, the server 2020 may determine color composition of the content of the received posts. For example, a color analysis may be performed on content, such as an image, to determine each color that appears therein. One technique for analyzing the color may be to perform a pixel- by-pixel analysis. Each pixel in the content may be mapped to a color category in a standard palette, such as the Xll standard palette. The color category may represent 11 basic color categories, such as red, pink, orange, yellow, brown, green, aqua, blue, purple, white, and gray/black. After each pixel is mapped to a color category in the standard palette, the analytics engine of the server 2020 may determine the proportion or ratio of each color category in the content. The proportion or ratio information may be displayed in an array-property value, such that earlier elements in the array represent color categories that are more prevalent than color categories that appear later in the array. For example, the color analysis may determine that content, such as an image, is 36.85% red, 19.79% pink, 16.51% brown, 9.29% orange, 6.24% white, 4.28% gray or black, 4.27% yellow, 1.28% green, 0.95% purple, 0.45% aqua, and 0.02% blue. This information may be displayed in a table or any suitable format, such as a pie chart or a Mondrian-esque square, for conveying the prevalence of each color category.

[0069] The color analysis may also include computing a color quantization for each of the color categories in the array-property value. Quantized colors are the sub-colors that make up each color category. For example, the color category red may include the quantized colors: LightSalmon, Salmon, DarkSalmon, LightCoral, IndianRed, Crimson, FireBrick, DarkRed, and Red. Accordingly, the content (image) may be further analyzed to determine the quantized colors that appear most frequently. The quantized colors may also be arranged to be presented in a table or any other suitable format, such as a pie chart or a Mondrian-esque square, that shows the relative weight of each quantized color in each color category .

[0070] In block 730, the server 2020 may filter colors from the color composition analysis, based on input from a user provided by the user device. For instance, a user may request that certain colors be removed from the analysis. Alternatively, the user may request that the image analysis only focus on a specific color or subset of colors.

[0071] A geo-location associated with the content may be determined by the server 2020 in block 740. For example, the GPS information or metadata associated with the content may be retrieved from the content. This may be used later in determining if certain locations show a color preference.

[0072] In block 750, the server 2020 may determine a color preference. For example, the server 2020 may determine that content posted with the hashtag "#re2pect" includes blue. Alternatively, the color preference may be that content with the hashtag "#re2pect" is mostly blue and white. Another example of the color preference may be that a hashtag with certain colors may be posted or re-posted more frequently than the same hashtag with different colors.

[0073] In other examples, the color preference may indicate that a particular user tends to post content with a certain color. Alternatively, the color preference may indicate that a particular user posts or re-posts content with a certain color more frequently than content with other colors. [0074] Another example of the color preference may be an indication of a certain color preference for certain geographic regions. For example, the color preference information may be linked to the previously determined geographic information. Accordingly, the combination of the color preference and geographic information may indicate that people in Miami show a color preference for warmer colors, like yellow and red, while people in New York and Los Angeles show a preference for neutral colors, like black, white, and gray .

[0075] In block 760, the server 2020 may link the content for which a color composition is determined (block 720) to a particular hashtag, keyword, phrase, topic, or username. Alternatively, the color analysis information discussed above may be linked to a hashtag, keyword, phrase, topic or username .

[0076] In block 770, the server 2020 may aggregate results of the color analysis to generate color analysis data.

[0077] Referring again to block 550 of FIG. 5, the server 2020 may combine the analytic data generated in the process 600 with the color analysis data generated in the process 700 to generate a report. In this regard, color information may be related to what is trending, where it is trending, and who is influencing the trend to provide additional information to marketers, advertisers, etc.

[0078] FIG. 8A shows an example of an analytic data that may be provided to a user in block 670 of FIG. 6, such as via a link, for display at a display of a user device. The analytic data may indicate recent activity data 805 related to a hashtag; a recap 810 of the hashtag data; an activity summary 815 of the hashtag; buzzwords 820 associated with the hashtag; an engagement 825 of the hashtag; participation 830 associated the hashtag; the top filters 835 used with hashtag; a snapshot 840 of the colors of posts associated with the hashtag, likes, and comments; the geolocation 845 of the hashtag; peak usage 850 of the hashtag; and the top other hashtags 855.

[0079] Referring to FIG. 8A, the analytic data associated with hashtag "#tiltshift" is shown. The recent activity data 805 may illustrate user profiles that have recently posted with the hashtag " #tiltshift . " The recap data 810 may indicate that 35 posts with the hashtag "#tiltshift" have been posted from 35 contributors. The recap data 810 may also indicate that posts with the hashtag "#tiltshift" have been commented on 41 times, been received 654 likes, reached 1,020 users, and received 8,254 impressions. The activity summary 815 may convey the recap data in a chart form.

[0080] The buzzwords section 820 of the report may indicate other keywords, topics, phrases or hashtags that have been posted with the hashtag being analyzed. For example, the buzzwords "#sky, " "#sun, " "#nyc, " "turban, " "#summer, " "#city," "quick," "city," "#art, " "#nature," and "#europe" have been posted with the hashtag "#tiltshift . " In some examples, the size of the buzzword may indicate the frequency with which the buzzwords appear with a hashtag. That is, more frequently occurring buzzwords may appear larger than other buzzwords .

[0081] The engagement data 825 may illustrate additional information related to the posts containing the hashtag. For example, the engagement data 825 may reflect the total number of posts associated with the hashtag in the center of a display portion. At the left side of the display portion, the analytic data may show that 35 posts have likes with a total number of 654 likes. At the right side of the display portion, the analytic data may show 16 of the 35 posts have received comments, and that the 16 posts that have received comments have received a total of 41 comments .

[ 0082 ] The participation data 830 may provide information about the users who have posted with the hashtag. For example, as shown in FIG. 8A, the display may show 34 of the contributors who posted with the hashtag "#tiltshift" have between 1-5 posts, and none of the contributors who posted with the hashtag "#tiltshift" had 6 or more posts.

[ 0083 ] The top filters 835 may provide information about what filters were used on the image before it was posted. As shown in FIG. 8A, the "normal" or "no filter" option was used the most for hashtag "#tiltshift" with a total of 22 posts with a "normal filter." According to this example, the "X-pro II" and "Amaro" filters were used 3 times in posts with the hashtag "#tiltshift" and the "Lo-fi" filter was used twice. As shown in FIG. 8A, the remaining filters were used once on images posted with the hashtag "#tiltshift . "

[ 0084 ] The color portion 840 may provide a snapshot of what colors appeared most commonly in posts, likes and comments. As illustrated in FIG. 8A, the 35 posts with the hashtag "#tiltshift" were mostly green followed in descending order by teal, red, gray, white, and purple. Of the 654 likes, the most common colors were white, yellow, gray and blue, in descending order. Finally, the most common colors associated with the comments were white, red and gray.

[ 0085 ] In some examples, icons used in the color portion 840 may be indicative of the social media site being analyzed. For example, the data displayed in FIG. 8A may be from analyzing of posts from Instagram®. Thus, in the color portion 840, the icon related to posts is a camera, the icon related to likes is a heart, and the icon related to comments is a bubble to coincide, respectively, with the posts, like and comment buttons used in Instagram®. Alternatively, for example, if posts from Twitter® were analyzed, the color portion 840 may include a bird icon for posts (i.e., Tweets) and a star icon for likes (i.e., favorites) .

[0086] The geolocation information 845 may provide information related to where the hashtag is being posted. According to FIG. 8A, the hashtag "#tiltshift" is being posted in the United States. The geolocation information 845 may be in the form of a map as shown in FIG. 8A. In alternative examples, the geolocation information 845 may include a list of locations where the hashtag is trending or a globe illustrating hotspots .

[0087] The peak usage information 850 may provide information related to when the posts with the hashtag were posted. As shown in FIG. 8A, the hashtag "#tiltshift" was posted Sunday evening and continued to be posted Monday afternoon .

[0088] The top other hashtags field 855 may provide information related to the most common hashtags being posted with the hashtag being analyzed. As shown in FIG. 8A, "#landscape" and "#picoftheday" were the most common hashtags used with the hashtag " #tiltshift . " While FIG. 8A shows the top other hashtags in a bar graph, one of ordinary skill in the art will recognize that this information may be conveyed in a pie chart, line graph, a table showing percentages, etc.

[0089] Turning to FIG. 8B, an example of a display, such as in a report displayed at a user device, of the color analysis data generated in block 770 of FIG. 7 is shown. FIG. 8B may include a grid 860 of thumbnails representing the content associated with the hashtag, a color filter option 885, and a context filter option 890.

[0090] Referring to FIG. 8B, the grid 860 may include a plurality of thumbnails, where each thumbnail is related to the content of a post. For example, the thumbnail may be a solid color that represents the predominant color that appears in that content. In other examples, the grid may display a thumbnail of the content, such as the image, video, text, etc. In further examples, the thumbnail may be a Mondrian-esque square or other graphical representation that provides a snapshot of the color composition of the content.

[ 0091 ] The color filter option 885 may allow a user to filter the grid 860. For example, a user may select one of the colors in the color filter option 885 such that the report, as provided by service 2012, only displays content that is predominantly the selected color.

[ 0092 ] The context filter option 890 may allow a user to filter the grid 860 according to contextual information. For example, the context filter option 890 may allow a user to filter the grid 860 by date, the number of likes, the number of comments, the number of retweets, the number of favorites, etc. For instance, the date context filter may allow a user to either sort the grid from Most Recent Posts to Later posts or vice-versa. Similarly, filtering based on likes may display a thumbnail with the most likes first and proceed to sort the thumbnails in descending order.

[ 0093 ] In FIG. 8B, the thumbnail 862 may be selected by a user. After the thumbnail 862 is selected, the service 2012 may provide color analysis data with additional information related to the selected thumbnail 862, such as the content 865 related to the thumbnail 862, the user 870 who posted the content 865, geo-location information 875 of the content 865, and the color composition 880 of the content 865. As discussed above, the color composition 880 may be a Mondrian- esque square or a similar graphical representation of the color composition of the image.

[ 0094 ] According to the example in FIG. 8B, the grid 860 shows thumbnails of content associated with the hashtag " #tiltshift . " As noted above, a user may select the thumbnail 862. After the thumbnail 862 is selected, the service 2012 may cause additional information to be displayed over the grid 860. In alternative examples, the grid 860 may move to display the additional information. The additional information displayed in FIG. 8B illustrates that the content 865 was posted by the user " omar jpeters " in 870. Further, the user information field 870 may illustrate how many posts the user has posted, how many followers they have, and how many users they are following. Additionally, the location at which the content 865 was captured or posted may be displayed in the geo-location field 875. The example shown in FIG. 8B illustrates that the image was taken in Ohio. Additionally, the color composition 880 of the content 865 may be shown in a Mondrian-esque square or any other suitable graphical representation for showing the color content of the content 865.

EXAMPLES

[0095] According to one example, a photographer may want to know what is trending in regards to tilt shift photography. The photographer may search for the hashtag "#tiltshift" using the analytics service described herein. As discussed above, the photographer may specify that only posts from Instagram® are to be analyzed. Accordingly, the analytics service may retrieve posts from Instagram® with the hashtag " #tiltshift . "

[0096] As discussed above, the analytics service may determine data related to the posts themselves. Additionally, the analytics service may perform a color analysis of any content posted with the hashtag "#tiltshift . " According to this example, a user may post an image to Instagram® of a city skyline, such as captured with a camera of a user device, with the hashtag " #tiltshift . " [0097] As discussed above, the analytics service may analyze a portion of the image, such as the portion including the skyline, determining the color composition of the portion of the image. In particular, there may be some information indicating that the Instagram® image, or portion thereof, is primarily blue. The analytics service may then combine the analytic data with the color analysis data to determine additional information. For example, the Instagram® image of the skyline may have more likes or shares in New York or Chicago than it does in Los Angeles or San Francisco. Accordingly, a photographer may use this information when composing photoshoots for potential clients.

[0098] According to another example, a marketer may desire to track content posted by a specific user. For instance, the marketer may want to determine if Ellen DeGeneres exhibits any patterns in the content that she posts in order to increase the chances that Ellen would view and re-post content. Therefore, the user may search the Twitter® username " @TheEllenShow . " The analytics service may retrieve and review all of " @TheEllenShow" posts.

[0099] As discussed above, the analytics service may determine the geo-location where the posts occur, the date/time of the posts, frequent hashtags used by Ellen, etc. Additionally, the analytics service may perform a color analysis of any content posted by " @TheEllenShow . " Based on this analysis, " @TheEllenShow" may have 100 posts with content. Of the 100 posts, 70 posts may include the color blue, 20 posts may include the color green, and the remaining 10 posts do not exhibit a majority color. Moreover, of the 100 posts, 13 posts may include the hashtag "#tbt" and 15 posts may include the hashtag "ClassicJokeWednesday . Thus, the analytics service may provide this information to the marketer to improve the chances that " @TheEllenShow" will view and re-post their content.

[ 0100 ] Although the invention herein has been described with reference to particular examples, it is to be understood that these examples are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative examples and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims .

Claims

1. A method for analyzing content associated with social media posts, the method comprising:

obtaining, by a processing device over a communication network, a plurality of social media posts posted from at least one communication device and indexed by a selected indexing element;

determining, by the processing device, whether any of the plurality of social media posts include content, wherein the content includes at least one of an image, a video, a sticker, an emoji, text or a background in which text is presented;

performing, by the processing device, a color analysis on the content;

generating, by the processing device, color data as a result of the color analysis; and

reporting, via the communication network to another communication device, the color data.

2. The method of claim 1, wherein the performing the color analysis further comprises:

determining a color composition of the content associated with the plurality of social media posts.

3. The method of claim 2, wherein the determining the color composition further comprises :

filtering a color associated with a topic of the plurality of social media posts.

4. The method of claim 1, wherein the generating the color data as a result of the color analysis further comprises :

determining a geographic location associated with each of the plurality of social media posts.

5. The method of claim 1, wherein the generating the color data as a result of the color analysis comprises:

determining at least one color preference associated with a topic of the social media posts .

6. The method of claim 1, wherein the indexing element corresponds to or is associated with a topic.

7. The method of claim 6, wherein the topic is selected based on at least one of a subject that is trending, a hashtag, a cashtag, or a username .

8. A non-transitory computer-readable medium configured to store instructions for analyzing content associated with social media posts, that when executed by one or more processors, perform a method comprising:

obtaining, over a communication network, a plurality of social media posts posted from at least one communication device and related to a topic, wherein the topic is selected based on at least one of a subject that is trending, a hashtag, a cashtag, or a username;

determining whether any of the plurality of social media posts include content, wherein the content is selected from at least one of an images, a video, a sticker, an emoji, text, or a background in which text is presented;

filtering the plurality of social media posts that include the content;

performing a color analysis of the content associated with the plurality of social media posts;

generating data as a result of color analysis; and reporting, via the communication network to another communication device, the data.

9. The non-transitory computer-readable medium of claim

8, wherein the performing the color analysis further comprises :

10. The non-transitory computer-readable medium of claim

9, wherein the determining the color composition further comprises :

filtering a color associated with the topic of the plurality of social media posts.

11. The non-transitory computer-readable medium of claim 8, wherein the generating the data as a result of the color analysis further comprises:

12. The non-transitory computer-readable medium of claim 8, wherein the generating the data as a result of the color analysis comprises:

determining at least one color preference associated with the topic of the social media posts.

13. An apparatus for analyzing content associated with social media posts, the apparatus comprising:

circuitry configured to control :

acquiring a plurality of social media posts related to a topic, wherein the topic is selected based on at least one of a subject that is trending, a hashtag, a cashtag, or a username; determining whether any of the plurality of social media posts includes content, wherein the content is selected from at least one of an image, a video, stickers, an emoji, text, or a background in which text is presented;

generating a report based on a result of the color analysis; and

communicating, via a communication network, the report to a communication device .

14. The apparatus of claim 13, wherein the color analysis determines a color composition of the content associated with the plurality of social media posts.

15. The apparatus of claim 14, wherein the color composition is determined by

16. The apparatus of claim 13, wherein the color analysis determines a geographic location associated with each of the plurality of social media posts.

17. The apparatus of claim 13, wherein the color analysis determines at least one color preference associated with the topic of the social media posts.