Patent Publication Number: US-2022237710-A1

Title: Identification and image construction for social media

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/846,848 filed on Apr. 13, 2020. All sections of the aforementioned application are incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     The popularity of social networking sites such as Twitter®, MySpace®, LinkedIn®, etc., are increasing. Social networking sites allow a subscriber to share personal content and information, status, or location information. 
     This background information is provided to reveal information believed by the applicant to be of possible relevance. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art. 
     SUMMARY 
     The disclosed subject matter may display a summary image of historical information associated with a social media account. In an example, an apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include receiving information during a period associated with a social media account, wherein the information comprises one or more keywords associated with an image, text, audio, or video of respective social media posts, and wherein the one or more keywords is determined based on computer image analysis or machine learning of the respective social media posts, based on the information, determining that a threshold amount of activity associated with the social media account corresponds with at least a representative image indicative of the activity during the period; and sending an alert that comprises the representative image 
     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 to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to limitations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale. 
         FIG. 1  illustrates an exemplary system  100  that may implement the subject matter disclosed herein, such as identification and image construction for social media, among other things. 
         FIG. 2  illustrates an exemplary method for identification and image construction for social media. 
         FIG. 3A  illustrate an exemplary map associated with geographical positioning while accessing a social media account. 
         FIG. 3B  illustrate an exemplary map associated with geographical positioning while accessing a social media account. 
         FIG. 3C  illustrate an exemplary map associated with geographical positioning while accessing a social media account. 
         FIG. 4  illustrates an exemplary method for identification and image construction for social media. 
         FIG. 5  illustrates exemplary representative images for identification and image construction for social media, as disclosed herein. 
         FIG. 6  illustrates a schematic of an exemplary network device. 
         FIG. 7  illustrates an exemplary communication system that provides wireless telecommunication services over wireless communication networks. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosed subject matter may display a summary (e.g. representative) image of historical information associated with a social media account. An image, video, text, or audio may be generated to represent the social media account over a period. 
       FIG. 1  illustrates an exemplary system  100  that may implement the subject matter disclosed herein, such as identification and image construction for social media, among other things. System  100  may include network  108 . Network  108  may communicate with or otherwise connect mobile device  101 , device  102 , mobile device  103 , base station  104 , base station  105 , base station  106 , or server  107 . The devices of system  100  may be communicatively connected with each other. Mobile device  101  or mobile device  103  may include a laptop, desktop computer, tablet, autonomous vehicle (e.g., SAE Intl level  3  to level  5  automation), or mobile phone, among other things. Device  102  may include wireless or wired devices, such as servers, routers, switches, desktop computers, or the like. In an example, mobile device  101  may be associated with a first and second social medial platform, while mobile device  103  may be associated with just a third social media platform. Server  107 , as shown, may process some or all of the steps of  FIG. 2  or  FIG. 4 , herein. 
     With continued reference to  FIG. 1 , in an example, the information obtained (e.g., received) and analyzed (e.g., reconcile common accounts) by server  107  may include user login ID, social media account numbers, login location information, access point information for social media account, written post sentence structure, written post words, written post grammatical behavior, other written post information, website cookies, device related cookies, proximity of the used devices, user age, account age, educational background (e.g., schools or degrees), careers, number of social friends linked, likes (e.g., likes of activity, events, music, movies, or political campaign), login frequency, login times, usage behavior that may include number of chats/posts/reposts, domains, IP addresses, protocols, data flows, or the like that may be used for identification and image construction for social media as provided herein. Server  107  may obtain information from multiple sources. The information of server  107  may include information from mobile device  101 , device  102 , mobile device  103 , base station  104 , base station  105 , base station  106 , or the like. 
       FIG. 2  illustrates an exemplary method for identification and image construction for social media. At step  111 , information, as described herein, associated with a plurality of social media accounts may be obtained. At step  112 , based on the information of step  111 , a geographical map (e.g., geographical map  127 ) of the plurality of social media accounts may be generated. The geographical map  127  (e.g.,  FIG. 3A ) may indicate a routine usage of some or all of the plurality of social media accounts that are associated with one or more users. At step  113 , based on comparing the geographical maps (and other information) associated with each social media account, commonality between users may automatically be determined by server  107 . At step  114 , an alert may be sent to indicate the likely commonality. This commonality may be used to streamline advertising, appropriately police users for misconduct, or appropriately suggest additional connections between accounts (e.g., friend suggestions), among other things. 
     With continued reference to  FIG. 2 , it is contemplated that information disclosed herein other than or in addition to location information may be used determine commonality between accounts. For example, style of written posts may be analyzed to find similarities in sentence structure, word use, or emoticons. The analysis may include frequency of use, locations in sentence, the order, or the number, among other things. It is also contemplated that the steps performed herein (e.g., steps  111 - 114 ) may be performed on one device (e.g., server  107  or mobile device  101 ) or multiple devices. A heat map may also be used based on the disclosed information to determine hot spots for social media platform usage (e.g., usage location heat map) or other parameters based on the disclosed information. 
       FIG. 3A - FIG. 3C  illustrate exemplary maps associated with geographical positioning while using a social media account, such as posting to a social media platform or viewing a social media post. In an example, server  107  may have information that a social media account has been accessed at a certain location, but may determine whether multiple different accounts likely belong (or do not belong) to the same user. Table 1 provides example parameters that relate to  FIG. 3A - FIG. 3C  and may be used to determine whether multiple different accounts likely belong (or do not belong) to the same user. As can be observed from Table 1, there are three different user names in which two of the three user names are identical. Further, some of the locations and times for social media account usage overlap. Based on the information in Table 1 (and other information not shown in Table 1), a determination may be made that John Doe #1 and J-Art are the same user and a score (e.g., 95%) may be assigned to the accuracy of such determination. John Doe #3 may be determined to be a different user from the other two in Table 1. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                 Social Media 
                 Approximate 
                   
               
               
                   
                   
                 Account  
                 Social Media 
                   
               
               
                   
                   
                 Access 
                 Daily Access 
                 Social Media 
               
               
                 # 
                 User Name 
                 Location 
                 Time 
                 Platform 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 John Doe 
                 Location 121, 
                  9 am, 
                 Platform #1 
               
               
                   
                   
                 location 122, 
                 12 pm,  
                   
               
               
                   
                   
                 location 123 
                 5 pm 
                   
               
               
                 2 
                 J-Art 
                 Location 121, 
                  9 am, 
                 Platform #2 
               
               
                   
                   
                 location 122, 
                 12 pm,  
                   
               
               
                   
                   
                 location 123, 
                  5 pm, 
                   
               
               
                   
                   
                 Location 124 
                 9 pm 
                   
               
               
                 3 
                 John Doe 
                 Location 124, 
                 12 pm,  
                 Platform #2 
               
               
                   
                   
                 Location 125 
                 6 pm 
               
               
                   
               
            
           
         
       
     
       FIG. 4  illustrates an exemplary method for identification and image construction for social media. As further described, a representative image may be constructed that provides a summarized story from historical posts, which may help more easily flag anomalies. At step  131 , information associated with a plurality of social media accounts during a period may be obtained. As described herein, the information may include user login ID, account numbers, location information, hardware access point for social media account (e.g., historic), written post sentence structure, written post words, written post grammatical behavior, written post emoticons, website cookies, device related cookies, proximity of the used devices, user age, account age, educational background (e.g., schools, degrees), career, number of social friends, likes (activity, events, music, movies, political campaign), login frequency, login times, or usage behavior that may include number of chats/posts/reposts, among other things. With regards to hardware (e.g., mobile phone, server, or desktop computer), a record may be created to indicate when one device is used to log into different accounts. With regards to proximity of the used devices, the proximity information may include detected connections to the same cell towers and the detected constant proximity to the same group of user devices during a period. 
     With continued reference to step  131  of  FIG. 4 , the plurality of social media accounts may include multiple different social media platforms (e.g., Twitter® or LinkedIn®, among others). The information may include keywords, image indicators (e.g., indication of flower, dog, cat, smile, frown etc.), or video indicators (e.g., indication of flower, dog, cat, smile, frown etc.). The image or video indicators may be words that describe the persons, places, or things within the image or video. The image indicators or video indicators may be extracted from one or more images based on computer image analysis or machine learning. Computer image analysis may include image analysis or video techniques, such as 2D and 3D object recognition, facial image recognition, image segmentation, motion detection e.g. single particle tracking, video tracking, optical flow, 3D Pose Estimation, or automatic number plate recognition. With reference to machine learning, a machine learning module may query big data and Internet to correlate aspects (images, words, etc.) found in the profile on hand and get the emotional state based on online comparison (e.g., if a happy occasion, then people say “congratulations”). Similar analysis may be made for posted quotes or religious texts and ideologies. In another example, the determination of similarities (e.g., commonalities) and therefore corresponding descriptors of an image may be based on when or how the majority of people on internet use these texts, videos, or images, among other things. 
     With continued reference to  FIG. 4 , at step  132 , based on the information of step  131 , determining that one or more activities (e.g., posts, likes, or other usage) on a social medial account during the period corresponds with at least an image indicative of the activity during the period. A threshold amount of activity associated with the social media account may be the number of post associated with the keyword flower, pet, sports, or the like. In an example, in a period of a week there may be multiple photos of flowers and outdoor scenes, therefore the representative image indicative of the activity during the period may be a flower. See  FIG. 5 . An artificial intelligence (AI) module, which may use machine learning, may obtain images (commonly understood by the population) related to the determined activity. For example, the AI may identify keywords and identifying features from posts, images (e.g., photos or computer-generated images), or videos. The generated image may evolve with time to portray users interests, ideas, or a forecast of interests (which me based on other users historical posts). 
     At step  133 , a message may be sent that includes the representative image. The sending of the image may include posting the first image to a news feed, displaying as a foreground or background picture, displaying as a main image on device (e.g., mobile device) as home screen wallpaper, or emailing the image, among other things. 
     At step  134 , an indication to zoom in on the representative image may be received. At step  135 , based on the indication to zoom of step  134 , the representative image of step  133  may increase in size and display additional images or previously not observable (or generally not observable) images that are indicative of a more detailed description of the period (e.g., zoom from a month representative image to a representative image for a week of said month). It is contemplated herein that the opposite may be true when zooming out (e.g., from a month view to a year-to-date view). 
     At step  136 , an indication of a selection of a portion of the image may be received. Any portion of the image (a petal of a flower) may be selected (e.g., mouse click or finger pressure on a touch interface). At step  137 , based on the selection, underlying social media posts or other information may be displayed (e.g., on mobile device  101 ) that led the AI to generate the image. Note the image may be selected from a stock image or created with unique colors or lines for a unique customization. 
     At step  138 , server  107  may obtain an indication that an emotional state threshold has been met, wherein the emotional state threshold is indicative of an emotional state. Emotional states may include fear, anger, sadness, joy, disgust, grief, or surprise, among others. The emotional state threshold may be determined by machine learning algorithms. At step  139 , based on the emotional state threshold being met, sending an alert message to an authority device. The authority device may be associated with a public safety agency (e.g., police or fire). For example, any sudden changes may trigger an alarm (from overly happy to being sad)—the alarm may trigger deep inspection to correlate events for the reason of mood changes (e.g., death in the family), otherwise an alarm may be triggered. The public safety agency may be sent and further review the representative images associated with step  132 . As disclosed, the representative images may allow for an observer (e.g., public safety official or friend) to quickly summarize a user&#39;s social media account activity. 
     Further perspective is provided below with regard to identification and image construction for social media, as disclosed herein. The disclosed subject matter may allow for the visualization of historical information associated with a social media account. For example, server  107  may construct a graphical picture or video to represent the profile over a period. In the example, there may be a short length video in which the video time length or other features is configurable. As disclosed, information such as keywords in posts, songs, reshared articles, and key aspects of pictures may be chosen to help construct a representative image or a video.  FIG. 5  illustrates exemplary representative images for identification and image construction for social media, as disclosed herein. In an example, during the first 4 days of the first week of March there may have been 6 out of 9 posts associated with gardening by a social media account. Therefore, flower  141  may be the representative image for the social media account. During the second week of March, there may have been sad emoticons and words related to a death (e.g., condolences) in the majority of the posts. Therefore, emoticon  142  may be the representative image for the social media account for a period during the second week. During the third week of March, there may have been a majority of liked posts by the social media account that are related to sports. Therefore, sports image  143  may be the representative image for the social media account for a period during the third week. During the fourth week of March, there may have been indications of locations that are abnormal and would usually be traveled to by plane in a significant number of the posts. Therefore, plane  144  may be the representative image for the social media account for a period during the fourth week, in which the plane may be indicative of extensive or abnormal travel. It is contemplated that the posts or other information may be scaled to have different weight over other posts (e.g., funeral post by the social media account may be weighted more than a “like” indication of another user&#39;s cat video). 
       FIG. 6  is a block diagram of network device  300  that may be connected to or comprise a component of system  100 . Network device  300  may comprise hardware or a combination of hardware and software. The functionality to facilitate telecommunications via a telecommunications network may reside in one or combination of network devices  300 . Network device  300  depicted in  FIG. 6  may represent or perform functionality of an appropriate network device  300 , or combination of network devices  300 , such as, for example, a component or various components of a cellular broadcast system wireless network, a processor, a server, a gateway, a node, a mobile switching center (MSC), a short message service center (SMSC), an automatic location function server (ALFS), a gateway mobile location center (GMLC), a radio access network (RAN), a serving mobile location center (SMLC), or the like, or any appropriate combination thereof. It is emphasized that the block diagram depicted in  FIG. 6  is exemplary and not intended to imply a limitation to a specific implementation or configuration. Thus, network device  300  may be implemented in a single device or multiple devices (e.g., single server or multiple servers, single gateway or multiple gateways, single controller or multiple controllers). Multiple network entities may be distributed or centrally located. Multiple network entities may communicate wirelessly, via hard wire, or any appropriate combination thereof. 
     Network device  300  may comprise a processor  302  and a memory  304  coupled to processor  302 . Memory  304  may contain executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations associated with mapping wireless signal strength. 
     In addition to processor  302  and memory  304 , network device  300  may include an input/output system  306 . Processor  302 , memory  304 , and input/output system  306  may be coupled together (coupling not shown in  FIG. 6 ) to allow communications between them. Each portion of network device  300  may comprise circuitry for performing functions associated with each respective portion. Thus, each portion may comprise hardware, or a combination of hardware and software. Input/output system  306  may be capable of receiving or providing information from or to a communications device or other network entities configured for telecommunications. For example, input/output system  306  may include a wireless communications (e.g., 3G/4G/GPS) card. Input/output system  306  may be capable of receiving or sending video information, audio information, control information, image information, data, or any combination thereof. Input/output system  306  may be capable of transferring information with network device  300 . In various configurations, input/output system  306  may receive or provide information via any appropriate means, such as, for example, optical means (e.g., infrared), electromagnetic means (e.g., RF, Wi-Fi, Bluetooth®, ZigBee®), acoustic means (e.g., speaker, microphone, ultrasonic receiver, ultrasonic transmitter), or a combination thereof. In an example configuration, input/output system  306  may comprise a Wi-Fi finder, a two-way GPS chipset or equivalent, or the like, or a combination thereof. 
     Input/output system  306  of network device  300  also may contain a communication connection  308  that allows network device  300  to communicate with other devices, network entities, or the like. Communication connection  308  may comprise communication media. Communication media typically embody 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. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, or wireless media such as acoustic, RF, infrared, or other wireless media. The term computer-readable media as used herein includes both storage media and communication media. Input/output system  306  also may include an input device  310  such as keyboard, mouse, pen, voice input device, or touch input device. Input/output system  306  may also include an output device  312 , such as a display, speakers, or a printer. 
     Processor  302  may be capable of performing functions associated with telecommunications, such as functions for processing broadcast messages, as described herein. For example, processor  302  may be capable of, in conjunction with any other portion of network device  300 , determining a type of broadcast message and acting according to the broadcast message type or content, as described herein. 
     Memory  304  of network device  300  may comprise a storage medium having a concrete, tangible, physical structure. As is known, a signal does not have a concrete, tangible, physical structure. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a transient signal. Memory  304 , as well as any computer-readable storage medium described herein, is not to be construed as a propagating signal. Memory  304 , as well as any computer-readable storage medium described herein, is to be construed as an article of manufacture. 
     Memory  304  may store any information utilized in conjunction with telecommunications. Depending upon the exact configuration or type of processor, memory  304  may include a volatile storage  314  (such as some types of RAM), a nonvolatile storage  316  (such as ROM, flash memory), or a combination thereof. Memory  304  may include additional storage (e.g., a removable storage  318  or a non-removable storage  320 ) including, for example, tape, flash memory, smart cards, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, USB-compatible memory, or any other medium that can be used to store information and that can be accessed by network device  300 . Memory  304  may comprise executable instructions that, when executed by processor  302 , cause processor  302  to effectuate operations to map signal strengths in an area of interest. 
       FIG. 7  depicts an exemplary diagrammatic representation of a machine in the form of a computer system  500  within which a set of instructions, when executed, may cause the machine to perform any one or more of the methods described above. One or more instances of the machine can operate, for example, as processor  302 , mobile device  101 , mobile device  103 , server  107 , device  102 , base station  104 -base station  106 , and other devices of  FIG. 1 . In some examples, the machine may be connected (e.g., using a network  502 ) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client user machine in a server-client user network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. 
     The machine may comprise a server computer, a client user computer, a personal computer (PC), a tablet, a smart phone, a laptop computer, a desktop computer, a control system, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. It will be understood that a communication device of the subject disclosure includes broadly any electronic device that provides voice, video or data communication. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein. 
     Computer system  500  may include a processor (or controller)  504  (e.g., a central processing unit (CPU)), a graphics processing unit (GPU, or both), a main memory  506  and a static memory  508 , which communicate with each other via a bus  510 . The computer system  500  may further include a display unit  512  (e.g., a liquid crystal display (LCD), a flat panel, or a solid state display). Computer system  500  may include an input device  514  (e.g., a keyboard), a cursor control device  516  (e.g., a mouse), a disk drive unit  518 , a signal generation device  520  (e.g., a speaker or remote control) and a network interface device  522 . In distributed environments, the examples described in the subject disclosure can be adapted to utilize multiple display units  512  controlled by two or more computer systems  500 . In this configuration, presentations described by the subject disclosure may in part be shown in a first of display units  512 , while the remaining portion is presented in a second of display units  512 . 
     The disk drive unit  518  may include a tangible computer-readable storage medium on which is stored one or more sets of instructions (e.g., software  526 ) embodying any one or more of the methods or functions described herein, including those methods illustrated above. Instructions  526  may also reside, completely or at least partially, within main memory  506 , static memory  508 , or within processor  504  during execution thereof by the computer system  500 . Main memory  506  and processor  504  also may constitute tangible computer-readable storage media. 
     As described herein, a telecommunications system may utilize a software defined network (SDN). SDN and a simple IP may be based, at least in part, on user equipment, that provide a wireless management and control framework that enables common wireless management and control, such as mobility management, radio resource management, QoS, load balancing, etc., across many wireless technologies, e.g. LTE, Wi-Fi, and future 5G access technologies; decoupling the mobility control from data planes to let them evolve and scale independently; reducing network state maintained in the network based on user equipment types to reduce network cost and allow massive scale; shortening cycle time and improving network upgradability; flexibility in creating end-to-end services based on types of user equipment and applications, thus improve customer experience; or improving user equipment power efficiency and battery life—especially for simple M2M devices—through enhanced wireless management. 
     While examples of a system in which alerts for identification and image construction for social media can be processed and managed have been described in connection with various computing devices/processors, the underlying concepts may be applied to any computing device, processor, or system capable of facilitating a telecommunications system. The various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. Thus, the methods and devices may take the form of program code (i.e., instructions) embodied in concrete, tangible, storage media having a concrete, tangible, physical structure. Examples of tangible storage media include floppy diskettes, CD-ROMs, DVDs, hard drives, or any other tangible machine-readable storage medium (computer-readable storage medium). Thus, a computer-readable storage medium is not a signal. A computer-readable storage medium is not a transient signal. Further, a computer-readable storage medium is not a propagating signal. A computer-readable storage medium as described herein is an article of manufacture. When the program code is loaded into and executed by a machine, such as a computer, the machine becomes a device for telecommunications. In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile or nonvolatile memory or storage elements), at least one input device, and at least one output device. The program(s) can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language, and may be combined with hardware implementations. 
     The methods and devices associated with a telecommunications system as described herein also may be practiced via communications embodied in the form of program code that is transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via any other form of transmission, wherein, when the program code is received and loaded into and executed by a machine, such as an EPROM, a gate array, a programmable logic device (PLD), a client computer, or the like, the machine becomes a device for implementing telecommunications as described herein. When implemented on a general-purpose processor, the program code combines with the processor to provide a unique device that operates to invoke the functionality of a telecommunications system. 
     While the disclosed systems have been described in connection with the various examples of the various figures, it is to be understood that other similar implementations may be used or modifications and additions may be made to the described examples of a telecommunications system without deviating therefrom. For example, one skilled in the art will recognize that a telecommunications system as described in the instant application may apply to any environment, whether wired or wireless, and may be applied to any number of such devices connected via a communications network and interacting across the network. Therefore, the disclosed systems as described herein should not be limited to any single example, but rather should be construed in breadth and scope in accordance with the appended claims. 
     In describing preferred methods, systems, or apparatuses of the subject matter of the present disclosure—identification and image construction for social media—as illustrated in the Figures, specific terminology is employed for the sake of clarity. The claimed subject matter, however, is not intended to be limited to the specific terminology so selected. In addition, the use of the word “or” is generally used inclusively unless otherwise provided herein. 
     This written description uses examples to enable any person skilled in the art to practice the claimed subject matter, including making and using any devices or systems and performing any incorporated methods. Other variations of the examples are contemplated herein. 
     Methods, systems, and apparatuses, among other things, as described herein may provide for identification and image construction for social media. A method, system, computer readable storage medium, or apparatus provides for obtaining (e.g., receiving) first information during a first period associated with a plurality of social media accounts, wherein the social media accounts comprise a first group of social media accounts from a first social medial platform and a second group of social media accounts from a second social media platform, wherein the first information comprises keywords which may be associated with images or videos, and wherein the keywords may be extracted from one or more images based on computer image analysis or machine learning; based on the first information, determining that a first threshold of activity of a first social media account corresponds with at least a first image indicative of the activity during the first period; and sending an alert that comprises the first image, wherein the sending the alert comprises posting the first image to a first social media account or displaying the first image on a device display. The method, system, computer readable storage medium, or apparatus may provide for obtaining an indication to zoom in on the first image; based on the indication to zoom in on the first image, altering the first image to be displayed at an increased size on the display; and displaying additional previously not observable images that are indicative of more detailed description of the of the first period. The method, system, computer readable storage medium, or apparatus may provide for obtaining an indication of a selection of a portion of the first image; and based on the indication of the selection of a portion of the first image, display posts or other information of the social media (e.g., account activity) that were used to determine the first image. The method, system, computer readable storage medium, or apparatus may provide for obtaining an indication that a second threshold has been met, wherein the second threshold is indicative of an emotional state; and based on the second threshold being met, sending an alert message to an authority device, wherein an authority device is associated with a public safety agency. All combinations in this paragraph (including the removal or addition of steps) are contemplated in a manner that is consistent with the other portions of the detailed description. 
     Methods, systems, and apparatuses, among other things, as described herein may provide for identification and image construction for social media. A method, system, computer readable storage medium, or apparatus provides for obtaining (e.g., receiving) first information during a first period associated with a plurality of social media accounts. The first information include user login ID, account #s, location information, hardware access point for social media account (historic), written post sentence structure, written post words, written post grammatical behavior, other written post information, website cookies, device related cookies, proximity of the used devices, user age, account age, educational background (schools, degrees), career, number of social friends, likes (activity, events, music, movies, political campaign), login frequency, login times, usage behavior that may include number of chats/posts/reposts, etc. The method, system, computer readable storage medium, or apparatus may provide for based on the first information, generating a geographical map of each social media account of at least a sub-group of the plurality of the social media accounts, wherein the geographical map indicates a routine for usage of one or more of the plurality of social media accounts that are associated with a first user; determining that the first user is a common user for multiple accounts of the plurality of social media accounts based on comparing the geographical maps of each social media account of the at least the sub-group of the plurality of the social media accounts (e.g., reconcile social media usage with the generated map); and providing an indication of the multiple accounts of the first user or an indication that the first user is the common user of the multiple accounts. In an example, an apparatus may include a processor and a memory coupled with the processor that effectuates operations. The operations may include receiving information during a period associated with a social media account, wherein the information comprises one or more keywords associated with an image, text, audio, or video of respective social media posts, and wherein the one or more keywords is determined based on computer image analysis or machine learning of the respective social media posts, based on the information, determining that a threshold amount of activity associated with the social media account corresponds with at least a representative image indicative of the activity during the period; and sending an alert that comprises the representative image. All combinations in this paragraph (including the removal or addition of steps) are contemplated in a manner that is consistent with the other portions of the detailed description.