# US 8,375,024 B2 · Modeling social networks using analytic measurements of online social media content > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,375,024 B2 - Document ID: US8375024B2 - Status: Granted - Kind: B2 - Application number: 12/353,166 - Filed: 2009-01-13 - Published: 2010-05-13 - Granted: 2013-02-12 - Earliest claimed: 2008-11-13 - Assignees: Buzzient, Inc. - Applicants: Andreas Goeldi - Inventors: Andreas Goeldi - Web page: https://patentgrep.com/patent/US8375024B2 ## CPC - G06Q 10/00: Administration; Management - G06Q 10/44: Identification of trends within social networks, e.g. identification of trending topics - G06Q 10/46: Determination of level of influence of users within social networking services - G06Q 10/48: using social graphs - G06Q 30/00: Commerce - G06Q 30/0251: Targeted advertisements - G06Q 30/0263: {based upon Internet or website rating} - G06Q 30/0277: {Online advertisement} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Methods, apparatuses, and computer-readable media for generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category. Embodiments are configured to harvest and aggregate posts of online social media conversations from one or more online social media sources; to perform content scraping on the posts of online social media conversations to obtain raw data that includes user-profile information of a social media author corresponding to each individual post of online social media conversations; and performing social network analysis processing on the raw data to obtain the social network graph. ## Claims ### Claim 1 A method comprising: generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category, wherein generating the social network graph comprises: harvesting and aggregating posts of online social media conversations from one or more online social media sources; performing content scraping on the posts of online social media conversations to obtain raw data including each individual post of an online social media conversation relevant to the subject matter of interest and user-profile information of a social media author corresponding to each individual post; and performing social network analysis processing on the raw data to obtain the social network graph, the processing including: analyzing an influence level of the social media author, the influence level indicative of a total number of other authors the social media author is connected to over one or more social networking paradigms, a total number of posts written by the author in one or more social networking paradigms, an importance score associated with the one or more social networking paradigms in which the social media author's posts are published, a total number of distinct subgroups of authors the social media author is connected with, and an overall activity level of the social media author; computing an influence score associated with the social media author using an influence score formula, the influence score formula computed by multiplying a raw betweenness centrality value for the social media author with a function of: (1) a number of other active authors on the website where the social media author is active, (2) a total number of posts the social media author has contributed, and (3) one or more correction parameters that are fine-tuned for purposes of a specific vertical that corresponds to the specific subject matter of interest; wherein the betweenness centrality value is computed as a result of a centrality measure of a vertex within a raw social graph generated for the given social networking paradigm; detecting a sentiment rating associated with a one or more posts of the social media author; and determining weighting factors associated with the one or more posts based on a combination of the sentiment rating associated with the one or more posts and the influence level associated with the social media author. ### Claim 2 (depends on 1) The method of claim 1, further comprising storing the social network graph in a database. ### Claim 3 (depends on 1) The method of claim 1, wherein harvesting the online social media conversations includes: performing forum analysis to discover websites of interest containing online social media content relevant to the subject matter of interest in the category; and searching through the websites of interest to extract social media conversations related to the subject matter of interest. ### Claim 4 (depends on 3) The method of claim 3, wherein the forum analysis comprises: searching for websites with online social media content relevant to the subject matter of interest using keywords in a search engine; assigning a relevance score to each website found in search engine results based on the keywords; and designating websites with a high relevance score as websites of interest. ### Claim 5 (depends on 1) The method of claim 1, wherein the content scraping comprises: identifying hyperlinks to potentially relevant online social media conversations in one or more web pages of each of the websites of interest; iteratively accessing and retrieving multiple levels of web pages within the each of the websites of interest using the hyperlinks until relevant conversation threads are found; breaking down each relevant conversation thread into individual posts of online social media conversations; and storing the raw data in the database. ### Claim 6 (depends on 5) The method of claim 5, wherein the raw data further comprises information on those social media authors responding to each of the individual posts. ### Claim 7 (depends on 1) The method of claim 1, wherein the user-profile information of the social media authors includes one or more of: author's username; author's demographic and geographic information; number of conversations the author has posted to the website; and online social media connections of the author. ### Claim 8 (depends on 1) The method of claim 1, wherein the social network analysis processing comprises: determining influence of each of the authors within the one or more social networks of related authors of online social media and their corresponding posts; ranking the influence of each of the authors to ascertain most influential authors for the category; and compiling a list of the most influential authors for the category sorted by influence. ### Claim 9 (depends on 8) The method of claim 8, wherein the influence of each of the online social media authors is determined by: calculating a centrality value for each author using a betweenness centrality algorithm on the raw data stored in the database for each of the websites of interest, the centrality value to indicate a degree of influence of the author in the one or more social networks; and calculating an influence score for each author based on the centrality value of the author to determine the influence of each of the authors within the one or more social networks of related authors of online social media and their corresponding posts. ### Claim 10 (depends on 9) The method of claim 9, wherein the influence score for each of the authors is further based on a number of active authors on the websites of interest and number of posts to the websites of interest by each author. ### Claim 11 (depends on 9) The method of claim 9, further comprising modifying the centrality value by an activity level of each of the authors and by an importance of the website where the author is active. ### Claim 12 (depends on 1) The method of claim 1, further comprising: parsing the social network graph to obtain early warnings about a service issue or quality issue associated with a particular service or product that embodies the subject matter of interest. ### Claim 13 (depends on 1) The method of claim 1, further comprising: identifying a first list of most influential authors corresponding to a particular subject matter of interest based on a ranking of the influence levels associated with a plurality of social media authors participating in the online social media conversations. ### Claim 14 (depends on 1) The method of claim 1, further comprising: based on the social network graph, generating a list of advertising networks that place advertisements that place advertisements in the analyzed online social media; utilizing the social network graph, identifying advertising networks that place advertisements most relevant to the subject matter of interest in the analyzed social media; and outputting the identified list of relevant advertising networks to a user of the social network graph. ### Claim 15 A system comprising: a processor; memory coupled to the processor, the memory configured to store a set of instructions corresponding to a method executed by the processor, the method including: generating a social network graph to model one or more social networks of related authors of online social media and their corresponding posts of online social media conversations relevant to subject matter of interest in a category, wherein generating the social network graph comprises: harvesting and aggregating posts of online social media conversations from one or more online social media sources; performing content scraping on the posts of online social media conversations to obtain raw data including each individual post of an online social media conversation relevant to the subject matter of interest and user-profile information of a social media author corresponding to each individual post; and performing social network analysis processing on the raw data to obtain the social network graph, the processing including: analyzing an influence level associated with the social media author, the influence level indicative of a total number of other authors the social media author is connected to over one or more social networking paradigms, a total number of posts written by the author in one or more social networking paradigms, an importance score associated with the one or more social networking paradigms in which the social media author's posts are published, a total number of distinct subgroups of authors the social media author is connected with, and an overall activity level of the social media author; computing an influence score associated with the social media author using an influence score formula, the influence score formula computed by multiplying a raw betweenness centrality value for the social media author with a function of: (1) a number of other active authors on the website where the social media author is active, (2) a total number of posts the social media author has contributed, and (3) one or more correction parameters that are fine-tuned for purposes of a specific vertical that corresponds to the specific subject matter of interest; wherein the betweenness centrality value is computed as a result of a centrality measure of a vertex within a raw social graph generated for the given social networking paradigm; detecting a sentiment rating associated with a one or more posts of the social media author; and determining weighting factors associated with the one or more posts based on a combination of the sentiment rating associated with the one or more posts and the influence level associated with the social media author. ### Claim 16 (depends on 15) The system of claim 15, wherein the method further comprises storing the social network graph in a database. ### Claim 17 (depends on 15) The system of claim 15, wherein harvesting the online social media conversations includes: performing forum analysis to discover websites of interest containing online social media content relevant to the subject matter of interest in the category; and searching through the websites of interest to extract social media conversations related to the subject matter of interest. ### Claim 18 (depends on 17) The system of claim 17, wherein the forum analysis comprises: searching for websites with online social media content relevant to the subject matter of interest using keywords in a search engine; assigning a relevance score to each website found in search engine results based on the keywords; and designating websites with a high relevance score as websites of interest. ### Claim 19 (depends on 15) The system of claim 15, wherein the content scraping comprises: identifying hyperlinks to potentially relevant online social media conversations in one or more web pages of each of the websites of interest; iteratively accessing and retrieving multiple levels of web pages within the each of the websites of interest using the hyperlinks until relevant conversation threads are found; breaking down each relevant conversation thread into individual posts of online social media conversations; and storing the raw data in the database. ### Claim 20 (depends on 19) The system of claim 19, wherein the raw data further comprises information on those social media authors responding to each of the individual posts. ### Claim 21 (depends on 15) The system of claim 15, wherein the user-profile information of the social media authors includes one or more of: author's username; author's demographic and geographic information; number of conversations the author has posted to the website; and online social media connections of the author. ### Claim 22 (depends on 1) The system of claim 1, wherein the social network analysis processing comprises: determining influence of each of the authors within the one or more social networks of related authors of online social media and their corresponding posts; ranking the influence of each of the authors to ascertain most influential authors for the category; and compiling a list of the most influential authors for the category sorted by influence. ### Claim 23 (depends on 15) The system of claim 15, wherein the method further comprises: parsing the social network graph to obtain early warnings about a service issue or quality issue associated with a particular service or product that embodies the subject matter of interest. ### Claim 24 (depends on 15) The system of claim 15, wherein the method further comprises: identifying a first list of most influential authors corresponding to a particular subject matter of interest based on a ranking of the influence levels associated with a plurality of social media authors participating in the online social media conversations. ### Claim 25 (depends on 15) The system of claim 15, wherein the method further comprises: based on the social network graph, generating a list of advertising networks that place advertisements that place advertisements in the analyzed online social media; utilizing the social network graph, identifying advertising networks that place advertisements most relevant to the subject matter of interest in the analyzed social media; and outputting the identified list of relevant advertising networks to a user of the social network graph.