# US 2014/0279906 A1 · Apparatus, system and method for multiple source disambiguation of social media communications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0279906 A1 - Document ID: US20140279906A1 - Status: Published application - Kind: A1 - Application number: 13/802,327 - Filed: 2013-03-13 - Published: 2014-09-18 - Earliest claimed: 2013-03-13 - Inventors: Bart Michael Peintner - Web page: https://patentgrep.com/patent/US20140279906A1 ## CPC - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/44: Identification of trends within social networks, e.g. identification of trending topics - G06Q 10/48: using social graphs - 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 The present invention is directed to a system for understanding social media. The system may provide automated machine understanding of social media communications based on: social media assertions, social media statements and conversations, social connections, user profile info, crowd-sourced databases, Internet pages, and semantic networks. ## Claims ### Claim 1 A computer-implemented method performed by a processor for understanding a snapshot of social network information, the method comprising: accessing social network information associated with a user of social media; collecting a snapshot of social network information associated with the user which comprises a plurality of social media statements; accessing a plurality of subculture models; analyzing the snapshot of social network information and the plurality of subculture models to identify a weighted set of subcultures that reflects interests of the user; analyzing the snapshot of social network information to identify one or more contacts associated with the user; assigning a weight to each contact that reflects the strength of each contact's connection to the user; generating a personalized language model for the user that is based on the weighted set of subcultures and the set of contacts associated with the user, and which comprises an entity list; extracting at least one mention of entities that are identified on the entity list from the plurality of social media statements; compiling a list of possible references for the at least one mention of entities extracted from the plurality of social media statements; inferring a weighted posterior distribution over the list of possible references for the at least one mention of entities that are identified on the entity list; and analyzing the weighted posterior distribution to identify a list of disambiguated references for the at least one mention of entities in the snapshot of social network information. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, further comprising rating the user's sentiment for the list of disambiguated references and recording the user's sentiment for the list of disambiguated references in a database of inferred user profile opinions. ### Claim 3 (depends on 2) The computer-implemented method of claim 2, wherein rating the user's sentiment for the list of disambiguated references comprises word-based targeted sentiment analysis and pattern-based targeted sentiment analysis. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the pattern-based targeted sentiment analysis comprises comparing at least one of the user's plurality of social media statements with a pattern of expressions. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein the pattern of expressions comprises a regular expression, a rating, and a confidence value. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, further comprising inferring an updated weighted set of subcultures that reflect interests of the user based on an analysis of the snapshot of social media and the list of disambiguated references. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, further comprising recording the updated weighted set of subcultures that reflect interests of the user in a database of inferred user profile interests. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, further comprising recording the updated weighted set of subcultures that reflect interests of the user in a database of inferred user profile interests. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of subculture models each comprise a database of subculture specific entities and a database of subculture specific entity nicknames ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein each of the plurality of subculture models further comprise a database of subculture specific sentiment patterns. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein each of the plurality of subculture models further comprise a database of subculture specific semantic graph connections. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein each of the plurality of subculture models further comprise a database of subculture specific semantic graph connections. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, wherein each of the plurality of subculture models further comprise a database of subculture specific weighted N-grams. ### Claim 14 (depends on 13) The computer-implemented method of claim 13, wherein each of the plurality of subculture models further comprise a database of subculture specific co-occurrence frequencies. ### Claim 15 (depends on 1) The computer-implemented method of claim 1, wherein generating the personalized language model for the user comprises modeling the user's likelihood to emit specific N-gram expressions and refer to a particular entities. ### Claim 16 A program storage device readable by a machine tangibly embodying a program of instructions executable by a machine to perform method steps for understanding a snapshot of social network information, the method steps comprising: accessing social network information associated with a user of social media; collecting a snapshot of social network information associated with the user, which comprises a plurality of social media statements; accessing a plurality of subculture models; analyzing the snapshot of social network information and the plurality of subculture models to identify a weighted set of subcultures that reflect interests of the user; analyzing the snapshot of social network information to identify one or more contacts associated with the user; assigning a weight to each contact that reflects the strength of each contact's connection to the user; generating a personalized language model for the user that is based on the weighted set of subcultures and the set of contacts associated with the user, and which comprises an entity list; extracting at least one mention of entities that are identified on the entity list from the plurality of social media statements; compiling a list of possible references for the at least one mention of entities extracted from the plurality of social media statements; inferring a weighted posterior distribution over the list of possible references for the at least one mention of entities that are identified on the entity list; and analyzing the weighted posterior distribution to identify a list of disambiguated references for the at least one mention of entities in the snapshot of social network information. ### Claim 17 A computer program product recorded in a computer storage medium for understanding a snapshot of social network information comprising: first program instructions for accessing social network information associated with a user of social media; second program instructions for collecting a snapshot of social network information associated with the user, which comprises a plurality of social media statements; third program instructions for accessing a plurality of subculture models; fourth program instructions for analyzing the snapshot of social network information and the plurality of subculture models to identify a weighted set of subcultures that reflect interests of the user; fifth program instructions for analyzing the snapshot of social network information to identify one or more contacts associated with the user; sixth program instructions for assigning a weight to each contact that reflects the strength of each contact's connection to the user; seventh program instructions for generating a personalized language model for the user that is based on the weighted set of subcultures and the set of contacts associated with the user, and which comprises an entity list; eighth program instructions for extracting at least one mention of entities that are identified on the entity list from the plurality of social media statements; ninth program instructions for compiling a list of possible references for the at least one mention of entities extracted from the plurality of social media statements; tenth program instructions for inferring a weighted posterior distribution over the list of possible references for the at least one mention of entities that are identified on the entity list; and eleventh program instructions for analyzing the weighted posterior distribution to identify a list of disambiguated references for the at least one mention of entities in the snapshot of social network information.