SYSTEMS AND METHODS FOR DETERMINING INFLUENCERS IN A SOCIAL DATA NETWORK AND RANKING DATA OBJECTS BASED ON INFLUENCERS

摘要

A method performed by a computing system is provided for searching for text sources including temporally-ordered data objects based on at least influence of an author. Users associated with a topic are identified, including authors. The users are modeled as a node and the method includes computing a topic network graph using the users as nodes and their relationships as edges. Users are ranked within the topic network graph. A search query based on a term and a time interval, including the topic, is obtained. Data objects based on the search query are identified. The method further includes: generating a popularity curve based on the frequency of data objects; identifying popular data objects based on the popularity curve; identifying an author of each of the popular data objects; and ranking the popular data objects according to a respective ranking of a respective author of each of the popular data objects.

主权项

1. A method performed by a computing system for searching for text sources including temporally-ordered data objects based on at least influence of an author, comprising:
identifying users associated with a topic, the users including authors of the data objects; modeling each of the users as a node and determining relationships between each of the users; computing a topic network graph using the users as nodes and the relationships as edges; ranking the users within the topic network graph; identifying and filtering outlier nodes within the topic network graph; outputting users remaining within the topic network graph according to their associated ranking of influence; obtaining or generating a search query based on one or more terms and one or more time intervals, the one or more terms including the topic; obtaining or generating time data associated with the data objects; identifying one or more data objects based on the search query; generating one or more popularity curves based on the frequency of data objects corresponding to one or more of the search terms in the one or more time intervals; identifying data objects as popular based on the one or more popularity curves; identifying an author of each of the popular data objects, each author identified as part of the outputted users within the topic network graph; and ranking each of the popular data objects according to a respective influence ranking of a respective author of each of the popular data objects.