Social Network Influence Analysis Using Topic-Specific Markers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to effectively identify influential users and related topics within social networks, which are crucial for businesses and academics to understand user interests and tailor services or marketing strategies.
Innovation Solution
A method and system that analyze messages from social networks to identify users interested in specific topics by using markers of interest, determining topic-specific influence values based on the number of registered followers, and clustering related topics through user similarity metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional follower count metrics are used to identify influential users, then the identification process is simple, but the accuracy of identifying truly influential users for specific topics is low
Solution Approach 1:
The patent segments the identification process into multiple components: topic detection from messages, user-interest mapping, influence score calculation based on follower counts and topic relevance, and ranking. This segmentation allows the system to move beyond simple follower counts by adding topic-specific analysis while maintaining manageable system complexity through modular processing steps.
Solution Approach 2:
The patent changes the parameters used for influence measurement from a single metric (total follower count) to multiple parameters including topic-specific follower counts, message frequency, engagement metrics, and relevance scores. This multi-parameter approach improves measurement precision by capturing different dimensions of user influence relevant to specific topics.
2Measurement precision
If comprehensive data analysis is performed to identify related topics, then the accuracy of topic identification improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing messages to extract and store topic keywords, user interests, and relationship mappings before actual analysis is needed. This preprocessing creates ready-to-use data structures that speed up the actual topic identification and influence calculation processes, reducing processing time while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The patent implements efficient algorithms that skip unnecessary processing steps by leveraging the pre-processed data structures. Once topic keywords and user interests are extracted, the system can quickly match new messages against these predefined categories rather than performing full-text analysis, thus rushing through the identification process while maintaining accuracy.
Data Source
AI summary
Method of analyzing data from an online social network having a set of network users. The method includes obtaining topic-specific messages sent by the network users. The messages have social content provided by the networks users, wherein the messages include a marker of interest in the social content. The marker of interest is associated with a topic of the social network. The method also includes identifying the network users that sent the messages having the marker of interest in the social content as interested users of the topic. The interested users are a subset of the set of network users. The method also includes determining a topic-specific influence (TSI) value of a designated user from the interested users in the subset. The TSI value of the designated user is based on a number of the interested users that are registered to receive the messages from the designated user.