Topic-Based Influence Computation in Social Networks Under Resource Constraints

Abstract

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy).

Topic-based influential user detection: a survey

“…While very few approaches have used manually handcrafted keywords to define multiple topics, [14,131], various other techniques, such as topic modeling [7,9,13,20,23,28,37,39,47,60,64,67,82,84,96,98,104,108,109,111,115,117,117,121,125,130,132], machine learning [85,108], and platform structures [50,126] are used to infer multiple topics in the literature. The way content is organized on a platform can be considered as a representation of a topic based on platform structures; e.g., a board on the Pinterest platform discusses a single topic.…”

“…Manually hand-crafting precise keywords sets denoting multiple topics is very challenging and rarely explored [14,131] as a multiple topic detection techniques in the literature. In this section, we discuss multiple topic-based IUD approaches that use a set of keywords to denote each topic.…”

Modified fuzzy sentiment analysis approach based on user ranking suitable for online social networks

“…Recent approaches to tackling influence analysis presumed that the graph structure is static, or even in case of being dynamic, the data is entirely known and gathered in a database. The issue of determining influential users and tracking them in dynamic social networks in a limited real world-data resource was discussed by Bingol et al [22]. This work was based on a local fresh copy of data and calculated influence scores depending on concluded texts and networks data.…”

Topic Influence Analysis Based on User Intimacy and Social Circle Difference

“…Bingol et al [25] studied the problem of identifying and maintaining influential users in evolving social network. In which, they proposed an algorithm by probing the relationships for global influence computation as well as posts for topic-based influence computation.…”