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Identifying Influential Users in On-line Support Forums using Topical Expertise and Social Network Analysis

Published: 25 August 2015 Publication History

Abstract

On-line support forums are a common method for businesses to provide product support for customers. In addition to trouble-shooting and how-to guides, on-line forums also serve the important purpose of allowing customers to interact and discuss the business's products. These interaction are an important factor in influencing customer opinions, and subsequently the adoption and use of products and services. The identification of influential users on these forums would therefore enable businesses to more effectively disseminate information and communicate with customers.
In this paper we develop a method for identifying influential users in support forums using topical expertise and social network analysis. One of the key challenges when analyzing influence in this context is that the users are generally less socially active than users on other social networks such as Twitter and Facebook. In order to address this issue we have taken a broader view of a social network and considered all of the users that a particular user has interacted with instead of just the subset of users for which there is an explicit relationship. The user's expertise in a particular category is then used to determine the weight or influence of each individual interaction. Finally, the influence of the top influential users is then categorized as positive or negative based on sentiment analysis of their posts.
We have demonstrated our approach using data from the Cisco Support Community, an on-line question and answer forum for computer networking products and services. Preliminary results show that the method was able to identify influential users in a discussion forum for network security products.

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      cover image ACM Conferences
      ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015
      August 2015
      835 pages
      ISBN:9781450338547
      DOI:10.1145/2808797
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 25 August 2015

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      • (2022) Identifying the Top- k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation IEEE Access10.1109/ACCESS.2022.321304410(107809-107845)Online publication date: 2022
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