Finding Influential Users in Social Media Using Association Rule Learning - Social Media Assignment Help

Assignment Task

Introduction

Online social networks are playing an important role in our society and have created a platform for people to communicate and express their thoughts. With the use of online social media, we have created a way to mimic real human communication in an online environment. Facebook alone attracts 1.3 billion users with 640 million minutes spent each month on the site.

Consequently, discovering trending topics or influential users is of interest for many researchers interested in areas such as marketing. Several studies have tried to identify user influence; however, most have used Page Rank Centrality or Degree Centrality based approaches to identify influential users. This paper builds on the initial discoveries on association rule learning in social networking sites:

In this article, we argue that users on Facebook groups are following each other and that it is possible to detect influential users and predict user participation. For example, if users A, B, C and D share common interests, there is a chance that if A, B, and C already have commented on a topic, D will also comment on it. Therefore, this paper relates to how users perform actions (e.g., comments or likes) on posts in Facebook pages. In addition, we use association rule learning to discover relationships between users in our dataset [6]. Given a list of posts from a specific domain, we extract users’ actions, such as comments and likes.

Using association rule learning on the data, we argue that it is possible to predict if a particular user will or will not participate on a post discussion based on the other users’ activity. This article has three major contributions: firstly, possibilities to identify influential users using association rule learning are presented; secondly, we present time performance of well-known for ranking users in social media together with our approach using association rule learning; and finally, we show how association rule learning can be used to predict user participation.

For evaluation, several experiments are conducted, which include building association rules that can be used to predict if a specific user will be active in a particular post. The prediction is done based on the activeness of users within current posts. In addition, an extended social network analysis is conducted to verify the findings of influential users.

The paper is organized as follows: in Section 2, related work is discussed; in Section 3, association rule learning and the evaluation metrics are discussed; in Section 4, the dataset is presented; and finally, the results are presented in Section 5 and discussed in Section 6.

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