ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging

As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided

Identifying Influential Users Of Micro-Blogging Services: A Dynamic Action-Based Network Approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our action-based user influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

IDENTIFYING INFLUENCERS FOR PSYOP

Social media has become one of the primary modes of communication throughout the world, especially in developed countries. Nearly every user of social media in its various forms or applications has an audience he or she can influence and a set of influencers from which he or she receives information. U.S. Psychological Operations (PSYOP) personnel focus on influencing foreign target audiences in their audience’s own language but have been slow to adapt to the use of social media as a means of influence. Drawing from principles used in influencer marketing, we ask, How can U.S. PSYOP forces and their partners best identify social media influencers with whom they can partner in their effort to change the behavior of foreign target audiences? Through this study, we identified the main factors for influence on social media using both quantitative and qualitative analysis and developed a decision-making tool to identify the key communicators, in particular social media influencers, who can elicit the desired behavioral change in a target audience. The seven-category influencer scorecard we created provides a low-tech, situationally adaptable method for identifying influencers with whom U.S. PSYOP can partner to execute a PSYOP series.Major, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited