Abstract
Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a new approach for multi-level influence assessment on multi-relational networks, such as Twitter. We define a social graph to model the relationships between users as a multiplex graph where users are represented by nodes, and links model the different relations between them (e.g., retweets, mentions, and replies). We explore how relations between nodes in this graph could reveal about the influence degree and propose a generic computational model to assess influence degree of a certain node. This is based on the conjunctive combination rule from the belief functions theory to combine different types of relations. We experiment the proposed method on a large amount of data gathered from Twitter during the European Elections 2014 and deduce top influential candidates. The results show that our model is flexible enough to to consider multiple interactions combination according to social scientists needs or requirements and that the numerical results of the belief theory are accurate. We also evaluate the approach over the CLEF RepLab 2014 data set and show that our approach leads to quite interesting results.
Highlights
Nowadays, online social networks, such as Twitter, gather people together and empower their relationships with new forms of cooperation and communication
We extend the model proposed in [6], we focus on extensibility, and we develop an algorithm for multi-level fusion of information about different relations and interactions
Experiments on the TEE 2014 data set show that the use of our approach leads to interesting results, and the method takes into account different relations and interaction patterns and provides a global influence score in the network
Summary
Online social networks, such as Twitter, gather people together and empower their relationships with new forms of cooperation and communication. As a result of its massive popularity, Twitter is exploited as a platform for very different purposes, such as marketing or political campaigns [1]. One of the most distinctive characteristics of Twitter is the information diffusion through social links. Links between users impact the information flow and indicate the user’s influence on others. Some users, called influentials, are more able than others to diffuse information to a huge number of users. Determining influential users in a network is a secret key of success for achieving a large-scale information diffusion at low cost
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