Social influence analysis in microblogging platforms - a topic-sensitive based approach

The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need

A Social Influence Analysis of Perceived Organizational Support

This dissertation examined the effects of social influence on employees' perceptions of organizational support (POS). An important characteristic of POS is that it reflects an employee's subjective evaluation of the treatment he or she receives from the organization. Employees' interactions with their coworkers, then, may have an important influence on their POS. As a result, the development of POS may be a social process rather than solely an intrapsychic one. However, the majority of POS research has focused on how an individual employee's personal experiences with an organization affect his/her POS and largely ignored social factors. To address this gap in the literature, I argue that advice ties between employees will be related to similarity in POS because they serve as a source of social information. Friendship ties, on the other hand, will result in similarity in POS because they are utilized for social comparison. Finally, role model ties will result in similarity in POS because employees learn from the perceptions, attitudes, and behaviors of others they respect and admire. In addition, I explored the differential effects of strong and weak ties and muliplex versus simplex ties on similarity in POS. My expectation was that strong ties and multiplex ties would be more influential than weak ties and simplex ties. Finally, I explored the effects reciprocated and non-reciprocated ties with the expectation that reciprocated ties would be more highly associated with POS because they are characterized by information sharing. Social network methods were utilized to test hypotheses among 93 admissions department employees at a university in the eastern United States. Results indicated that when reciprocated ties were considered, employees tended to have POS that are similar to those of their strong role model ties, strong advice-role model ties, and strong friend-advice-role model ties. However, when reciprocity was not a requirement for strong ties between employees, only strong friend-advice-role model ties were related to similarity in POS. This pattern of results suggests that strong, multiplex ties in which two-way information sharing occured were more likely to lead to similarity in POS. Implications were drawn from these findings, and suggestions for future research were made

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding