Abstract
The advent of Online Social Networks (OSNs) has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. Considerable research has focused on the social influence phenomenon and its impact on OSNs. Social influence plays a crucial role in shaping people behavior and affecting human decisions in various domains.In this paper, we study the impact of social influence on offline dynamics to study human real-life behavior. We introduce Social Influence Deep Learning (SIDL), a framework that combines deep learning with network science for modeling social influence and predicting human behavior on real-world activities, such as attending an event or visiting a location. We propose different approaches at varying degree of network connectivity with the objective of facing two typical challenges of deep learning: interpretability and scalability.We validate and evaluate our approaches using data from Plancast, an Event-Based Social Network, and Foursquare, a Location-Based Social Network. Finally, we explore the usage of different deep learning architectures, and we discuss the correlation between social influence and users privacy presenting results and some notes of caution about the risks of sharing sensitive data.
Highlights
Information diffusion in techno-social systems has received tremendous interest in the last decade
Evaluation we evaluate the results of the Social Influence Deep Learning (SIDL) approaches and we compare them with two state of the art approaches, namely the Linear Threshold (LT) model proposed by Goyal et al (2010), and the Independent Cascade (IC) model of Saito et al (2008)
(i) they do not rely either on specific hand-crafted features or on topic affinity, which in turn may depend on the Online social network (OSN) analyzed and on the availability of metadata, and (ii) they both take as input only the action log Al and the social graph G
Summary
Information diffusion in techno-social systems has received tremendous interest in the last decade. The advent of Online Social Networks (OSNs), and their intrinsic multirelational data, has offered the opportunity to study the dynamics of information spread and influence propagation at a huge scale. The understanding of (2019) 4:34 how influence propagates in OSNs opens the door to a wide range of applications more beneficial for users, such as targeted advertising, viral marketing, and recommendation. This has become possible as OSNs do connect people, by providing a medium for spreading processes (Newman 2003; Albert and Barabási 2002; Castellano et al 2009), and (and most importantly) reveal preferences, activities, and interests of their users over the time
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