Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2025

Abstract

The popular problem of Influence Maximization (IM) asks for the k users who can maximize the influence of a fixed post in a social network. In contrast, the problem of Content- Aware Influence Maximization (CAIM) asks for the k features to form a viral tunable post in a social network starting its diffusion from a fixed set of initial adopters. CAIM paves the way for a number of novel problems to be studied that altogether can lead to the development of a system that would be valuable for advertisers who manage social network pages. This holds since features (brands) in CAIM map to specific social network pages and each advertiser of a certain page can utilize their own feature along with others in a variety of ways to form a proper content for influence and subscription maximization purposes. In this article, we present our content-based perspective about how such a system (named b2biers) can be built, the technical challenges about it, and the novel services that it can yield to every kind of brands and advertisers running the brand pages.

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

ACM SIGKDD Explorations Newsletter

Volume

27

Issue

1

First Page

32

Last Page

51

ISSN

1931-0145

Identifier

10.1145/3748239.374824

Publisher

ACM

Additional URL

https://doi.org/10.1145/3748239.374824

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