Estimating Exposure to Information on Social Networks

Buddhika Nettasinghe; Kowe Kadoma; Mor Naaman; Vikram Krishnamurthy

doi:10.1145/3688599

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Estimating Exposure to Information on Social Networks

Journal article

Open access

Peer reviewed

Estimating Exposure to Information on Social Networks

Buddhika Nettasinghe, Kowe Kadoma, Mor Naaman and Vikram Krishnamurthy

ACM transactions on social computing, Vol.7(1-4), pp.1-24

12/31/2024

DOI: 10.1145/3688599

Appears in UI Libraries Support Open Access

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Abstract

Estimating exposure to information on a social network is a problem with important consequences for our society. The exposure estimation problem involves finding the fraction of people on the network who have been exposed to a piece of information (e.g., a URL of a news article on Facebook, a hashtag on Twitter). The exact value of exposure to a piece of information is determined by two features: the structure of the underlying social network and the set of people who shared the piece of information. Often, both features are not publicly available (i.e., access to the two features is limited only to the internal administrators of the platform) and are difficult to estimate from data. As a solution, we propose two methods to estimate the exposure to a piece of information in an unbiased manner: a vanilla method which is based on sampling the network uniformly and a method which non-uniformly samples the network motivated by the Friendship Paradox. We provide theoretical results which characterize the conditions (in terms of properties of the network and the piece of information) under which one method outperforms the other. Further, we outline extensions of the proposed methods to dynamic information cascades (where the exposure needs to be tracked in real-time). We demonstrate the practical feasibility of the proposed methods via experiments on multiple synthetic and real-world datasets.

Information Retrieval

Information systems

Social networks

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Details

Title: Subtitle: Estimating Exposure to Information on Social Networks
Creators: Buddhika Nettasinghe - University of Iowa, Business Analytics
Kowe Kadoma - , , ,
Mor Naaman - , , ,
Vikram Krishnamurthy - Cornell University
Resource Type: Journal article
Publication Details: ACM transactions on social computing, Vol.7(1-4), pp.1-24
Publisher: Association for Computing Machinery (ACM)
DOI: 10.1145/3688599
ISSN: 2469-7818
eISSN: 2469-7826
Language: English
Date published: 12/31/2024
Academic Unit: Business Analytics
Record Identifier: 9984740658202771

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