Preprint
Estimating Exposure to Information on Social Networks
ArXiv.org
07/13/2022
DOI: 10.48550/arxiv.2207.05980
Abstract
This paper considers the problem of estimating exposure to information in a
social network. Given a piece of information (e.g., a URL of a news article on
Facebook, a hashtag on Twitter), our aim is to find the fraction of people on
the network who have been exposed to it. 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
difficult to be estimated 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.
Details
- Title: Subtitle
- Estimating Exposure to Information on Social Networks
- Creators
- Buddhika NettasingheKowe KadomaMor NaamanVikram Krishnamurthy
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2207.05980
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 07/13/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984423765002771
Metrics
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