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One of the challenges in social media research is that, often times, researchers or third parties could not obtain the massive of data collected by a limited number of “big brothers” (e.g., Facebook and Google). In this paper, we shed light on leveraging social network topological properties and local information to effectively conduct search in Online Social Networks (OSN). The problem we focus on is to discover the reachability of a group of target people in an OSN, particularly from the perspective of a third-party analyst who does not have full access to the OSN. We developed effective and efficient detection techniques which demand only a small number of queries to discover people's connections (e.g. friendship) in the OSN. After conducting experiments on real-world data sets, we found that our proposed techniques perform as well as the centralized detection algorithm, which assumes the availability of the global information in the OSN.
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This content will become publicly available on December 31, 2025
Estimating Exposure to Information on Social Networks
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 that is based on sampling the network uniformly and a method that non-uniformly samples the network motivated by the Friendship Paradox. We provide theoretical results that 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.
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- Award ID(s):
- 2112457
- PAR ID:
- 10607959
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Social Computing
- Volume:
- 7
- Issue:
- 1-4
- ISSN:
- 2469-7818
- Page Range / eLocation ID:
- 1 to 24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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