Maximizing the Diversity of Exposure in a Social Network

Aslay, Çiğdem; Matakos, Antonis; Galbrun, Esther; Gionis, Aristides

doi:10.1109/icdm.2018.00102

Cited by 24 publications

(18 citation statements)

References 27 publications

(40 reference statements)

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“…Two works closely related to ours are the ones of Aslay et al (2018) and Matakos and Gionis (2018). Indeed, both of these works aim to break filter bubbles.…”

Section: Related Workmentioning

confidence: 96%

Balancing Spreads of Influence in a Social Network

Becker

Coró

D’Angelo

et al. 2020

AAAI

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The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. To tackle this issue, Garimella et al. (NIPS'17) modeled the spread of these viewpoints, also called campaigns, using the independent cascade model introduced by Kempe, Kleinberg and Tardos (KDD'03) and studied an optimization problem that aims to balance information exposure when two opposing campaigns propagate in a network. This paper investigates a natural generalization of this optimization problem in which μ different campaigns propagate in the network and we aim to maximize the expected number of nodes that are reached by at least ν or none of the campaigns, where μ ≥ ν ≥ 2. Following Garimella et al., despite this general setting, we also investigate a simplified one, in which campaigns propagate in a correlated manner. While for the simplified setting, we show that the problem can be approximated within a constant factor for any constant μ and ν, for the general setting, we give reductions leading to several approximation hardness results when ν ≥ 3. For instance, assuming the gap exponential time hypothesis to hold, we obtain that the problem cannot be approximated within a factor of n−g(n) for any g(n) = o(1) where n is the number of nodes in the network. We complement our hardness results with an Ω(n−1/2)-approximation algorithm for the general setting when ν = 3 and μ is arbitrary.

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“…Two works closely related to ours are the ones of Aslay et al (2018) and Matakos and Gionis (2018). Indeed, both of these works aim to break filter bubbles.…”

Section: Related Workmentioning

confidence: 96%

Balancing Spreads of Influence in a Social Network

Becker

Coró

D’Angelo

et al. 2020

AAAI

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“…On the other hand, a very interesting recent line of work in the influence maximization literature considers other target outcomes besides maximum adoption (Matakos and Gionis 2018;Aslay et al 2018;Tsang et al 2019;Chen et al 2019;Pasumarthi et al 2015;Loukides and Gwadera 2018). As one example, Matakos and Gionis (2018) and Aslay et al (2018) consider maximizing the diversity of information shared in a social network. Are "overambitious seeding" considerations relevant in such a setting, or is seeding as widely as possible beneficial for promoting diversity?…”

Section: Resultsmentioning

confidence: 99%

When can overambitious seeding cost you?

Iyer

Adamic

2019

Appl Netw Sci

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In the classic "influence-maximization" (IM) problem, people influence one another to adopt a product and the goal is to identify people to "seed" with the product so as to maximize long-term adoption. Many influence-maximization models suggest that, if the number of people who can be seeded is unconstrained, then it is optimal to seed everyone at the start of the IM process. In a recent paper, we argued that this is not necessarily the case for social products that people use to communicate with their friends (Iyer and Adamic, The costs of overambitious seeding of social products. In: International Workshop on Complex Networks and Their Applications_273-286, 2018). Through simulations of a model in which people repeatedly use such a product and update their rate of subsequent usage depending upon their satisfaction, we showed that overambitious seeding can result in people adopting in suboptimal contexts, having bad experiences, and abandoning the product before more favorable contexts for adoption arise. Here, we extend that earlier work by showing that the costs of overambitious seeding also appear in more traditional threshold models of collective behavior, once the possibility of permanent abandonment of the product is introduced. We further demonstrate that these costs can be mitigated by using conservative seeding approaches besides those that we explored in the earlier paper. Synthesizing these results with other recent work in this area, we identify general principles for when overambitious seeding can be of concern in the deployment of social products.

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“…In the both sides condition, each learning agent received input from the source with the most similar and the most dissimilar belief on every round, providing a model of the "both sides" approach to persuasion. Prior work has sometimes assumed that exposure to diverse views can help prevent polarization or disinformation bubbles 43,44 . When learning was weighted by the HEW function, however, the learners again fractionated into subgroups possessing different stable beliefs.…”

Section: Si-2: Detailed Methods For Simulationsmentioning

confidence: 99%

A cognitive mechanism for the persistence of widespread false beliefs

Frigo¹,

Chen²,

Tt³

2020

Preprint

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Cognitive science commonly assumes human learners combine prior knowledge with new information to form statistically optimal beliefs. Why then are false beliefs so resilient and widespread? We develop and test a new hypothesis stemming from computational models of human category learning. In four experiments we found that learners weight conflicting information sources pursuant to a nonlinear function dependent on the learners’ initial beliefs. Highly agreeable sources receive strong weight preventing belief change, but this deference drops sharply and then flattens with increasing disparity, allowing even very distal sources to effect belief change. To understand the implications of this bias for the maintenance of false beliefs in society, we simulated groups of agents learning from one another and from a truth-telling oracle. The learning bias often led agents to fractionate into mutually distrustful groups or to converge on a common false belief. Thus, opposing belief bubbles in contemporary society may reflect stable states in a dynamic system composed of individual learners communicating their beliefs to one another and updating those beliefs according to an evidence-weighting function that strongly favors close agreement but, in the absence of such agreement, never completely discounts extreme voices.

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Maximizing the Diversity of Exposure in a Social Network

Cited by 24 publications

References 27 publications

Balancing Spreads of Influence in a Social Network

Balancing Spreads of Influence in a Social Network

When can overambitious seeding cost you?

A cognitive mechanism for the persistence of widespread false beliefs

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