Gaps in Information Access in Social Networks?

Fish, Benjamin; Bashardoust, Ashkan; boyd, danah; Friedler, Sorelle A.; Scheidegger, Carlos; Venkatasubramanian, Suresh

doi:10.1145/3308558.3313680

Cited by 36 publications

(47 citation statements)

References 27 publications

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“…Recently, the influence maximization paradigm has raised some ethical challenges. These challenges have very diverse objectives as limiting the spread of a "bad" campaign by starting the spreading of a "good" campaign blocking the first one (Budak, Agrawal, and El Abbadi 2011), maximizing social welfare by controlling the spread of multiple campaigns and orienting them towards the right agents (Borodin et al 2017) or minimizing the access gap between the highly connected individuals and the poorly connected ones (Fish et al 2019). We here focus on works that tackle algorithmically the challenges related to diversity.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Balancing Spreads of Influence in a Social Network

Becker

Coró

D’Angelo

et al. 2020

AAAI

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“…These networks constitute a feedback channel and create an open culture for communication and knowledge exchange (Richter et al, 2016;Cetto et al, 2018). Because connections between individuals are as important as the individuals themselves, the opportunities that social networks offer to businesses increase their importance, especially for activities such as recruitment, marketing and business advancement or innovation (Fish et al, 2019). While explicit knowledge could be embedded in information repositories, tacit knowledge is embedded in the enterprise social networks, including the technology-enabled social networks that influence both individuals' emotional and behavioral reactions, which affect the performance outcomes.…”

Section: Croatian Information Technologies and Communication Companiesmentioning

confidence: 99%

Impact of information technologies and social networks on knowledge management processes in Middle Eastern audit and consulting companies

Raudeliūnienė

Albats

Kordab

2020

JKM

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Purpose The purpose of this study is to examine the impact of information technologies and technology-enabled social networks on the efficiency of knowledge management processes in the Middle Eastern audit and consulting companies. Design/methodology/approach Scientific literature analysis, structural equation modeling and expert evaluation (structured questionnaire) were used to develop the research model, collect data from the audit and consulting companies’ experts and test the research hypotheses. Findings The empirical results of this research supported the hypotheses stating that information technologies and social networks positively affect the knowledge management cycle, including five processes (acquisition, creation, storage, sharing and application) within the Middle Eastern audit and consulting companies. Research limitations/implications The research results were generated from the Middle Eastern audit and consulting companies, which form a limitation concerning the geographical area and the business sector. Practical implications From the results of this study, audit and consulting companies, as well as organizations and society broadly, would benefit via the positive effect of information technologies and technology-enabled social networks on the whole knowledge management cycle, which has a further impact on organizational performance. These practical implications are related to a more open, sharing culture that drives organizational performance to the members and stakeholders of organizations, which, in turn, benefits society. Originality/value This research analyzes information technologies and technology-enabled social networks’ impact on knowledge management processes, particularly in the context of the Middle Eastern audit and consulting companies. While the phenomena have received some attention in the prior scientific research, the studied context so far remained under-researched, where a gap is found in studying the knowledge management cycle as a whole.

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“…Group fairness has been first studied by Tsang et al [29]. Similarly, the definition of diversity for access gap is first introduced by Fish et al [7] as an individual-level definition of fairness in IM. Balance constraint as two-stage problem is introduced in Gershtein et al [8].…”

Section: Related Workmentioning

confidence: 99%

A Unifying Framework for Fairness-Aware Influence Maximization

Farnad

Babaki

Gendreau

2020

Companion Proceedings of the Web Conference 2020

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The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms. CCS CONCEPTS• Social and professional topics → User characteristics; • Theory of computation → Mathematical optimization; • Networks → Online social networks.

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Gaps in Information Access in Social Networks?

Cited by 36 publications

References 27 publications

Balancing Spreads of Influence in a Social Network

Balancing Spreads of Influence in a Social Network

Impact of information technologies and social networks on knowledge management processes in Middle Eastern audit and consulting companies

A Unifying Framework for Fairness-Aware Influence Maximization

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