Approximate Algorithms for Data-Driven Influence Limitation

Medya, Sourav; Silva, Arlei; Singh, Ambuj K.

doi:10.1109/tkde.2020.3016293

Cited by 7 publications

(4 citation statements)

References 52 publications

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“…Social media platforms such as Facebook, Instagram, and YouTube use complex algorithms to determine the content to show in a user's feed. The algorithms are designed to prioritize content that generates high engagement in the form of likes, comments, and shares since they drive user activities and increase revenues [74]. However, prioritizing sensational and controversial posts over accurate and reliable information that derives engagement can contribute to spreading fake news [75,76].…”

Section: Social Media Algorithms That Prioritize Engagement Over the ...mentioning

confidence: 99%

Fake News in Marketing

Silva¹,

Rosário²,

Gestoso³

et al. 2023

Preprint

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Fake news was conventionally assumed to mean fabricated information published in newspapers and other mass media. It was mostly done to increase the paper's sales and was unlikely to have long-term impacts on society. Fake news in marketing concerns intentionally delivering false or misleading information regarding a product or service to influence consumer behavior. Despite the severity of these impacts, we noted that there is very little scientific research on fake news in marketing and consumer behavior. This systematic literature review (LRSB) aims to bridge this knowledge gap by searching and analysing relevant publications on "fake news in marketing" and synthesising data from 117 relevant studies, providing a framework for marketers and business leaders to reduce the spread and impact of online misinformation. The main highlights identify that the implementation and use of social media, shaping a presence on the internet, diffusion, and consumption of fake news have become a serious challenge and a dominant problem in the marketing sector, especially with the growth of digital marketing. Strategically, fake news spread can be categorized into various types, and the use of these tactics can produce short-term benefits such as increased engagement and instant sales, but they can also have severe long-term costs such as reputational damage, loss of revenues and market share, and discouraged investors and shareholders.

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Section: Social Media Algorithms That Prioritize Engagement Over the ...mentioning

confidence: 99%

Fake News in Marketing

Silva¹,

Rosário²,

Gestoso³

et al. 2023

Preprint

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show abstract

“…In network design, the goal is to modify the network so that an objective function modeling a desirable property is optimized. Examples of such objective functions include optimizing shortest path distances (traffic and sustainability improvement) [11,23,27,28], increasing centrality of target nodes by adding a small set of edges [7,18,26], optimizing the 𝑘-core [24,39], manipulating node similarities [10], and boosting/containing influence on social networks [5,20,25].…”

Section: Related Workmentioning

confidence: 99%

Balance Maximization in Signed Networks via Edge Deletions

Sharma

Gillani

Medya

et al. 2021

Proceedings of the 14th ACM International Conference on Web Search and Data Mining

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In signed networks, each edge is labeled as either positive or negative. The edge sign captures the polarity of a relationship. Balance of signed networks is a well-studied property in graph theory. In a balanced (sub)graph, the vertices can be partitioned into two subsets with negative edges present only across the partitions. Balanced portions of a graph have been shown to increase coherence among its members and lead to better performance. While existing works have focused primarily on finding the largest balanced subgraph inside a graph, we study the network design problem of maximizing balance of a target community (subgraph). In particular, given a budget 𝑏 and a community of interest within the signed network, we aim to make the community as close to being balanced as possible by deleting up to 𝑏 edges. Besides establishing NP-hardness, we also show that the problem is non-monotone and non-submodular. To overcome these computational challenges, we propose heuristics based on the spectral relation of balance with the Laplacian spectrum of the network. Since the spectral approach lacks approximation guarantees, we further design a greedy algorithm, and its randomized version, with provable bounds on the approximation quality. The bounds are derived by exploiting pseudo-submodularity of the balance maximization function. Empirical evaluation on eight realworld signed networks establishes that the proposed algorithms are effective, efficient, and scalable to graphs with millions of edges.

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“…These problems aim to optimize network properties or processes under network modifications. Examples include diameter [6], node centrality [5,19] shortest path [7,16,20,21], and influence spread [10,11,18,26] improvement. However, our objective is different from the ones considered by network design studies.…”

Section: Related Workmentioning

confidence: 99%

Network Robustness via Global k-cores

Dey¹,

Maity²,

Medya³

et al. 2020

Preprint

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Network robustness is a measure a network's ability to survive adversarial attacks. But not all parts of a network are equal. Kcores, which are dense subgraphs, are known to capture some of the key properties of many real-life networks. Therefore, previous work has attempted to model network robustness via the stability of its 𝑘-core. However, these approaches account for a single core value and thus fail to encode a global network resilience measure. In this paper, we address this limitation by proposing a novel notion of network resilience that is defined over all cores. In particular, we evaluate the stability of the network under node removals with respect to each node's initial core. Our goal is to compute robustness via a combinatorial problem: find 𝑏 most critical nodes to delete such that the number of nodes that fall from their initial cores is maximized. One of our contributions is showing that it is NPhard to achieve any polynomial factor approximation of the given objective. We also present a fine-grained complexity analysis of this problem under the lens of parameterized complexity theory for several natural parameters. Moreover, we show two applications of our notion of robustness: measuring the evolution of species and characterizing networks arising from different domains.

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Approximate Algorithms for Data-Driven Influence Limitation

Cited by 7 publications

References 52 publications

Fake News in Marketing

Fake News in Marketing

Balance Maximization in Signed Networks via Edge Deletions

Network Robustness via Global k-cores

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