Balancing Spreads of Influence in a Social Network

Becker, Ruben; Coró, Federico; D’Angelo, Gianlorenzo; Gilbert, Hugo

doi:10.1609/aaai.v34i01.5327

Cited by 21 publications

(17 citation statements)

References 15 publications

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“…As for the event dimension, a large share of the messages (49.68%) mentioned some type of event, whereas 45.33% did not (the remaining 4.68% were classified as “other” or got no response). This finding is consistent with previous research documenting that nonevent content is prominent on social media because people share personal information, feelings, and random thoughts (Becker, Naaman, and Gravano 2011).…”

Section: Datasupporting

confidence: 93%

Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics

Gelper¹,

Peres²,

Eliashberg³

2018

Journal of Marketing Research

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Before their launch, many new products generate word of mouth (WOM) on social media. Such WOM typically increases toward the release date and contains sudden spikes. These spikes capture manifestations of peak consumer attention and are therefore of managerial importance, yet they have not received research attention. This article is the first to provide a comprehensive descriptive treatment of WOM spikes. The authors propose a conceptual framework to present spikes as a standalone WOM dimension and explain their emergence. They employ a robust filtering procedure to detect spikes and apply it in a data set of 90,000 prerelease online WOM messages on 157 Hollywood movies. The results indicate that prerelease spikes are widely prevalent: While some of them are event-driven, emerging in response to firm-created communications (e.g., trailer release), they are far more likely to emerge spontaneously. Content analysis reveals that WOM in spikes is more positive in sentiment and is more likely to deal with factual details than is WOM outside spikes. Prerelease WOM spikes also contribute significantly to the predictability of future product sales.

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Section: Datasupporting

confidence: 93%

Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics

Gelper¹,

Peres²,

Eliashberg³

2018

Journal of Marketing Research

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“…The literature is rich, to the point that times seems ripe for an in-depth survey on the topic. Due to space limitations, we discuss here only the relationship between our work and the most relevant algorithmic contributions to polarization reduction [7,9,16,26,28,41,43,44,48,58].…”

Section: Related Workmentioning

confidence: 99%

“…A first important difference of our work with respect to most previous contributions is that they consider a network of users, with edges representing notions such as friendship or endorsement (e.g., retweets) [7,9,16,26,28,41,48,58]. We focus instead on networks of content, such as web pages linked to each other, or products that are connected when similar.…”

Section: Related Workmentioning

confidence: 99%

“…An interesting line of work studies how to reduce polarization in the content seen by the users, by adapting information diffusion approaches through better selection of the seed set for cascades [7,9,28,41,57], or by directly acting on recommendation systems [54]. These methods can not be adapted to the problem we study, as they do not act on the graph of content, but on that of users.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Haddadan

Menghini

Riondato

et al. 2021

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

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The topology of the hyperlink graph among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a "polarized" bubble with no access to other opinions. We model readers' behavior as random walks. A node is in a "polarized" bubble if the expected length of a random walk from it to a page of different opinion is large. The structural bias of a graph is the sum of the radii of highly-polarized bubbles. We study the problem of decreasing the structural bias through edge insertions. "Healing" all nodes with high polarized bubble radius is hard to approximate within a logarithmic factor, so we focus on finding the best 𝑘 edges to insert to maximally reduce the structural bias. We present RePBubLik, an algorithm that leverages a variant of the random walk closeness centrality to select the edges to insert. RePBubLik obtains, under mild conditions, a constant-factor approximation. It reduces the structural bias faster than existing edge-recommendation methods, including some designed to reduce the polarization of a graph.

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“…2. To the best of our knowledge, the spreading of multiple information with the independent cascade model is only studied in scenarios different from election control, e.g., (Becker et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

Castiglioni

Ferraioli

Gatti

et al. 2021

jair

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We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases. More precisely, in the case of seeding, we show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to election manipulation when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges, and to the basic case of social influence maximization/minimization in the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold in a re-optimization variant, where the manipulator already knows an optimal solution to the problem and computes a new solution once a local modification occurs, e.g., the removal/addition of a single edge.

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Balancing Spreads of Influence in a Social Network

Cited by 21 publications

References 15 publications

Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics

Talk Bursts: The Role of Spikes in Prerelease Word-of-Mouth Dynamics

RePBubLik: Reducing Polarized Bubble Radius with Link Insertions

Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

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