Correlated Cascades: Compete or Cooperate

Zarezade, Ali; Khodadadi, Ali; Farajtabar, Mehrdad; Rabiee, Hamid R.; Zha, Hongyuan

doi:10.1609/aaai.v31i1.10483

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…We evaluate OMM on two tasks: predicting attention market share and exposing relationships between online items. For the predictive task, OMM outperforms the current state of the art in market share modeling (Correlated Cascades (Zarezade et al 2017) and Competing Products (Valera and Gomez-Rodriguez 2015)) and predictive baselines on both datasets. For the second task, we leverage the OMM to expose the relations between opinions on the two platforms.…”

Section: Introductionmentioning

confidence: 92%

“…We set N p i (t) to be the number of opinionated posts conveying opinion i on platform p on time t, and λ p i (t) to be its conditional intensity. Using the notion of limited attention (Zarezade et al 2017), we relate λ p i (t) to λ p (t) in Eq. ( 5) by introducing the market share s p i (t) ∈ [0, 1] as the fraction of opinion i posts on platform p. That is,…”

Section: The Omm Modelmentioning

confidence: 99%

“…1) uses a discrete-time Hawkes process to estimate the size of the opinion attention marketthat is, the daily number of postings featuring opinions. The Hawkes process has been widely used to model online attention Zarezade et al 2017) due to its ability to account for exogenous factors and the endogenous "word-of-mouth" through its self-and cross-exciting property. The second tier of OMM (bottom row in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…We have observed a lack of recent research in this area, with few works dating after 2017. Closely related to our proposal is the Correlated Cascades (CC) model (Zarezade et al 2017), a variant of the multivariate Hawkes process to model product adoption across a set of competing products in a social network. It estimates the interaction parameter β, tuning the market cooperation or competition level.…”

Section: Introductionmentioning

confidence: 99%

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Opinion Market Model: Stemming Far-Right Opinion Spread Using Positive Interventions

Calderon,

Ram,

Rizoiu

2024

ICWSM

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Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.

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Section: Introductionmentioning

confidence: 92%

Section: The Omm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Opinion Market Model: Stemming Far-Right Opinion Spread Using Positive Interventions

Calderon,

Ram,

Rizoiu

2024

ICWSM

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“…While significant work has focused on predicting individual cascades, less attention has been invested in predicting the popularity of a group of cascades. For example, several works predict the aggregate volume of user activities on Twitter via Hawkes processes that model the events around a group of cascades (Valera and Gomez-Rodriguez, 2015 ; Zarezade et al, 2017 ). Theoretical models that capture the spread of social-influence when a group of competitive cascades evolve over a network have also been proposed (He et al, 2012 ; Lu et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Modeling information diffusion in social media: data-driven observations

et al. 2023

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Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was “to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution.” In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.

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Weaving Information Propagation: Modeling the Way Information Spreads in Document Collections

Huyghues-Despointes

Khouas²,

Velcin

et al. 2019

Advances in Artificial Intelligence

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Information usually spreads between people by the mean of textual documents. During such propagation, a piece of information can either remain the same or mutate. We propose to formulate information spread with a set of time-ordered document chains along which some information has likely been transmitted. This formulation is different from the usual graph view of a transmission process as it integrates a notion of lineage of the information. We also propose a way to construct a candidate set of document chains for the information propagation in a corpus of documents. We show that most of the chains have been judged as plausible by human experts.

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Correlated Cascades: Compete or Cooperate

Cited by 19 publications

References 15 publications

Opinion Market Model: Stemming Far-Right Opinion Spread Using Positive Interventions

Opinion Market Model: Stemming Far-Right Opinion Spread Using Positive Interventions

Modeling information diffusion in social media: data-driven observations

Weaving Information Propagation: Modeling the Way Information Spreads in Document Collections

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