Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models

Poux-Médard, Gaël; Velcin, Julien; Loudcher, Sabine

doi:10.1145/3460231.3474254

Cited by 11 publications

(20 citation statements)

References 12 publications

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“…This finding agrees with previous works, which stated that the most informative interactions within Twitter URL dataset occur within the 3 time steps before the possible retweet [12]. We also find that the vast majority of interactions are weak, matching with previous study's findings [12,16]. However, it seems that tweets still exert influence even a long time after being seen, but with lesser intensity.…”

Section: Discussionsupporting

confidence: 93%

“…1. Recent works address the various flaws observed in [12] and suggest a more general approach to the estimation of the interaction intensity parameters [16]. The latter model develops a scalable algorithm that correctly accounts for interacting processes but neglects the interactions' temporal aspect.…”

Section: Related Workmentioning

confidence: 99%

“…IMMSBM The Interactive Mixed-Membership Stochastic Block Model (IMMSBM) is a model that takes interactions between pieces of information into account to compute the probability of a (non-)contagion [16]. Note that this baseline does not take the position of the interacting pieces of information into account (timeindependent) and assumes that interactions are symmetric (the effect of A on B is the same as B on A).…”

Section: Clash Of the Contagionsmentioning

confidence: 99%

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Information Interaction Profile of Choice Adoption

Poux-Médard,

Velcin,

Loudcher

2021

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Interactions between pieces of information (entities) play a substantial role in the way an individual acts on them: adoption of a product, the spread of news, strategy choice, etc. However, the underlying interaction mechanisms are often unknown and have been little explored in the literature. We introduce an efficient method to infer both the entities interaction network and its evolution according to the temporal distance separating interacting entities; together, they form the interaction profile. The interaction profile allows characterizing the mechanisms of the interaction processes. We approach this problem via a convex model based on recent advances in multi-kernel inference. We consider an ordered sequence of exposures to entities (URL, ads, situations) and the actions the user exerts on them (share, click, decision). We study how users exhibit different behaviors according to combinations of exposures they have been exposed to. We show that the effect of a combination of exposures on a user is more than the sum of each exposure's independent effect-there is an interaction. We reduce this modeling to a non-parametric convex optimization problem that can be solved in parallel. Our method recovers state-of-the-art results on interaction processes on three real-world datasets and outperforms baselines in the inference of the underlying data generation mechanisms. Finally, we show that interaction profiles can be visualized intuitively, easing the interpretation of the model.

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Section: Discussionsupporting

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Clash Of the Contagionsmentioning

confidence: 99%

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Information Interaction Profile of Choice Adoption

Poux-Médard,

Velcin,

Loudcher

2021

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“…Their conclusion is that interactions can have a large overall effect of the datageneration processes (here retweeting mechanisms), but that most interactions have little influence. Later works tackling the problem from a similar perspective on several real-world datasets find similar results: significant interactions between clusters of memes are sparse [19,21]. This highlight the need to cluster memes so that it becomes possible to retrieve meaningful interaction terms.…”

Section: Introductionmentioning

confidence: 78%

Properties of Reddit News Topical Interactions

Poux-Médard¹,

Velcin²,

Loudcher³

2022

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Most models of information diffusion online rely on the assumption that pieces of information spread independently from each other. However, several works pointed out the necessity of investigating the role of interactions in real-world processes, and highlighted possible difficulties in doing so: interactions are sparse and brief. As an answer, recent advances developed models to account for interactions in underlying publication dynamics. In this article, we propose to extend and apply one such model to determine whether interactions between news headlines on Reddit play a significant role in their underlying publication mechanisms. After conducting an in-depth case study on 100,000 news headline from 2019, we retrieve state-of-the-art conclusions about interactions and conclude that they play a minor role in this dataset.

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“…En 2012, il a été montré que l'ordre d'apparition (ou la date) de tweets modifie notre réaction aux tweets suivants (Myers et Leskovec, 2012). En outre, la dimension temporelle n'importe que pour certains groupes bien définis d'information (Poux-Médard et al, 2021a) et ne perdure pas dans le temps (Cao et Sun, 2019). Il est donc nécessaire d'allier la notion de cluster à la notion de dynamique pour parvenir à une description correcte des processus de publication en ligne.…”

Section: Contexteunclassified

Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes

Poux-Médard¹,

Velcin²,

Loudcher³

2022

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lyon2.frRésumé. Le contenu textuel d'un document et sa date de publication sont corrélés. Par exemple, une publication scientifique est influencée par les précédents articles cités dans ladite publication. Utiliser cette corrélation permet d'améliorer la compréhension de grands corpus textuel datés. Cependant, cette tâche peut se compliquer lorsque les textes considérés sont courts ou possèdent des vocabulaires similaires. De plus, la corrélation entre texte et date est rarement parfaite. Nous développons une méthode répondant à ces limites, permettant de créer des clusters de documents en fonction de leur contenu et de leur date : le processus Powered Dirichlet-Hawkes (PDHP). Nous montrons que PDHP présente de meilleures performances que les modèles état de l'art (qu'il généralise) lorsque l'information textuelle ou temporelle est peu informative. Le PDHP se libère également de l'hypothèse d'une corrélation parfaite entre texte et date des documents. Enfin, nous illustrons une possible application sur des données réelles, provenant de Reddit.

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Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models

Cited by 11 publications

References 12 publications

Information Interaction Profile of Choice Adoption

Information Interaction Profile of Choice Adoption

Properties of Reddit News Topical Interactions

Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes

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