Search Results for author: Siwar Jendoubi

Found 8 papers, 0 papers with code

Evidential positive opinion influence measures for viral marketing

no code implementations11 Jul 2019 Siwar Jendoubi, Arnaud Martin

We also use an influence maximization model that the set of detected influencers for each scenario.

A reliability-based approach for influence maximization using the evidence theory

no code implementations30 Jun 2017 Siwar Jendoubi, Arnaud Martin

Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network.

Dynamic time warping distance for message propagation classification in Twitter

no code implementations26 Jan 2017 Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Boutheina Ben Yaghlane, Hend Ben Hadji

In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN).

Dynamic Time Warping General Classification

Two Evidential Data Based Models for Influence Maximization in Twitter

no code implementations20 Jan 2017 Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Ben Hend, Ben Boutheina

In this paper, we propose two evidential influence maximization models for Twitter social network.

Social and Information Networks

Maximizing positive opinion influence using an evidential approach

no code implementations20 Oct 2016 Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Hend Hadji, Boutheina Yaghlane

In this paper, we propose a new data based model for influence maximization in online social networks.

Second-Order Belief Hidden Markov Models

no code implementations22 Jan 2015 Jungyeul Park, Mouna Chebbah, Siwar Jendoubi, Arnaud Martin

The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.

Classification of Message Spreading in a Heterogeneous Social Network

no code implementations22 Jan 2015 Siwar Jendoubi, Arnaud Martin, Ludovic Liétard, Boutheina Ben Yaghlane

We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier.

General Classification

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