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Event Entry
What and Who
Shaping Social Activity by Incentivizing Users
Manuel Gomez Rodriguez
MMCI
Joint Lecture Series
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Wednesday, 7 January 2015
12:15
60 Minutes
E1 5
002
Saarbrücken
Abstract
Events in an online social network can be categorized roughly into
endogenous events, where users just respond to the actions of their
neighbors within the network, or exogenous events, where users take
actions due to drives external to the network. How much external drive
should be provided to each user, such that the network activity can be
steered towards a target state? In this paper, we model social events
using multivariate Hawkes processes, which can capture both endogenous
and exogenous event intensities, and derive a time dependent linear
relation between the intensity of exogenous events and the overall
network activity. Exploiting this connection, we develop a convex
optimization framework for determining the required level of external
drive in order for the network to reach a desired activity level. We
experimented with event data gathered from Twitter, and show that our
method can steer the activity of the network more accurately than
alternatives.
endogenous events, where users just respond to the actions of their
neighbors within the network, or exogenous events, where users take
actions due to drives external to the network. How much external drive
should be provided to each user, such that the network activity can be
steered towards a target state? In this paper, we model social events
using multivariate Hawkes processes, which can capture both endogenous
and exogenous event intensities, and derive a time dependent linear
relation between the intensity of exogenous events and the overall
network activity. Exploiting this connection, we develop a convex
optimization framework for determining the required level of external
drive in order for the network to reach a desired activity level. We
experimented with event data gathered from Twitter, and show that our
method can steer the activity of the network more accurately than
alternatives.
Contact
Jennifer Müller
2900
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Jennifer Müller, 12/18/2014 14:41
Jennifer Müller, 09/22/2014 11:09 -- Created document.
Jennifer Müller, 09/22/2014 11:09 -- Created document.