21 research outputs found
Discovering latent influence in online social activities via shared cascade poisson processes
Many people share their activities with others through on-line communities. These shared activities have an impact on other users ’ activities. For example, users are likely to become interested in items that are adopted (e.g. liked, bought and shared) by their friends. In this paper, we pro-pose a probabilistic model for discovering latent influence from sequences of item adoption events. An inhomogeneous Poisson process is used for modeling a sequence, in which adoption by a user triggers the subsequent adoption of the same item by other users. For modeling adoption of multiple items, we employ multiple inhomogeneous Poisson processes, which share parameters, such as influence for each user and relations between users. The proposed model can be used for finding influential users, discovering relations between users and predicting item popularity in the future. We present an efficient Bayesian inference procedure of the proposed model based on the stochastic EM algorithm. The effectiveness of the proposed model is demonstrated by using real data sets in a social bookmark sharing service
Modeling trend progression through an extension of the Polya Urn Process
Knowing how and when trends are formed is a frequently visited research goal.
In our work, we focus on the progression of trends through (social) networks.
We use a random graph (RG) model to mimic the progression of a trend through
the network. The context of the trend is not included in our model. We show
that every state of the RG model maps to a state of the Polya process. We find
that the limit of the component size distribution of the RG model shows
power-law behaviour. These results are also supported by simulations.Comment: 11 pages, 2 figures, NetSci-X Conference, Wroclaw, Poland, 11-13
January 2016. arXiv admin note: text overlap with arXiv:1502.0016
Shaping Social Activity by Incentivizing Users
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
Modeling Adoption and Usage of Competing Products
The emergence and wide-spread use of online social networks has led to a
dramatic increase on the availability of social activity data. Importantly,
this data can be exploited to investigate, at a microscopic level, some of the
problems that have captured the attention of economists, marketers and
sociologists for decades, such as, e.g., product adoption, usage and
competition.
In this paper, we propose a continuous-time probabilistic model, based on
temporal point processes, for the adoption and frequency of use of competing
products, where the frequency of use of one product can be modulated by those
of others. This model allows us to efficiently simulate the adoption and
recurrent usages of competing products, and generate traces in which we can
easily recognize the effect of social influence, recency and competition. We
then develop an inference method to efficiently fit the model parameters by
solving a convex program. The problem decouples into a collection of smaller
subproblems, thus scaling easily to networks with hundred of thousands of
nodes. We validate our model over synthetic and real diffusion data gathered
from Twitter, and show that the proposed model does not only provides a good
fit to the data and more accurate predictions than alternatives but also
provides interpretable model parameters, which allow us to gain insights into
some of the factors driving product adoption and frequency of use
Modelling Heterogeneous Effects in Network Contagion: Evidence from the Steam Community
This study considers heterogeneous effects of reviews and social interactions on diffusion or contagion of new products in a networked setting, using a sample of interconnected public user profiles from the Steam Community. Ownership and reviews of two cult hit independent games – The Binding of Isaac: Rebirth, and To the Moon – are analyzed over a period of four years. This data was fit with a Hawkes Process Hazard Regression Model with exponential decay kernels for each game, yielding estimates of scale and duration of incremental heterogeneous actions within the network. This analysis finds strong, short term, additive, and marginally decreasing, social contagion effects from other users buying games, with much smaller, but also far more durable and highly significant, effects from review posting behavior in the network, independent of review quality. This seems to suggest that review influence, while still distinguishable from network homophily, is unlikely to lead to cascade effects