1,520 research outputs found
Learning user-specific latent influence and susceptibility from information cascades
Predicting cascade dynamics has important implications for understanding
information propagation and launching viral marketing. Previous works mainly
adopt a pair-wise manner, modeling the propagation probability between pairs of
users using n^2 independent parameters for n users. Consequently, these models
suffer from severe overfitting problem, specially for pairs of users without
direct interactions, limiting their prediction accuracy. Here we propose to
model the cascade dynamics by learning two low-dimensional user-specific
vectors from observed cascades, capturing their influence and susceptibility
respectively. This model requires much less parameters and thus could combat
overfitting problem. Moreover, this model could naturally model
context-dependent factors like cumulative effect in information propagation.
Extensive experiments on synthetic dataset and a large-scale microblogging
dataset demonstrate that this model outperforms the existing pair-wise models
at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Independent Asymmetric Embedding for Cascade Prediction on Social Networks
The prediction for information diffusion on social networks has great
practical significance in marketing and public opinion control. Cascade
prediction aims to predict the individuals who will potentially repost the
message on the social network. One kind of methods either exploit
demographical, structural, and temporal features for prediction, or explicitly
rely on particular information diffusion models. The other kind of models are
fully data-driven and do not require a global network structure. Thus massive
diffusion prediction models based on network embedding are proposed. These
models embed the users into the latent space using their cascade information,
but are lack of consideration for the intervene among users when embedding. In
this paper, we propose an independent asymmetric embedding method to learn
social embedding for cascade prediction. Different from existing methods, our
method embeds each individual into one latent influence space and multiple
latent susceptibility spaces. Furthermore, our method captures the
co-occurrence regulation of user combination in cascades to improve the
calculating effectiveness. The results of extensive experiments conducted on
real-world datasets verify both the predictive accuracy and cost-effectiveness
of our approach
Longitudinal Modeling of Social Media with Hawkes Process based on Users and Networks
Online social networks provide a platform for
sharing information at an unprecedented scale. Users generate
information which propagates across the network resulting in
information cascades. In this paper, we study the evolution of
information cascades in Twitter using a point process model
of user activity. We develop several Hawkes process models
considering various properties including conversational structure,
users’ connections and general features of users including the
textual information, and show how they are helpful in modeling
the social network activity. We consider low-rank embeddings
of users and user features, and learn the features helpful in
identifying the influence and susceptibility of users. Evaluation
on Twitter data sets associated with civil unrest shows that
incorporating richer properties improves the performance in
predicting future activity of users and memes
Influence Maximization with Fairness at Scale (Extended Version)
In this paper, we revisit the problem of influence maximization with
fairness, which aims to select k influential nodes to maximise the spread of
information in a network, while ensuring that selected sensitive user
attributes are fairly affected, i.e., are proportionally similar between the
original network and the affected users. Recent studies on this problem focused
only on extremely small networks, hence the challenge remains on how to achieve
a scalable solution, applicable to networks with millions or billions of nodes.
We propose an approach that is based on learning node representations for fair
spread from diffusion cascades, instead of the social connectivity s.t. we can
deal with very large graphs. We propose two data-driven approaches: (a)
fairness-based participant sampling (FPS), and (b) fairness as context (FAC).
Spread related user features, such as the probability of diffusing information
to others, are derived from the historical information cascades, using a deep
neural network. The extracted features are then used in selecting influencers
that maximize the influence spread, while being also fair with respect to the
chosen sensitive attributes. In FPS, fairness and cascade length information
are considered independently in the decision-making process, while FAC
considers these information facets jointly and considers correlations between
them. The proposed algorithms are generic and represent the first policy-driven
solutions that can be applied to arbitrary sets of sensitive attributes at
scale. We evaluate the performance of our solutions on a real-world public
dataset (Sina Weibo) and on a hybrid real-synthethic dataset (Digg), which
exhibit all the facets that we exploit, namely diffusion network, diffusion
traces, and user profiles. These experiments show that our methods outperform
the state-the-art solutions in terms of spread, fairness, and scalability
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