10,446 research outputs found
CasGCN: Predicting future cascade growth based on information diffusion graph
Sudden bursts of information cascades can lead to unexpected consequences
such as extreme opinions, changes in fashion trends, and uncontrollable spread
of rumors. It has become an important problem on how to effectively predict a
cascade' size in the future, especially for large-scale cascades on social
media platforms such as Twitter and Weibo. However, existing methods are
insufficient in dealing with this challenging prediction problem. Conventional
methods heavily rely on either hand crafted features or unrealistic
assumptions. End-to-end deep learning models, such as recurrent neural
networks, are not suitable to work with graphical inputs directly and cannot
handle structural information that is embedded in the cascade graphs. In this
paper, we propose a novel deep learning architecture for cascade growth
prediction, called CasGCN, which employs the graph convolutional network to
extract structural features from a graphical input, followed by the application
of the attention mechanism on both the extracted features and the temporal
information before conducting cascade size prediction. We conduct experiments
on two real-world cascade growth prediction scenarios (i.e., retweet popularity
on Sina Weibo and academic paper citations on DBLP), with the experimental
results showing that CasGCN enjoys a superior performance over several baseline
methods, particularly when the cascades are of large scale
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Scalable Privacy-Compliant Virality Prediction on Twitter
The digital town hall of Twitter becomes a preferred medium of communication
for individuals and organizations across the globe. Some of them reach
audiences of millions, while others struggle to get noticed. Given the impact
of social media, the question remains more relevant than ever: how to model the
dynamics of attention in Twitter. Researchers around the world turn to machine
learning to predict the most influential tweets and authors, navigating the
volume, velocity, and variety of social big data, with many compromises. In
this paper, we revisit content popularity prediction on Twitter. We argue that
strict alignment of data acquisition, storage and analysis algorithms is
necessary to avoid the common trade-offs between scalability, accuracy and
privacy compliance. We propose a new framework for the rapid acquisition of
large-scale datasets, high accuracy supervisory signal and multilanguage
sentiment prediction while respecting every privacy request applicable. We then
apply a novel gradient boosting framework to achieve state-of-the-art results
in virality ranking, already before including tweet's visual or propagation
features. Our Gradient Boosted Regression Tree is the first to offer
explainable, strong ranking performance on benchmark datasets. Since the
analysis focused on features available early, the model is immediately
applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective
Content Analysi
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