We present a quantitative study that links collective attention across two social media platforms -- YouTube and Twitter, around videos of controversial political topics.
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We propose a new model, Radflow, for networks of time series that influence each other.
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It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades.
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We present SupMMD, a novel technique for generic and update summarization of document collections based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity.
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In this project, we aim to link attention metrics and communication strategies to real world actions. In particular, we start by contrasting popularity and engagement of online social movements. We then link the measurements to real-world metrics of these activities, as measured by participant turnout, election outcome, legislative success, and others. Answers to these questions will empower content producers, consumers, and hosting platforms to channel attention in mutually beneficial, and socially responsible ways.
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We propose an end-to-end model which generates captions for images embedded in news articles. News images present two key challenges: they rely on real-world knowledge, especially about named entities, and they typically have linguistically rich captions that include uncommon words. We address both.
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ASNETs is a neural network architecture that can learn how to solve large planning and sequential decision making problems in a domain, from examples of plans or policies for small problems in that domain.
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This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects. It calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.
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Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical components.
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Little is known about how human attention is allocated over the large-scale networks used by most video hosting sites and about the impacts of the recommender systems they use. In this paper, we propose a model that accounts for the network effects for predicting video popularity, and we show it consistently outperforms the baselines.
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In this project, we aim to link attention metrics and communication strategies to real world actions. In particular, we start by contrasting popularity and engagement of online social movements. We then link the measurements to real-world metrics of these activities, as measured by participant turnout, election outcome, legislative success, and others. Answers to these questions will empower content producers, consumers, and hosting platforms to channel attention in mutually beneficial, and socially responsible ways.
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