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Everyone's an influencer: quantifying influence on twitter
TLDR
It is concluded that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects and that predictions of which particular user or URL will generate large cascades are relatively unreliable.
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The Structural Virality of Online Diffusion
TLDR
This work proposes a formal measure of what it label “structural virality” that interpolates between two conceptual extremes: content that gains its popularity through a single, large broadcast and that which grows through multiple generations with any one individual directly responsible for only a fraction of the total adoption.
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Who says what to whom on twitter
TLDR
A striking concentration of attention is found on Twitter, in that roughly 50% of URLs consumed are generated by just 20K elite users, where the media produces the most information, but celebrities are the most followed.
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Bayesian approach to network modularity.
TLDR
This work presents an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network, implemented using a variational technique developed only in the past decade.
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Scalable Recommendation with Hierarchical Poisson Factorization
TLDR
H hierarchical Poisson matrix factorization is developed, a novel method for providing users with high quality recommendations based on implicit feedback, such as views, clicks, or purchases, and it is shown that it more accurately captures the long-tailed user activity found in most consumption data.
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Manipulating and Measuring Model Interpretability
TLDR
A sequence of pre-registered experiments showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box).
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Predicting consumer behavior with Web search
TLDR
This work uses search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes.
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Scalable Recommendation with Poisson Factorization
TLDR
A variational inference algorithm for approximate posterior inference that scales up to massive data sets and is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations.
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Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data.
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Nonmuscle myosin IIA-dependent force inhibits cell spreading and drives F-actin flow.
TLDR
The results suggest that NMM-IIA is involved in generating a coherent cytoplasmic contractile force from one side of the cell to the other through the cross-linking and the contraction of dorsal actin filaments.
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