Information manipulation is widespread in today’s media environment. Online networks have disrupted the gatekeeping role of traditional media by allowing various actors to influence the public agenda; they have also allowed automated accounts (or bots) to blend with human activity in the flow of information. Here, we assess the impact that bots had on the dissemination of content during two contentious political events that evolved in real-time on social media. We focus on events of heightened political tension because they are particularly susceptible to information campaigns designed to mislead or exacerbate conflict. We compare the visibility of bots with human accounts, verified accounts, and mainstream news outlets. Our analyses combine millions of posts from a popular microblogging platform with web-tracking data collected from two different countries and timeframes. We employ tools from network science, natural language processing, and machine learning to analyze the diffusion structure, the content of the messages diffused, and the actors behind those messages as the political events unfolded. We show that verified accounts are significantly more visible than unverified bots in the coverage of the events but also that bots attract more attention than human accounts. Our findings highlight that social media and the web are very different news ecosystems in terms of prevalent news sources and that both humans and bots contribute to generate discrepancy in news visibility with their activity.

Bots are Less Central than Verified Accounts during Contentious Political Events

cover point processes in the modern sense of random measures. This chapter also is more technical than the subsequent two on Markov processes. Chapter 4 (on discrete-time countable state Markov processes) is extremely short. At only 32 pages, it is shorter than all those covering a specific type of process, except for Chapter 6 (martingales). I wonder how much the typical student will benefit from the minimal, and delayed, presentation of this topic. In my experience, most students need this material early on, to fully appreciate just what stochastic processes are and what the relevant issues are, before moving on to the more technical processes. The fifth chapter (on continuous-time countable state Markov processes) is the best-developed chapter and contains many more examples than do the other chapters. Birth–death processes and queuing models get substantial coverage, and there is plenty of material here that an instructor may choose to incorporate into a course. I personally believe that an undergraduate text also should devote more space to discussion of covariance structure, especially in the context of Gaussian processes, to reinforce the notions of dependence. Examples with Kalman filters, time series, and control theory are certainly within the range of applications appropriate for a text such as this one. The opening chapter is quite long, covering a wide range of basic probability concepts, such as classification of distributions according to hazard rate behavior (e.g., IFR), partial ordering of distributions, Wald’s identity, general limit theorems, and even advanced inequalities, such as Hölder’s and Minkowski’s. The concise treatment makes these results easy to refer to, but again few if any examples are given for each topic and no context in which the student might appreciate their value is provided. On the other hand, these presentations can be skipped and then returned to later as needed. Although the title claims finance as an applied area, I could find only one financial application (options pricing in the chapter on Brownian motion), unless a few applications to actuarial science are included. The bottom line is that I would not use this book as a text for either an undergraduate or a graduate course. However, I will likely put it on the reference list for my course.

Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Julian J. Faraway

This paper studies the robustness of exemplar effects in word comprehension by means of four long-term priming experiments with lexical decision tasks in Dutch. A prime and target represented the same word type and were presented with the same or different degree of reduction. In Experiment 1, participants heard only a small number of trials, a large proportion of repeated words, and stimuli produced by only one speaker. They recognized targets more quickly if these represented the same degree of reduction as their primes, which forms additional evidence for the exemplar effects reported in the literature. Similar effects were found for two speakers who differ in their pronunciations. In Experiment 2, with a smaller proportion of repeated words and more trials between prime and target, participants recognized targets preceded by primes with the same or a different degree of reduction equally quickly. Also, in Experiments 3 and 4, in which listeners were not exposed to one but two types of pronunciation variation (reduction degree and speaker voice), no exemplar effects arose. We conclude that the role of exemplars in speech comprehension during natural conversations, which typically involve several speakers and few repeated content words, may be smaller than previously assumed.

How robust are exemplar effects in word comprehension

informe -- Universidad de Costa Rica. Centro de Investigaciones en Matematicas Puras y Aplicadas, 2014. San Jose, 25-28 Febrero

XIX Simposio Internacional de Métodos Matemáticos Aplicados a las Ciencias

/pdf/when-moderation-fosters-persuasion-the-persuasive-power-of-3efmk38ugt.pdf

When Moderation Fosters Persuasion: the Persuasive Power of Deviatory Reviews

INTRODUCTION BINOMIAL DATA Challenger Disaster Example Binomial Regression Model Inference Tolerance Distribution Interpreting Odds Prospective and Retrospective Sampling Choice of Link Function Estimation Problems Goodness of Fit Prediction and Effective Doses Overdispersion Matched Case-Control Studies COUNT REGRESSION Poisson Regression Rate Models Negative Binomial CONTINGENCY TABLES Two-by-Two Tables Larger Two-Way Tables Matched Pairs Three-Way Contingency Tables Ordinal Variables MULTINOMIAL DATA Multinomial Logit Model Hierarchical or Nested Responses Ordinal Multinomial Responses GENERALIZED LINEAR MODELS GLM Definition Fitting a GLM Hypothesis Tests GLM Diagnostics OTHER GLMS Gamma GLM Inverse Gaussian GLM Joint Modeling of the Mean and Dispersion Quasi-Likelihood RANDOM EFFECTS Estimation Inference Predicting Random Effects Blocks as Random Effects Split Plots Nested Effects Crossed Effects Multilevel Models REPEATED MEASURES AND LONGITUDINAL DATA Longitudinal Data Repeated Measures Multiple Response Multilevel Models MIXED EFFECT MODELS FOR NONNORMAL RESPONSES Generalized Linear Mixed Models Generalized Estimating Equations NONPARAMETRIC REGRESSION Kernel Estimators Splines Local Polynomials Wavelets Other Methods Comparison of Methods Multivariate Predictors ADDITIVE MODELS Additive Models Using the gam Package Additive Models Using mgcv Generalized Additive Models Alternating Conditional Expectations Additivity and Variance Stabilization Generalized Additive Mixed Models Multivariate Adaptive Regression Splines TREES Regression Trees Tree Pruning Classification Trees NEURAL NETWORKS Statistical Models as NNs Feed-Forward Neural Network with One Hidden Layer NN Application Conclusion APPENDICES Likelihood Theory R Information Bibliography Index

Julian James Faraway

Papers

Extending the linear model with R : generalized linear, mixedeffects and nonparametric regression models