3,383 research outputs found
A meta-analysis of state-of-the-art electoral prediction from Twitter data
Electoral prediction from Twitter data is an appealing research topic. It
seems relatively straightforward and the prevailing view is overly optimistic.
This is problematic because while simple approaches are assumed to be good
enough, core problems are not addressed. Thus, this paper aims to (1) provide a
balanced and critical review of the state of the art; (2) cast light on the
presume predictive power of Twitter data; and (3) depict a roadmap to push
forward the field. Hence, a scheme to characterize Twitter prediction methods
is proposed. It covers every aspect from data collection to performance
evaluation, through data processing and vote inference. Using that scheme,
prior research is analyzed and organized to explain the main approaches taken
up to date but also their weaknesses. This is the first meta-analysis of the
whole body of research regarding electoral prediction from Twitter data. It
reveals that its presumed predictive power regarding electoral prediction has
been rather exaggerated: although social media may provide a glimpse on
electoral outcomes current research does not provide strong evidence to support
it can replace traditional polls. Finally, future lines of research along with
a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table
Diverse randomized agents vote to win
We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse
Truth Discovery in Crowdsourced Detection of Spatial Events
ACKNOWLEDGMENTS This research is based upon work supported in part by the US ARL and UK Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the US ARL, the US Government, the UK Ministry of Defense or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.Peer reviewedPostprin
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Learning with Aggregate Data
Various real-world applications involve directly dealing with aggregate data. In this work, we study Learning with Aggregate Data from several perspectives and try to address their combinatorial challenges.
At first, we study the problem of learning in Collective Graphical Models (CGMs), where only noisy aggregate observations are available. Inference in CGMs is NP- hard and we proposed an approximate inference algorithm. By solving the inference problems, we are empowered to build large-scale bird migration models, and models for human mobility under the differential privacy setting.
Secondly, we consider problems given bags of instances and bag-level aggregate supervisions. Specifically, we study the US presidential election and try to build a model to understand the voting preferences of either individuals or demographic groups. The data consists of characteristic individuals from the US Census as well as
voting tallies for each voting precinct. We proposed a fully probabilistic Learning with Label Proportions (LLPs) model with exact inference to build an instance-level model.
Thirdly, we study distribution regression. It has similar problem setting to LLPs but builds bag-level models. We experimentally evaluated different algorithms on three tasks, and identified key factors in problem settings that impact the choice of algorithm
Philosophy and the practice of Bayesian statistics
A substantial school in the philosophy of science identifies Bayesian
inference with inductive inference and even rationality as such, and seems to
be strengthened by the rise and practical success of Bayesian statistics. We
argue that the most successful forms of Bayesian statistics do not actually
support that particular philosophy but rather accord much better with
sophisticated forms of hypothetico-deductivism. We examine the actual role
played by prior distributions in Bayesian models, and the crucial aspects of
model checking and model revision, which fall outside the scope of Bayesian
confirmation theory. We draw on the literature on the consistency of Bayesian
updating and also on our experience of applied work in social science.
Clarity about these matters should benefit not just philosophy of science,
but also statistical practice. At best, the inductivist view has encouraged
researchers to fit and compare models without checking them; at worst,
theorists have actively discouraged practitioners from performing model
checking because it does not fit into their framework.Comment: 36 pages, 5 figures. v2: Fixed typo in caption of figure 1. v3:
Further typo fixes. v4: Revised in response to referee
Theo-Political Conspiracy Discourse in \u3cem\u3eThe Wanderer\u3c/em\u3e
This study undertakes an intensive analysis of The Wanderer, an ultra·conservative Catholic weekly newspaper. It is argued that con· spiracy discourse in The Wanderer provides a continuous series of god and devil terms that playoff one another as generic warrants authorizing a domino effect that solidifies an over·arching rhetorical vision, which ultimately affects the interpretation of U.S. Roman Catholic Church doctrine and its application to a number of contem· porary socio·political issues. Discowse emanating from this particular publication is representative of a paranoid style and provides a case study for tracing operant terms in an ongoing backlash movement
Community Detection in Weighted Multilayer Networks with Ambient Noise
We introduce a novel class of stochastic blockmodel for multilayer weighted
networks that accounts for the presence of a global ambient noise that governs
between-block interactions. We induce a hierarchy of classifications in
weighted multilayer networks by assuming that all but one cluster (block) are
governed by unique local signals, while a single block is classified as ambient
noise, which behaves identically as interactions across differing blocks.
Hierarchical variational inference is employed to jointly detect and typologize
block-structures as local signals or global noise. These principles are
incorporated into novel community detection algorithm called Stochastic Block
(with) Ambient Noise Model (SBANM) for multilayer weighted networks. We apply
this method to several different domains. We focus on the Philadelphia
Neurodevelopmental Cohort to discover communities of subjects that form
diagnostic categories relating psychopathological symptoms to psychosis.Comment: 27 page
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