Digital Propaganda: The Tyranny of Ignorance

© The Author(s) 2018. The existence of propaganda is inexorably bound to the nature of communication and communications technology. Mass communication by citizens in the digital age has been heralded as a means to counter elite propaganda; however, it also provides a forum for misinformation, aggression and hostility. The extremist group Britain First has used Facebook as a way to propagate hostility towards Muslims, immigrants and social security claimants in the form of memes, leading to a backlash from sites antithetical to their message. This article provides a memetic analysis, which addresses persuasion, organisation, political echo chambers and self-correcting online narratives; arguing that propaganda can be best understood as an evolving set of techniques and mechanisms which facilitate the propagation of ideas and actions. This allows the concept to be adapted to fit a changing political and technological landscape and to encompass both propaganda and counter-propaganda in the context of horizontal communications networks

Kernel archetypal analysis for clustering web search frequency time series

We analyze time series which indicate how collective attention to social media services or Web-based businesses evolves over time. Data was gathered from Goolge Trends and consists of discrete time series of varying duration. Following the related literature, we fit Weibull distributions to the data. Given the two parameters of its fitted model, we embed each time series in a low-dimensional space and apply kernel archetypal analysis based on the Kullback-Leibler divergence for clustering. Our results reveal strong regularities in the dynamics of collective attention to social media and thus illustrate the potential of advanced pattern recognition techniques in the emerging area of Web science

Collective attention on the web

Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age. Tackling this challenge, this monograph explores the dynamics of collective attention related to Internet phenomena such as Internet memes, viral videos, or social media platforms and Web-based businesses. To this end, we analyze time series data that directly or indirectly represent how the interest of large populations of Web users in content or services develops over time. Regardless of regional or cultural contexts, we generally observe strong regularities in time series that reflect attention dynamics and we discuss mathematical models that provide plausible explanations as to what drives the apparently dominant dynamics of rapid initial growth and prolonged decline

Separable Linear Discriminant Classification

Linear discriminant analysis is a popular technique in computer vision, machine learning and data mining. It has been successfully applied to various problems, and there are numerous variations of the original approach. This paper introduces the idea of separable LDA. Towards the problem of binary classification for visual object recognition, we derive an algorithm for training separable discriminant classifiers. Our approach provides rapid training and runtime behavior and also tackles the small sample size problem. Experimental results show that the method performs robust and allows for online learning

Bounding box splitting for robust shape classification

This paper presents a fast method to compute homeomorphisms between 2D lattices and shapes found in binary images. Unlike many other methods, this mapping is not restricted to simply connected shapes but applies to arbitrary topologies. Moreover, it provides an avenue to the embedding of shapes in vectorspaces over R and C and thus enables robust shape recognition

Separable Linear Classifiers for Online Learning in Appearance Based Object Detection

Abstract. Online learning for object detection is an important requirement for many computer vision applications. In this paper, we present an iterative optimization algorithm that learns separable linear classifiers from a sample of positive and negative example images. We demonstrate that separability not only leads to rapid runtime behavior but enables very fast training. Experimental results underline that the approach even allows for real time online learning for tracking of articulated objects in real world environments. Motivation and Scientific Context A general trend in present day computer vision research appears to be the integration of machine learning techniques into visual processing. Especially in the case of object detection in real world environments, the entanglement of vision and learning has led to stunning results. Cascaded weak classifiers rapidly detect objects of constraint shape and texture Robust as they are, the above techniques all require extensive training times. This hampers their use in scenarios where online learning is mandatory, as in the case of vision systems that assist their users in real world tasks. Among the few current proposals for such a scenario is a system that applies the Winnow algorithm for learning linear classifiers to motion data In this paper, we present a simple approach to very fast object learning which, nevertheless, provides rapid runtime behavior and reliable detection. Based on positive and negative example images, we propose an iterative least mean squares technique of learning separable linear classifiers. The method accomplishes input processing as rapidly as the popular cascaded weak classifiers. Moreover, it copes with objects of considerably varying shape and texture and is characterized by very short training times. Our classifiers therefore enable real time online learning in object recognition. The next section first discusses the benefits of linear classifiers for visual object detection and then introduces our algorithm for learning separable classifiers. Section