Cultural Analytics in Large-Scale Visualization Environments

Yamaoka, So; Манович, Лев; Douglass, J. Matthew; Kuester, Falko

doi:10.1109/mc.2011.386

Cited by 6 publications

(5 citation statements)

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“…Otherwise, alternative visualization strategies that could have been considered span set diagrams (ordered categories of content/artifact), image collages (combination of images), and scatterplots (for comparing two variables). [56][57][58] Nevertheless, these techniques deal with one or few variables and mainly for merely descriptive goals or following a driving thesis (e.g. a prescriptive hierarchy).…”

Section: Visualization Strategymentioning

confidence: 99%

Enjoying death among gamers, viewers, and users: A network visualization of Dark Souls 3’s trends on Twitch.tv and Steam platforms

Gandolfi

2017

Information Visualization

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In current Game Research, gaming service platforms such as PlayStation Network, Steam, and Twitch.tv represent a still poorly investigated topic. Despite the millions of monthly viewers and members, little efforts have been done to shed light on their dynamics and trends. This article aims to address such a lack by presenting the findings of an empirical inquiry guided by the key concepts of “platform” and “actor–network theory” with the support of a novel network visualization technique. Specifically, the role-playing game Dark Souls 3–related activity on Steam and Twitch.tv was collected for the first 20 days from the release (12 April–1 May 2016). Targeted data concerned several variables among which: most viewed streamers, streaming types, debating topics and reviews’ highlights on Steam (etc.) through screenshots, user-generated content, and text gathering. Data were processed and then visualized with the network-oriented software Gephi for uncovering associations and patterns in the targeted online environments. The action game The Division worked as an exploratory case study and counter-example for stressing the proposal. Although with some limitations, the visualization strategy adopted (four networks for each platform) proved to be effective in framing and communicating the results in a straightforward way. Finally, findings enlightened a phenomenon (i.e. gaming service platforms), that is, getting increasingly central in digital entertainment, and might inform further investigations with alternative designs and focuses.

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Section: Visualization Strategymentioning

confidence: 99%

Enjoying death among gamers, viewers, and users: A network visualization of Dark Souls 3’s trends on Twitch.tv and Steam platforms

Gandolfi

2017

Information Visualization

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“…Beyond the important application domain of forensics, image provenance analysis can form a powerful framework for academic research in other fields. Cultural analytics has emerged as a distinct sub-discipline within the digital humanities [10], [11] that is concerned with combining quantitative methods from social science and computer science to answer humanistic questions about cultural trends. An example of this (which we have already touched upon in Fig.…”

Section: Introductionmentioning

confidence: 99%

Image Provenance Analysis at Scale

Moreira¹,

Bharati²,

Brogan³

et al. 2018

Preprint

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Prior art has shown it is possible to estimate, through image processing and computer vision techniques, the types and parameters of transformations that have been applied to the content of individual images to obtain new images. Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images. This is a problem that recently has received the name of image provenance analysis. In these times of public media manipulation (e.g., fake news and meme sharing), obtaining the history of image transformations is relevant for fact checking and authorship verification, among many other applications. This article presents an end-to-end processing pipeline for image provenance analysis, which works at real-world scale. It employs a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected. A comprehensive set of experiments for each stage of the pipeline is provided, comparing the proposed solution with state-of-the-art results, employing previously published datasets. In addition, this work introduces a new dataset of real-world provenance cases from the social media site Reddit, along with baseline results.

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“…Cultural Analytics is a research program which develops toolkits for visual analytics of cultural patterns in large image sets created within media theorist Lev Manovich's Software Studies Initiative (Yamaoka, Manovich, Douglass & Kuester 2011). Suggesting a middle way between scientist and hermeneutic approaches to visual analytics, it departs from the question, 'What will happen when humanists start using interactive visualizations as a standard tool in their work, the way many scientists already do?'…”

Section: Cultural Analytics and Action -Gesturing Towards Humanistic ...mentioning

confidence: 99%

The Datafied Society

2017

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A few years back we embarked on an expedition into the rapidly transforming landscape of data research, the narratives of big data and the practices emerging with novel data resources, tools and new directions of social and cultural inquiry. This book represents our own experiences and impressions of this journey. We were out there on unfamiliar and at times even uncharted territory. Often we depended on the help of more learned colleagues who generously shared their knowledge, gave us directions or helped with problems. Many of them became authors for this book and we would like to thank them for trusting us with collecting their contributions and presenting them in a joint volume.The editors wish to thank the Utrecht Data School staff Thomas Boeschoten, Irene Westra, Iris Muis, Daniela van Geenen and Gerwin van Schie for their input and for providing an intellectually stimulating work environment. With the Utrecht Data School we have created a place where ambitious and enthusiastic students can meet to join us in this exploration. We are grateful for having the rare opportunity of conducting research together with students from whom we can learn so much and whose insatiable curiosity is an inspiration as well as a constant reminder of why we became teachers in the first place. Our gratitude extends also to the Institute of Cultural Inquiry and the open access fund at Utrecht University for enabling us to make this book open access.A special thanks to William Uricchio, Fernando van der Vlist and Eef Masson for their helpful comments and advice at various stages of the editing process. Finally, we are particularly grateful to Nicolás López Coombs for helping us with the editing and the formatting of the book, but most importantly for keeping an eye on our timeline.

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Cultural Analytics in Large-Scale Visualization Environments

Cited by 6 publications

References 0 publications

Enjoying death among gamers, viewers, and users: A network visualization of Dark Souls 3’s trends on Twitch.tv and Steam platforms

Enjoying death among gamers, viewers, and users: A network visualization of Dark Souls 3’s trends on Twitch.tv and Steam platforms

Image Provenance Analysis at Scale

The Datafied Society

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