VINCENT: A visual analytics system for investigating the online vaccine debate

Ninkov, Anton; Sedig, Kamran

doi:10.5210/ojphi.v11i2.10114

Cited by 10 publications

(6 citation statements)

References 40 publications

(56 reference statements)

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“…By combining visualizations, interaction mechanisms, ML techniques, and analytical models, VASes are capable of providing both computational and cognitive possibilities [6,48,49]. Not only through these possibilities is the analyst equipped with more robust analytical tools, but also a cognitive coupling of the system and the human analyst is created [28][29][30]42,45].…”

Section: Visual Analyticsmentioning

confidence: 99%

“…In recent years, proliferation of smartphones has further pushed people to use mobile applications and, hence, social media platforms. However, it seems that due to manifold types of platform hosts, overlaps in the discussed topics, numerous varieties of rules and regulations, and multiplicity in forms and data structures of the content, in order to analyze, understand, gain insight into, and make sense of the online discussions in forums, one has to traverse through heterogeneous webs of platforms and online discussions [6]. Meanwhile, comments may quickly become outdated or unrelated as well as suffer from source credibility.…”

Section: Introductionmentioning

confidence: 99%

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VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Haghighati

Sedig

2020

Data

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Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

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Section: Visual Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Haghighati

Sedig

2020

Data

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“…VA can address this issue through human-in-the-loop strategies that enable analysts to work iteratively with computational methods that extract knowledge from messy data, cope with uncertainties in computational results, and improve those results over time (Endert et al, 2014;Robinson, 2017). VA is especially suitable for big, diverse, messy data that can be interpreted differently (Tapia-McClung and Silván-Cárdenas, 2021;Angelini et al, 2018;Ninkov and Sedig, 2019;Snyder et al, 2020;MacEachren et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

Pezanowski

Mitra

MacEachren

2022

KN J. Cartogr. Geogr. Inf.

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Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable. Supplementary Information The online version contains supplementary material available at 10.1007/s42489-022-00098-3.

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“…Visual analytics tools (VATs) help users form valuable connections with their information and be more active participants in the analysis process [4,5]. They can be used to support a wide variety of domain tasks, such as making sense of misinformation, searching large document sets, and making decisions regarding health data, to name a few [6][7][8][9]. More than ever, researchers are investigating strategies to combat the rising computational needs of analytic tasks [10].…”

Section: Introductionmentioning

confidence: 99%

Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach

Demelo

Sedig

2021

Information

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We investigate the design of ontology-supported, progressively disclosed visual analytics interfaces for searching and triaging large document sets. The goal is to distill a set of criteria that can help guide the design of such systems. We begin with a background of information search, triage, machine learning, and ontologies. We review research on the multi-stage information-seeking process to distill the criteria. To demonstrate their utility, we apply the criteria to the design of a prototype visual analytics interface: VisualQUEST (Visual interface for QUEry, Search, and Triage). VisualQUEST allows users to plug-and-play document sets and expert-defined ontology files within a domain-independent environment for multi-stage information search and triage tasks. We describe VisualQUEST through a functional workflow and culminate with a discussion of ongoing formative evaluations, limitations, future work, and summary.

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VINCENT: A visual analytics system for investigating the online vaccine debate

Cited by 10 publications

References 40 publications

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Erforschung von Bewegungsbeschreibungen durch geovisuelle Analytik

Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach

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