CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization

Chung, Haeyong; Dasari, Sai Prashanth; Nandhakumar, Santhosh; Andrews, Christopher

doi:10.1109/vast.2017.8585484

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Willett et al [14] proposed a crowd-assisted clustering method to detect and remove redundant explanations provided by crowd workers. CRICTO [15] was developed for the sensemaking of text data by constructing a graph that represents entity connections annotated by workers. Analysts can generate cohesive hypotheses by interactively exploring the graph.…”

Section: Related Workmentioning

confidence: 99%

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Chen

Wang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Section: Related Workmentioning

confidence: 99%

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Chen

Wang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…Smaller categories were folded into the "Other" class in Fig. 1: two papers using demographic data [25] [38] and one each of the Planetary Science [6], Geoscience [17], Economics [4], Education [16], Text [7], and Art [8] domains.…”

Section: Resultsmentioning

confidence: 99%

A Review of Geospatial Content in IEEE Visualization Publications

Yoshizumi

Coffer

Collins

et al. 2020

Preprint

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geospatial nature of the paper (GEO), and VIS Conference paper type and track (TRK) are shown for each paper. Percentages on the top band (lightest gray bars) correspond to data domain types. The GEO band marks papers as either containing both geospatial data and a geospatial analysis (dark gray) or geospatial data only (light gray). The TRK band is colored by the VIS Conference paper types and tracks listed on Open Access VIS [15].

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“…For example, systems such as ManyEyes [72] and Sense.us [39] have used novice crowds to create, collaborate on, and analyze data visualizations. Connect the Dots [54] and CRICTO [12] ask novice crowds to build social networks for intelligence analysis, but not to create layouts for them. Inspired by these projects, we explore the use of non-expert workers on Amazon Mechanical Turk to create biological network layouts.…”

Section: Leveraging Human Abilities Through Crowdsourcing To Lay Out ...mentioning

confidence: 99%

Flud: a hybrid crowd-algorithm approach for visualizing biological networks

Bharadwaj¹,

Gwizdala²,

Kim³

et al. 2019

Preprint

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Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this paper, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, an online game with a purpose (GWAP) that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Further, we propose a novel hybrid approach for graph layout wherein crowdworkers and a simulated annealing algorithm build on each other's progress. To showcase the effectiveness of Flud, we recruited crowd workers on Amazon Mechanical Turk to lay out complex networks that represent signaling pathways. Our results show that the proposed hybrid approach outperforms state-of-the-art techniques for graphs with a large number of feedback loops. We also found that the algorithmically generated suggestions guided the players when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in human computation games.CCS Concepts: • Human-centered computing → Collaborative and social computing systems and tools; Empirical studies in collaborative and social computing; Information visualization; Human computer interaction (HCI).

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CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization

Cited by 8 publications

References 28 publications

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

Interactive Graph Construction for Graph-Based Semi-Supervised Learning

A Review of Geospatial Content in IEEE Visualization Publications

Flud: a hybrid crowd-algorithm approach for visualizing biological networks

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