Visualization of social media flows with interactively identified key players

Yuan, Xiaoru; Wang, Zhenhuang; Liu, Zipeng; Guo, Cong; Ai, Hong-Wei; Ren, Donghao

doi:10.1109/vast.2014.7042535

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Analysis of such event sequence data has exhibited potential for understanding various types of user behavior on social media. Existing efforts have proposed a range of visual analytics techniques to help yield insights about collective behaviors [12], [57], [76], [101], [117], [123], [129] and ego-centric behaviors [4], [6], [11].…”

Section: Social Mediamentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

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Section: Social Mediamentioning

confidence: 99%

Visual Progression Analysis of Event Sequence Data

Guo

Jin

Gotz

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…The entities include brands, person names, products, etc. Yuan et al [YWL*14] propose a system to identify key players’ names and explore their roles in the reposting network. The system uses a multi‐faceted filter to enable exploring statistics of the additional entities, including location, time, followers, etc., derived from NLP techniques.…”

Section: Visualization Techniquesmentioning

confidence: 99%

Social Media Visual Analytics

Chen

Lin

Yuan

2017

Computer Graphics Forum

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With the development of social media (e.g. Twitter, Flickr, Foursquare, Sina Weibo, etc.), a large number of people are now using them and post microblogs, messages and multi‐media information. The everyday usage of social media results in big open social media data. The data offer fruitful information and reflect social behaviors of people. There is much visualization and visual analytics research on such data. We collect state‐of‐the‐art research and put it into three main categories: social network, spatial temporal information and text analysis. We further summarize the visual analytics pipeline for the social media, combining the above categories and supporting complex tasks. With these techniques, social media analytics can apply to multiple disciplines. We summarize the applications and public tools to further investigate the challenges and trends.

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Data Analysis in Social Network: A Case Study

Kundu

Sarkar

2021

Transactions on Large-Scale Data- And Knowledge-Centered Systems XLVII

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Visualization of social media flows with interactively identified key players

Cited by 7 publications

References 4 publications

Visual Progression Analysis of Event Sequence Data

Visual Progression Analysis of Event Sequence Data

Social Media Visual Analytics

Data Analysis in Social Network: A Case Study

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