Predictive Visual Analytics using Topic Composition

Yeon, Hanbyul; Jang, Yun

doi:10.1145/2801040.2801054

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…For example, M ühlbacher et al provided a survey of frequently used algorithms and discussed possibilities to enable user involvement in ongoing computations [38]. Yeon et al combined the contextual similar cases in the past to predict and visualize event evolution patterns [39]. Their approach combines with a prediction model and visualization.…”

Section: Predictive Visual Analytics On Social Mediamentioning

confidence: 99%

WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Social media, such as Facebook and WeChat, empowers millions of users to create, consume, and disseminate online information on an unprecedented scale. The abundant information intensifies the competition of WeChat Articles (i.e., posts) for gaining user attention due to the zero-sum nature of attention. Therefore, only a small portion of information tends to become popular while the rest remains unnoticed or quickly disappears. Recent years have witnessed a growing interest in predicting the future trend in the popularity of posts and understanding the factors that influence the popularity of the posts. Nevertheless, existing predictive models either rely on cumbersome feature engineering or sophisticated parameter tuning, which are difficult to understand and improve. In this paper, we enhance a point process-based model by incorporating visual reasoning to support communication between the users and the predictive model for a better prediction result. The proposed system supports users to uncover the working mechanism behind the model and improve the prediction accuracy accordingly based on the insights gained. We use realistic WeChat articles to demonstrate the effectiveness of the system and verify the improved model on a large scale of WeChat articles. We also elicit and summarize the feedback from WeChat domain experts.

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Section: Predictive Visual Analytics On Social Mediamentioning

confidence: 99%

WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…(d) Anomaly detection from geo‐tagged social media trajectories [CCJ*15]. (e) Predictive analysis for future event patterns through topic composition [YJ15]. (f) Visual analytics for situation awareness integrating spatial, temporal and textual information [BTH*13].…”

Section: Visual Analytics Techniquesmentioning

confidence: 99%

“…Furthermore, users, especially analysts, care about the trends and patterns in the future, which requires predictive analysis. Research in this category usually integrates the classification model with trained data to predict and to verify the prediction [LWM14, LKT*14, YJ15]. In the visual analytics pipeline of Yeon et al's work [YJ15], we can see the topic extraction process with general patterns and abnormal event detection is the basis for predictive analysis (Figure e).…”

Section: Visual Analytics Techniquesmentioning

confidence: 99%

“…Research in this category usually integrates the classification model with trained data to predict and to verify the prediction [LWM14, LKT*14, YJ15]. In the visual analytics pipeline of Yeon et al's work [YJ15], we can see the topic extraction process with general patterns and abnormal event detection is the basis for predictive analysis (Figure e). Then they predict and visualize event evolution patterns by combing the contextual similar cases occurring in the past.…”

Section: Visual Analytics Techniquesmentioning

confidence: 99%

“…In general, there are few works addressing predictive analysis in visual analytics. Most of them address temporal evolution patterns [YJ15, LKT*14]. In geographic information, Wu et al use geo‐tagged data to predict movements and human mobility [WZSL14].…”

Section: Visual Analytics Techniquesmentioning

confidence: 99%

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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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Predictive Visual Analytics using Topic Composition

Cited by 7 publications

References 25 publications

WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles

Social Media Visual Analytics

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