WeiboEvents: A Crowd Sourcing Weibo Visual Analytic System

Ren, Donghao; Zhang, Xin; Wang, Zhenhuang; Li, Jing; Yuan, Xiaoru

doi:10.1109/pacificvis.2014.38

“…Text marking/highlighting cite2vec [71], TopicLens [72], SurVis [73], Poemage [74], Overview [75] Tags or word cloud SentenTree [76], InfoVis [77], VisOHC [78], IncreSTS [79], Word storms [80] Bar charts TextTile [81], SentiCompass [82], NewsViews [83], WeiboEvents [84], CatStream [85] Scatterplot PhenoLines [86], SocialBrands [87], TopicPanorama [88], #FluxFlow [89], PEARL [90] Line chart Vispubdata.org [91], GameFlow [92], MultiConVis [93], Contextifier [94], Google+Ripples [95] Node-link NEREx [96], iForum [97], NameClarifier [98], DIA2 [99],…”

Section: Visualization Style Tool [Reference]mentioning

confidence: 99%

Artificial Intelligence-Based Medical Data Mining

Zia¹,

Aziz

²

,

Popa³

et al. 2022

JPM

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Understanding published unstructured textual data using traditional text mining approaches and tools is becoming a challenging issue due to the rapid increase in electronic open-source publications. The application of data mining techniques in the medical sciences is an emerging trend; however, traditional text-mining approaches are insufficient to cope with the current upsurge in the volume of published data. Therefore, artificial intelligence-based text mining tools are being developed and used to process large volumes of data and to explore the hidden features and correlations in the data. This review provides a clear-cut and insightful understanding of how artificial intelligence-based data-mining technology is being used to analyze medical data. We also describe a standard process of data mining based on CRISP-DM (Cross-Industry Standard Process for Data Mining) and the most common tools/libraries available for each step of medical data mining.

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“…There is no public editorial repost tree dataset. Therefore, we manually selected 10 hot events taking place during January 2nd -July 28th 2014, and then used the PKUVIS toolkit (Ren et al, 2014) to crawl the complete repost trees for all the events given the corresponding original posts. Table 3 shows the details about the repost tree corpus 4 .…”

Section: Data Collection and Evaluation Metricsmentioning

confidence: 99%

Using Content-Level Structures for Summarizing Microblog Repost Trees

Li

¹

,

Gao

²

,

Wei

³

et al. 2017

Social Media Content Analysis

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A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the result of leader detection. To reduce the error propagation cascaded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the reposting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.

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“…We first crawled 1,300 different repost trees using the public PKUVIS toolkit (Ren et al, 2014). Given an original microblog post, the toolkit can automatically crawl its complete repost tree.…”

Section: Data Collection and Setupmentioning

confidence: 99%

“…• We propose a novel microblog context summarization framework, in which given reposting messages organized as a repost tree (obtaining repost tree is trivial using public microblogging toolkit (Ren et al, 2014)), we summarize the repost trees based on content information and reposting relations of messages.…”

Section: Introductionmentioning

confidence: 99%

Using Content-level Structures for Summarizing Microblog Repost Trees

Li

¹

,

Gao²,

Wei³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the result of leader detection. To reduce the error propagation cascaded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the reposting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.

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WeiboEvents: A Crowd Sourcing Weibo Visual Analytic System

Cited by 18 publications

References 17 publications

Artificial Intelligence-Based Medical Data Mining

Artificial Intelligence-Based Medical Data Mining

Using Content-Level Structures for Summarizing Microblog Repost Trees

Using Content-level Structures for Summarizing Microblog Repost Trees

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