Visualizing graph differences from social media streams

Minjeong Shin, Dongwoo Kim, Jae Hee Lee, Umanga Bista, Lexing Xie

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    We propose KGdiff, a new interactive visualization tool for social media content focusing on entities and relationships. The core component is a layout algorithm that highlights the differences between two graphs. We apply this algorithm on knowledge graphs consisting of named entities and their relations extracted from text streams over different time periods. The visualization system provides additional information such as the volume and frequency ranking of entities and allows users to select which parts of the graph to visualize interactively. On Twitter and news article collections, KGdiff allows users to compare different data subsets. Results of such comparisons often reveal topical or geographical changes in a discussion. More broadly, graph differences are useful for a wide range of relational data comparison tasks, such as comparing social interaction graphs, identifying changes in user behavior, or discovering differences in graphs from distinct sources, geography, or political stance.

    Original languageEnglish
    Title of host publicationWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
    PublisherAssociation for Computing Machinery, Inc
    Pages806-809
    Number of pages4
    ISBN (Electronic)9781450359405
    DOIs
    Publication statusPublished - 30 Jan 2019
    Event12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia
    Duration: 11 Feb 201915 Feb 2019

    Publication series

    NameWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

    Conference

    Conference12th ACM International Conference on Web Search and Data Mining, WSDM 2019
    Country/TerritoryAustralia
    CityMelbourne
    Period11/02/1915/02/19

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