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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Senaratne, Hansi Lehle, Dominic Schreck, Tobias Earth Observation Center German Aerospace Center Oberpfaffenhofen Germany Advanced Research & Technologies Avira Tettnang Germany Inst. of Computer Graphics & Knowledge Visualization Graz University of Technology Graz Austria

ISBN: (纸本)9781665456616

A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial-and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter. © 2022 IEEE.

关键词： Trajectories

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Characteristics Analysis of Moving Conversations to Detect Events on Twitter

Characteristics Analysis of Moving Conversations to Detect E...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Hansi Senaratne Dominic Lehle Tobias Schreck Earth Observation Center German Aerospace Center Oberpfaffenhofen Germany Advanced Research & Technologies Avira Tettnang Germany Inst. of Computer Graphics & Knowledge Visualization Graz University of Technology Graz Austria

A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial- and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter.

关键词： knowledge engineering visualization Text analysis Social networking (online) Blogs Linearity Oral communication

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Industrial applications of VEs

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IEEE computer graphics AND APPLICATIONS 1996年第1期16卷 10-13页

作者： Gobel, M Fraunhofer Inst. for Comp. Graphics Darmstadt Germany Visualization and Media Syst. Design Institut for Media Communication Natl. Res. Ctr. for Computer Science D-53754 St. Augustin Germany

Besides entertainment, three main areas of application attract industrial interest in virtual environment (VE): marketing, prototyping, and training. Advanced VEs derived from available CAD data or simulation results still require efficient tools for data preparation, as well as high-end workstations and peripherals. The high costs for these advanced VEs prevent small and medium companies from taking the first steps in this direction.

关键词： Virtual reality

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