Semantics-Space-Time Cube: A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales.

[1]  William Ribarsky,et al.  I‐SI: Scalable Architecture for Analyzing Latent Topical‐Level Information From Social Media Data , 2012, Comput. Graph. Forum.

[2]  David S. Ebert,et al.  Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[3]  Yale Song,et al.  #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media , 2014, IEEE Transactions on Visualization and Computer Graphics.

[4]  Gennady L. Andrienko,et al.  Exploratory analysis of spatial and temporal data - a systematic approach , 2005 .

[5]  Xiaohua Sun,et al.  Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time , 2012, IEEE Transactions on Visualization and Computer Graphics.

[6]  Jian Pei,et al.  Online Visual Analytics of Text Streams , 2015, IEEE Transactions on Visualization and Computer Graphics.

[7]  Carlos Eduardo Scheidegger,et al.  Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data , 2017, IEEE Transactions on Visualization and Computer Graphics.

[8]  William Wright,et al.  GeoTime Information Visualization , 2004, IEEE Symposium on Information Visualization.

[9]  Michael J. McGuffin,et al.  The Impact of Interactivity on Comprehending 2D and 3D Visualizations of Movement Data , 2015, IEEE Transactions on Visualization and Computer Graphics.

[10]  Jaegul Choo,et al.  UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization , 2013, IEEE Transactions on Visualization and Computer Graphics.

[11]  Thomas Ertl,et al.  Thematic Patterns in Georeferenced Tweets through Space-Time Visual Analytics , 2013, Computing in Science & Engineering.

[12]  M. Sheelagh T. Carpendale,et al.  A Review of Temporal Data Visualizations Based on Space-Time Cube Operations , 2014, EuroVis.

[13]  Gennady L. Andrienko,et al.  Detection, tracking, and visualization of spatial event clusters for real time monitoring , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[14]  Fangzhao Wu,et al.  OpinionFlow: Visual Analysis of Opinion Diffusion on Social Media , 2014, IEEE Transactions on Visualization and Computer Graphics.

[15]  David S. Ebert,et al.  Public behavior response analysis in disaster events utilizing visual analytics of microblog data , 2014, Comput. Graph..

[16]  Yingcai Wu,et al.  EvoRiver: Visual Analysis of Topic Coopetition on Social Media , 2014, IEEE Transactions on Visualization and Computer Graphics.

[17]  Weiwei Cui,et al.  How Hierarchical Topics Evolve in Large Text Corpora , 2014, IEEE Transactions on Visualization and Computer Graphics.

[18]  William Ribarsky,et al.  CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data , 2017, 2017 IEEE Conference on Visual Analytics Science and Technology (VAST).

[19]  Sinno Jialin Pan,et al.  Short and Sparse Text Topic Modeling via Self-Aggregation , 2015, IJCAI.

[20]  Thomas Ertl,et al.  ScatterBlogs: Geo-spatial document analysis , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[21]  Baining Guo,et al.  How ideas flow across multiple social groups , 2016, 2016 IEEE Conference on Visual Analytics Science and Technology (VAST).

[22]  Shimei Pan,et al.  Interactive, topic-based visual text summarization and analysis , 2009, CIKM.

[23]  William Ribarsky,et al.  Less After-the-Fact: Investigative visual analysis of events from streaming twitter , 2013, 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV).

[24]  Claire Cardie,et al.  39. Opinion mining and sentiment analysis , 2014 .

[25]  Piotr Jankowski,et al.  Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces , 2016, Inf. Vis..

[26]  Shashi Shekhar,et al.  CubeView: a system for traffic data visualization , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[27]  Gennady L. Andrienko,et al.  Interactive analysis of event data using space-time cube , 2004, Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004..

[28]  Bo Zhao,et al.  Text Cube: Computing IR Measures for Multidimensional Text Database Analysis , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[29]  Yingcai Wu,et al.  Visual Analysis of Topic Competition on Social Media , 2013, IEEE Transactions on Visualization and Computer Graphics.

[30]  John T. Stasko,et al.  iVisClustering: An Interactive Visual Document Clustering via Topic Modeling , 2012, Comput. Graph. Forum.

[31]  Thomas Ertl,et al.  ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering , 2013, IEEE Transactions on Visualization and Computer Graphics.

[32]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[33]  Jignesh M. Patel,et al.  Efficient aggregation for graph summarization , 2008, SIGMOD Conference.

[34]  Zhe Wang,et al.  Gaussian Cubes: Real-Time Modeling for Visual Exploration of Large Multidimensional Datasets , 2017, IEEE Transactions on Visualization and Computer Graphics.

[35]  Cláudio T. Silva,et al.  Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series , 2018, Comput. Graph. Forum.

[36]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[37]  Jie Li,et al.  Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity , 2018, EuroVA@EuroVis.

[38]  Shaowen Wang,et al.  A scalable framework for spatiotemporal analysis of location-based social media data , 2014, Comput. Environ. Urban Syst..

[39]  Carlos Eduardo Scheidegger,et al.  Nanocubes for Real-Time Exploration of Spatiotemporal Datasets , 2013, IEEE Transactions on Visualization and Computer Graphics.

[40]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[41]  William Ribarsky,et al.  HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies , 2013, IEEE Transactions on Visualization and Computer Graphics.

[42]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[43]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[44]  Gennady L. Andrienko,et al.  Tracing the German centennial flood in the stream of tweets: first lessons learned , 2013, GEOCROWD '13.

[45]  Michelle X. Zhou,et al.  Event detection with social media data , 2012 .

[46]  William Ribarsky,et al.  LeadLine: Interactive visual analysis of text data through event identification and exploration , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[47]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[48]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[49]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[50]  Michael S. Bernstein,et al.  Twitinfo: aggregating and visualizing microblogs for event exploration , 2011, CHI.

[51]  David Whitney,et al.  How Capacity Limits of Attention Influence Information Visualization Effectiveness , 2012, IEEE Transactions on Visualization and Computer Graphics.

[52]  Brian D. Davison,et al.  Empirical study of topic modeling in Twitter , 2010, SOMA '10.

[53]  Chen Xu,et al.  Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data , 2017, ISPRS Int. J. Geo Inf..

[54]  Jiawei Han,et al.  Topic modeling for OLAP on multidimensional text databases: topic cube and its applications , 2009, Stat. Anal. Data Min..

[55]  Xiaohui Yan,et al.  A biterm topic model for short texts , 2013, WWW.

[56]  Gennady L. Andrienko,et al.  Visual analytics of movement: An overview of methods, tools and procedures , 2013, Inf. Vis..

[57]  Xiaoru Yuan,et al.  Social Media Visual Analytics , 2017, Comput. Graph. Forum.

[58]  Heidrun Schumann,et al.  2D and 3D presentation of spatial data: A systematic review , 2014, 2014 IEEE VIS International Workshop on 3DVis (3DVis).

[59]  Lucy T. Nowell,et al.  ThemeRiver: visualizing theme changes over time , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[60]  Gennady L. Andrienko,et al.  Visual analytics methods for categoric spatio-temporal data , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).