Difference between revisions of "Twitris"
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=Introduction= | =Introduction= | ||
| − | Well over a billion people have become 'citizens' of an Internet- or Web-enabled social | + | Well over a billion people have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. With over 5 billion mobile connections, over a billion with data connections (smartphones) and with many more having ability to communicate using SMS, digital media can be shared with the rest of the humanity instantly. As a result, humanity is interconnected as never before. This interconnected network of people actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing (Sheth, 2009-a) (Sheth, 2009-b). This phenomenon is different from the traditional centralized information dissemination and consumption environments where citizens |
| − | community. Web 2.0 fostered the open environment and applications for tagging, blogging, | + | |
| − | wikis, and social networking sites that have made information consumption, production, and | + | |
| − | sharing so incredibly easy. With over 5 billion mobile connections, over a billion with data | + | |
| − | connections (smartphones) and with many more having ability to communicate using SMS, | + | |
| − | digital media can be shared with the rest of the humanity instantly. As a result, humanity is | + | |
| − | interconnected as never before. This interconnected network of people actively observe, | + | |
| − | report, collect, analyze, and disseminate information via text, audio, or video messages, | + | |
| − | increasingly through pervasively connected mobile devices, has led to what we term citizen | + | |
| − | sensing (Sheth, 2009-a) (Sheth, 2009-b). This phenomenon is different from the traditional | + | |
| − | centralized information dissemination and consumption environments where citizens | + | |
primarily act as consumers of reported information from several authoritative sources. <br/> | primarily act as consumers of reported information from several authoritative sources. <br/> | ||
[[File:Twitris_fig1.jpg|right]] | [[File:Twitris_fig1.jpg|right]] | ||
| − | This citizen sensing is complemented by the growing ability to access, integrate, dissect, and | + | This citizen sensing is complemented by the growing ability to access, integrate, dissect, and analyze individual and collective thinking of humanity, giving us a capability that is recognized as collective intelligence. Citizen sensing involves humans in the loop, and with it all the complexities associated with and intelligence captured in human communication. As citizen sensing has gained momentum, it’s generating millions of observations, creating significant information overload. In many cases it becomes nearly impossible to make sense of the information around a topic of interest. Given this data deluge, analyzing the numerous social signals can be extremely challenging. In response to this growing citizen sensing data deluge, Twitris has been developed with the vision of performing semantics-empowered analysis of a broad variety of social media exchanges. <br/> |
| − | analyze individual and collective thinking of humanity, giving us a capability that is | + | |
| − | recognized as collective intelligence. Citizen sensing involves humans in the loop, and with it | + | |
| − | all the complexities associated with and intelligence captured in human communication. As | + | |
| − | citizen sensing has gained momentum, it’s generating millions of observations, creating | + | |
| − | significant information overload. In many cases it becomes nearly impossible to make sense | + | |
| − | of the information around a topic of interest. Given this data deluge, analyzing the numerous | + | |
| − | social signals can be extremely challenging. In response to this growing citizen sensing data | + | |
| − | deluge, Twitris has been developed with the vision of performing semantics-empowered | + | |
| − | analysis of a broad variety of social media exchanges. <br/> | + | |
| − | Twitris, named by combining Twitter with Tetris, a tile-matching puzzle game, has | + | Twitris, named by combining Twitter with Tetris, a tile-matching puzzle game, has incorporated increasingly sophisticated analysis of social data and associated metadata, combining it with background knowledge, and more recently (albeit not discussed here) machine sensor or data captured from sensors and devices that make up Internet of Things(IoT). Twitris’ evolution can be characterized in three phases (and corresponding versions of the system). Figure 1 outlines the corresponding dimensions Twitris considers. <br/> |
| − | incorporated increasingly sophisticated analysis of social data and associated metadata, | + | |
| − | combining it with background knowledge, and more recently (albeit not discussed here) | + | |
| − | machine sensor or data captured from sensors and devices that make up Internet of Things | + | |
| − | (IoT). Twitris’ evolution can be characterized in three phases (and corresponding versions of | + | |
| − | the system). Figure 1 outlines the corresponding dimensions Twitris considers. <br/> | + | |
| − | Twitris is a comprehensive platform for analyzing social content along multiple dimensions | + | Twitris is a comprehensive platform for analyzing social content along multiple dimensions leading to in-depth insights into various aspects of an event or a situation. The central thesis behind this work is that citizen sensor observations are inherently multi-dimensional in nature and taking these dimensions into account while processing, aggregating, connecting and visualizing data will provide useful organization and consumption principles. Twitris evolved in three phases, characterized by the versions of the systems:<br/> |
| − | leading to in-depth insights into various aspects of an event or a situation. The central thesis | + | |
| − | behind this work is that citizen sensor observations are inherently multi-dimensional in nature | + | |
| − | and taking these dimensions into account while processing, aggregating, connecting and | + | |
| − | visualizing data will provide useful organization and consumption principles. Twitris evolved | + | |
| − | in three phases, characterized by the versions of the systems:<br/> | + | |
| − | *Twitris v1: Spatio-Temporal-Thematic (STT) processing of Twitter and associated news, | + | *Twitris v1: Spatio-Temporal-Thematic (STT) processing of Twitter and associated news, multimedia and Wikipedia content (Sheth, 2009-b), (Nagarajan, 2009-a) (Jadhav, 2010) |
| − | multimedia and Wikipedia content (Sheth, 2009-b), (Nagarajan, 2009-a) (Jadhav, 2010) | + | *Twitris v2: People-Content-Network Analysis (PCNA) (Purohit, 2011-a) with use of background knowledge and semantic metadata extraction and querying/exploration |
| − | *Twitris v2: People-Content-Network Analysis (PCNA) (Purohit, 2011-a) with use of | + | *Twitris v3: sentiment-emotion-intent (SEI) extraction (Chen, 2012), (Wang 2012), (Nagarajan, 2009-b) along with personalization (Kapanipathi, 2011-a) and emerging |
| − | background knowledge and semantic metadata extraction and querying/exploration | + | continuous semantics (Sheth, 2010) capability involving semantic streaming social stream (i.e., real-time) processing using dynamically generated and updated domain models for semantics and context <br/> |
| − | *Twitris v3: sentiment-emotion-intent (SEI) extraction (Chen, 2012), (Wang 2012), | + | The above versions, or phases, of Twitris development is not as granular as painted above, that is, the issues identified above are not explicitly segregated by the version of the Twitris which has been in continuous development with senior students graduating and new students picking up the work. Four talks including a tutorial cover many of the issues covered by Twitris (Sheth, 2009-a), (Nagarajan, 2010-a), (Nagarajan, 2011), (Sheth, 2011). |
| − | (Nagarajan, 2009-b) along with personalization (Kapanipathi, 2011-a) and emerging | + | |
| − | continuous semantics (Sheth, 2010) capability involving semantic streaming social stream | + | |
| − | (i.e., real-time) processing using dynamically generated and updated domain models for | + | |
| − | semantics and context <br/> | + | |
| − | The above versions, or phases, of | + | |
| − | that is, the issues identified above are not explicitly segregated by the version of the Twitris | + | |
| − | which has been in continuous development with senior students graduating and new students | + | |
| − | picking up the work. Four talks including a tutorial cover many of the issues covered by | + | |
| − | Twitris (Sheth, 2009-a), (Nagarajan, 2010-a), (Nagarajan, 2011), (Sheth, 2011). | + | |
=Key Points= | =Key Points= | ||
Revision as of 16:52, 15 April 2013
Twitris, a Semantic Web application that facilitates understanding of social perceptions by Semantics-based processing of massive amounts of event-centric data. Twitris 2.0 addresses challenges in large scale processing of social data, preserving spatio-temporal-thematic properties. Twitris 2.0 also covers context based semantic integration of multiple Web resources and expose semantically enriched social data to the public domain. Semantic Web technologies enable the system's integration and analysis abilities.
Introduction
Well over a billion people have become 'citizens' of an Internet- or Web-enabled social community. Web 2.0 fostered the open environment and applications for tagging, blogging, wikis, and social networking sites that have made information consumption, production, and sharing so incredibly easy. With over 5 billion mobile connections, over a billion with data connections (smartphones) and with many more having ability to communicate using SMS, digital media can be shared with the rest of the humanity instantly. As a result, humanity is interconnected as never before. This interconnected network of people actively observe, report, collect, analyze, and disseminate information via text, audio, or video messages, increasingly through pervasively connected mobile devices, has led to what we term citizen sensing (Sheth, 2009-a) (Sheth, 2009-b). This phenomenon is different from the traditional centralized information dissemination and consumption environments where citizens
primarily act as consumers of reported information from several authoritative sources.
This citizen sensing is complemented by the growing ability to access, integrate, dissect, and analyze individual and collective thinking of humanity, giving us a capability that is recognized as collective intelligence. Citizen sensing involves humans in the loop, and with it all the complexities associated with and intelligence captured in human communication. As citizen sensing has gained momentum, it’s generating millions of observations, creating significant information overload. In many cases it becomes nearly impossible to make sense of the information around a topic of interest. Given this data deluge, analyzing the numerous social signals can be extremely challenging. In response to this growing citizen sensing data deluge, Twitris has been developed with the vision of performing semantics-empowered analysis of a broad variety of social media exchanges.
Twitris, named by combining Twitter with Tetris, a tile-matching puzzle game, has incorporated increasingly sophisticated analysis of social data and associated metadata, combining it with background knowledge, and more recently (albeit not discussed here) machine sensor or data captured from sensors and devices that make up Internet of Things(IoT). Twitris’ evolution can be characterized in three phases (and corresponding versions of the system). Figure 1 outlines the corresponding dimensions Twitris considers.
Twitris is a comprehensive platform for analyzing social content along multiple dimensions leading to in-depth insights into various aspects of an event or a situation. The central thesis behind this work is that citizen sensor observations are inherently multi-dimensional in nature and taking these dimensions into account while processing, aggregating, connecting and visualizing data will provide useful organization and consumption principles. Twitris evolved in three phases, characterized by the versions of the systems:
- Twitris v1: Spatio-Temporal-Thematic (STT) processing of Twitter and associated news, multimedia and Wikipedia content (Sheth, 2009-b), (Nagarajan, 2009-a) (Jadhav, 2010)
- Twitris v2: People-Content-Network Analysis (PCNA) (Purohit, 2011-a) with use of background knowledge and semantic metadata extraction and querying/exploration
- Twitris v3: sentiment-emotion-intent (SEI) extraction (Chen, 2012), (Wang 2012), (Nagarajan, 2009-b) along with personalization (Kapanipathi, 2011-a) and emerging
continuous semantics (Sheth, 2010) capability involving semantic streaming social stream (i.e., real-time) processing using dynamically generated and updated domain models for semantics and context
The above versions, or phases, of Twitris development is not as granular as painted above, that is, the issues identified above are not explicitly segregated by the version of the Twitris which has been in continuous development with senior students graduating and new students picking up the work. Four talks including a tutorial cover many of the issues covered by Twitris (Sheth, 2009-a), (Nagarajan, 2010-a), (Nagarajan, 2011), (Sheth, 2011).
Key Points
We investigate the role and benefits of using semantic approach, especially by metadata extraction and enrichments and contextually applying relevant background knowledge, along with demonstrating examples on real-world data using system (Twitris) developed at Kno.e.sis.
- Event-specific analysis of citizen sensing and discuss opportunities and challenges in
understanding temporal, spatial and thematic cues
- Facets of people-content-network analysis with focus on user-community engagement
analysis
- Real time social media data analysis, and the concept of continuous semantics
supported by dynamic model creation
- Sentiment and emotion identification from citizen sensing data
- Recent advance in developing semantic abstracts or semantic perception to convert
massive amounts of raw observational data into nuggets of information and insights that can aid in human decision making
Publications
- A. Jadhav et al., Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data, ISWC Semantic Web Challenge 2010.
- A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, February 17, 2009.
- A. Sheth, Citizen Sensing, Social Signals, and Enriching Human Experience IEEE Internet Computing, July/August 2009.
- M. Nagarajan et al., Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences, Tenth International Conference on Web Information Systems Engineering, Oct 5-7, 2009, Poland.
- What are people talking about, Why people write, How people write: Meena Nagarajan's research
- Real Time Web - A primer Part I and Part II, August 29, 2009
Internal
For project members only: Twitris Internal Page
