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    Go To Twitris What is Twitris

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            Twitris 2.0, 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.

    Why Twitris?

            Emergence of microblogging platforms such as Twitter, friendfeed etc. have revolutionized how unfiltered, real-time information is disseminated and consumed by citizens. Twitter, has therefore emerged as the preeminent medium for sharing citizen-sensor observations, as was demonstrated in a variety of situations ranging from Mumbai terrorist attack to Iran elections.

            While the decentralized information diffusion model offered by twitter has gained momentum and has created avenues for experiential data sharing, millions of observations, shared through tweets, create significant information overload. In many cases it becomes nearly impossible to make sense of the information around a topic of interest. This problem is further compounded by the fact that tweets increasingly integrate other social networking sites (flickr, twitpics) and general Web content(news, Wikipedia, blogs) through embedded links and metadata. Given this data deluge, analyzing the numerous social signals carried by tweets and associated content to find out what is being said about an event (theme), where (spatial), when (temporal), how are key concerns (topics of discussion) changing over a period of time and whether there are regional differences in the opinions on a given topic, can be extremely challenging.

    What is Twitris?

            In response to this growing data deluge, we have developed Twitris (currently Twitris 2.0) with the vision of performing semantics-empowered analysis of a broad variety of social media content. Specifically, Twitris aims to capture semantics (i.e., meaning and understanding) with spatial, temporal, thematic dimensions, user intentions and sentiments, networking behavior (user interactions patterns and features such as information diffusion and centrality) and other information present in social media. Semantic Web technologies enable its core integration, analysis and data/knowledge sharing abilities. Twitris 2.0, focuses only on content centric analysis , leveraging the relevant Semantic Web technologies, background knowledge, languages, tools where appropriate.

            Twitris 2.0 is a Semantic Social Web approach to detect social signals by analyzing massive, event-centric data through:
            a. Analysis of casual text with spatio-temporal-thematic (STT) bias, to extract event descriptors.
            b. Capturing semantics from contexts associated with tweets.
            c. Use of deep semantics (using automatically created domain models) to understand the meaning of standard event descriptors.
            d. Use of shallow semantics(semantically annotated entities) for knowledge discovery and representation.
            e. Exposure of processed social data to the public domain, complying with semantic Web standards.
            f. Semantic Integration of multiple external Web resources (news, articles, images and videos) utilizing the semantic similarity between contexts.

    Twitris 2.0 is developed as a multi-layered system where each component acts as part of a pipeline. Here is a Functional Overview of Twitris.

            The system is currently being used for a number of People-Content-Network study experiments and being extended to integrate with SMS and other Web data used by a number of widely deployed open source projects. These include applications used by non governmental organizations (NGO) in developing countries for crisis management (in particular,, and Twitris 2.0 is being extended with Twarql technology for limited real-time support and is being adapted for a cloud platform for much higher scalability.

            TWITRIS is part of a larger research agenda on semantics-enriched social computing [1, 2, 4] at the Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) Center at the Wright State University, Dayton, Ohio (other key themes include semantics-enriched services computing and the sensor Web). For some of the related material, see:

            [1] A. Jadhav et al., Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data, ISWC Semantic Web Challenge 2010.
            [2] A. Sheth, Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness, February 17, 2009.
            [3] A. Sheth, Citizen Sensing, Social Signals, and Enriching Human Experience- IEEE Internet Computing, July/August 2009.
            [4] 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.
            [5] What are people talking about, Why people write, How people write: Meena Najarajan's research
            [6] Real Time Web - A primer Part I and Part II, August 29, 2009

    Semantics driven Content Analysis of Social Media

            Over the last few years, there has been a growing public fascination with 'social media' and its role in modern society. At the heart of this fascination is the ability for users to create and share content via a variety of platforms such as blogs, micro-blogs, collaborative wikis, multimedia sharing sites, social networking sites etc. Our research primarily focuses on the analysis of various aspects of User-Generated Content (UGC) that are central to understanding inter-personal communication on social media. More recently, our interdisciplinary collaboration is studying People-Content-Network analysis. The objective of our work on semantic content analysis is to bring structure and organization to unstructured chatter on social media centered around the following questions:

    - What are people talking about: What are the Named Entities and topics that people are making references to? How are cultures interpreting any situation in local contexts and supporting them in their variable observations on a social medium?
    - How are they expressing themselves: What do word usages tell us about an active population or about individual allegiances or non-conformity to group practices?
    - Why do they scribe: What are the diverse intentions that produce the diverse content on social media? Can we understand why we share by looking at what we predominantly do with the medium? What emotions are people sharing about something?

            On one hand, the social context surrounding the production, consumption, and sharing of user-generated content has opened several opportunities for enriching user interaction with content. On the other hand, this same social aspect to content production has introduced new challenges in terms of the content's informal nature.
            User-generated textual content in social media has unique characteristics that set it apart from the traditional content we find in news or scientific articles. Due to social media's personal and interactive communication format, user-generated content is inherently less formal and unmediated. Off-topic discussions are common, making it difficult to automatically identify context. Content is often fragmented, doesn't always follow English grammar rules, and relies heavily on domain- or demographic-specific slang, abbreviations, and entity variations (using "skik3" for "SideKick 3", for example). Some user-generated content is also terse by nature, such as in Twitter posts, which leaves minimal clues for automatically identifying context. All of these factors make the process of automatically identifying what a social media snippet is actually about much harder.

    Our work in this space spans five closely-aligned projects:

    1. Named Entity Recognition
    2. Language usage in Social Media
    3. Monetization/Targeted Content Delivery on Social Networks
    4. Exploration of People, Content and Network dynamics in the online social networks
    5. Linked Open Social Signals

            Depending on the context, we have studied, analyzed and evaluated a variety of content from Twitter, Facebook, Myspace and Wikipedia and also, we have used a variety of relevant background knowledge to advance traditional computational techniques.

    Please Click here to read details.

            Kno.e.sis is a Center in the College of Engineering and Computer Science at Wright State University, founded as part of Ohio's Third Frontier program. Our cutting-edge research in the use of semantic and services science for data integration, analysis, and process management complements daytaOhio's mission to leverage innovation in data-intensive technologies for economic expansion. Kno.e.sis research focuses on realizing a knowledge society with semantics and services as key enablers. Our work leads to prototypes and their evaluation with the participation of academic and commercial partners, technology development and licensing followed by real-world deployment, and commercialization with our partner institution daytaOhio.

            Kno.e.sis faculty collectively have decades of expertise of in database management (including integration, mining, and visualization), AI and knowledge representation, and bioinformatics. Much of Kno.e.sis's research is transdisciplinary, multidisciplinary, and team-oriented, with a strong practical and systems orientation, and involves partners from industry, the sciences, and government. Key application areas are semantic e-science (including bioinformatics, biomedicine, health care), Web-based information management (including search and business intelligence), and national and homeland security (including intelligence analysis). Our research has pioneered techniques and capabilities related to:

            - Ontology management and multi-ontology environments
            - Integration and analysis of heterogeneous (structured, semi-structured, unstructured) data
            - Advanced and intelligent search, browsing, querying, mining, analysis and knowledge discovery
            - Semantic annotation of documents, scientific data and services involving entity and relationship extraction/disambiguation
            - Semantic enhancement of Web2.0 including social search and lightweight services, semantic middleware and semantics-enabled networking
            - Semantic Web services and processes including semantics-based publication, discovery, composition and dynamic binding of services
            - Data mining
            - Metadata and languages research

    What is Twitris