Market Driven Innovations and Scaling up of Twitris

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This PFI: AIR Technology Translation project focuses on translating Twitris’ collective social media intelligence technology to capabilities well beyond current state-of-the-art social media monitoring and analysis tools. The Twitris platform is important because it can provide collective exploitation of real-time social media streams, and a variety of relevant knowledge, to significantly improve decision-making and support timely actions in various domains of economic, human, and social development. Twitris’ unique features include real-time semantic analysis of social media content along spatio-temporal-thematic, people-content-network, and sentiment-emotion-intent dimensions. These features result in deeper, contextually-relevant analysis and actionable insights when compared to the leading competing technology in this market space. This project will result in a scale-up of Twitris.

This project addresses several technology gaps as it transitions Twitris from a research prototype to a scaled-up technology capable of supporting commercial applications. Consequently, three areas of research and technology enhancement will be conducted: 1) enhancing the functionalities of Twitris with a broad range of location-specific processing that requires addressing the challenge of scarcity of spatial metadata on Twitter, 2) semantics-enhanced filtering and improved user experience for automatic and semi-automatic filtering of tweets, which requires addressing challenges such as content ambiguity and information overload, and 3) scalable architecture supporting domain-specific, knowledge-enabled modules to handle high volume, variety and velocity of data.

In addition, the project will also provide a unique education and training platform for students and recent graduates to prepare them for careers involving entrepreneurship and business and economic development, and careers in startups. Specifically, the project (a) bridges basic research with technology development and intellectual property development that can lead to successful commercialization and (b) involves close collaboration with successful entrepreneurs, business partners, and customers. It will also undertake structured educational activities involving five technical and business courses, while continuing to foster much-needed diversity in high-tech fields and computer science. This project engages several business partners in strategically-important markets to carry out trials involving their customers in an effort to evaluate the efficacy and benefits of research and technology enhancements involved in this scale-up.


People

Principal Investigators: Prof. Amit P. Sheth
Collaborators: Jeremy Brunn, Pavan Kapanipathi, Alan Smith

Funding

Nsf.jpg
  • NSF Award#: IIP 1542911
  • PFI:AIR-TT: Market-driven Innovations and Scaling up of Twitris - A System for Collective Social Intelligence
  • Timeline: 01 Oct 2015 - 31 Mar 2017
  • Award Amount: $200,000.

Social Media

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Related Projects Using Twitris

NSF SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response

NIH eDrugTrends: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use

Harassment: Context-aware Online Harassment Detection on Social Media

Project Safe Neighborhood (PSN): Westwood Partnership to Prevent Juvenile Repeat Offenders

Hazards SEES: Social and Physical Sensing Enabled Decision Support

Depression: Modeling Social Behavior for Healthcare Utilization in Depression

kHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care


Twitris is also being used for graduate courses in Computer Science, Internet Marketing, and Management Sciences.

This project is a follow-on to

I-Corps: Towards Commercialization of Twitris — a system for collective intelligence: (NSF IIP-1343041). Outcome summary video

Publications

  1. Kamer Yuksel, Sergio Biggemann, Amit Sheth, Jeremy Brunn (2016). Using Social Data to Understand Brand Development. Direct/Interactive Marketing Research Summit. Los Angeles, CA.
  2. Sarasi Lalithsena, Pavan Kapanipathi, Amit Sheth (2016). Harnessing Relationships for Domain-specific Subgraph Extraction: A Recommendation Use Case. IEEE International Conference on Big Data. Published. DOI. 10.1109/BigData.2016.7840663.
  3. Sanjaya Wijeratne, Shreyansh Bhatt, Lakshika Balasuriya, Hussein Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Amit Sheth (2017). Feature Engineering for Twitter-based Applications. Feature Engineering for Machine Learning and Data Analytics Guozhu Dong and Huan Liu. Chapman and Hall/CRC.
  4. Andrew Hampton, Shreyansh Bhatt, Alan Smith, Jeremy Brunn, Hemant Purohit, Valerie Shalin, John Flach, Amit Sheth (2017). Constructing Synthetic Social Media Stimuli for an Emergency Preparedness Functional Exercise. 14th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2017). 181. ISSN: 2411-3387
  5. Michelle Miller, Tanvi Banerjee, RoopTeja Muppalla, William Romine, and Amit Sheth (2017). What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention. JMIR Public Health Surveillance. 3 (2), e38. PMID: 28630032
  6. Andrew Hampton, Shreyansh Bhatt, Alan Smith, Jeremy Brunn, Hemant Purohit, Valerie Shalin, John Flach, Amit Sheth (2017). Constructing Synthetic Social Media Stimuli for an Emergency Preparedness Functional Exercise. 14th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2017). 181.
  7. Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William Romine, Amit Sheth (2017). What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention. 3. (2). JMIR Public Health Surveillance, 3. Published. DOI. 10.2196/publichealth.7157.
  8. Monireh Ebrahimi, Amir Hossein Yazdavar, and Amit Sheth (2017). Challenges of Sentiment Analysis for Dynamic Events. Magazine article in IEEE Intelligent Systems (Series: Affective Computing and Sentiment Analysis Editor: Erik Cambria).
  9. Sanjaya Wijeratne, Shreyansh Bhatt, Lakshika Balasuriya, Hussein Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Amit Sheth (2018). Feature Engineering for Machine Learning and Data Analytics. Guozhu Dong and Huan Liu. Chapman and Hall/CRC. ISBN. 9781138744387.
  10. Amit Sheth, Hemant Purohit, Gary Alan Smith, Jeremy Brunn, Ashutosh Jadhav, Pavan Kapanipathi, Chen Lu, Wenbo Wang (2018). Twitris: A System for Collective Social Intelligence. Encyclopedia of Social Network Analysis and Mining 2. Reda Alhajj, Jon Rokne. Springer-Verlag New York. New York. ISBN: 978-1-4939-7132-9.
  11. Amit Sheth, Hemant Purohit, Gary Alan Smith, Jeremy Brunn, Ashutosh Jadhav, Pavan Kapanipathi, Chen Lu, Wenbo Wang (2018). Encyclopedia of Social Network Analysis and Mining 2. Reda Alhajj, Jon Rokne. Springer-Verlag New York. New York. Published. ISBN. 978-1-4939-7130-5.

References

Twitris

News/Media

  1. The Twitris sentiment analysis tool by Cognovi Labs predicted the Brexit hours earlier than polls, TechCrunch, June 29, 2016. [PDF]
  2. Donovan, J.J. The Twitris sentiment analysis tool by Cognovi Labs predicted the Brexit hours earlier than polls, TechCrunch, June 29, 2016.
    1. From the article: "Cognovi Labs is a new analytics startup that relies on Twitris, a Wright State University-developed tool that claims to be able to take a sample of social media chatter about a specific topic and deduce real-time, large-scale, automated sentiment about the specific topic they are researching. As a real-world example of the tool’s capability, the Cognovi Labs research team — led by Wright State University researcher (and Cognovi Labs inventor) Dr. Amit Sheth — analyzed Twitter chatter leading up to the Great Britain/European Union Membership Referendum (Brexit) on June 23. The team was able to predict some six hours before the news broke that the polls leaning toward the “remain” camp were incorrect."
    2. [Hours before the EU referendum votes closed and well before results were declared, Prof. Sheth’s analysis of Twitter data predicted #Brexit - votes for leave outpacing remain. Analysis was done using a campaign set up by Cognovi Labs that is powered by Twitris technology.]
  3. WSU Lab Works to Mine Social Media Posts, Dayton Daily News, July 13, 2016. [PDF]
    1. [Discusses commercialization effort for Twitris developed at Kno.e.sis by Dayton startup Cognovi Labs, and quotes Prof. Sheth.]
  4. Cognovi Labs will study the presidential debates tonight with their emotional sentiment tool, TechCrunch, 09/26/2016
    1. (Live Blogging by TechCrunch, a very prominent technology Web-site, describing findings from real-time analysis using Twitris on social media (Twitter) data related to the 1st US Presidential Debate held on September 26, 2016. From the Web site: "Cognovi Labs is an analytics startup that relies on Twitris, a Wright State University-developed tool and is based in Dayton, OH.”)
  5. Sarah Ammar,Does Size Matter? How Accounting for Tweet Volume Impacts Sentiment Analysis and What That May Mean for the Election , Applied Policy Research Institute, 26 Sept 2016. (This blog by Applied Policy Research Institute (APRI) of Wright State University exlpores some technical analysis issues related to use of Twitris for real-time analysis of the 1st Presidential debate. From the Web page: "If you followed our live tweets, or those of our friends at Kno.e.sis or Cognovi Labs, you saw some interesting real-time data output about how Twitter users were responding to the candidates during last night’s debate.”)
  6. Cognovi Labs is watching the U.S. Presidential Election with its predictive, social sentiment tool ,TechCrunch, 08 Nov 2016.
    1. (The article discusses the use of Twitris technology, developed by the Kno.e.sis Center at Wright State University and licensed by the university to a Dayton area startup Cognovi Labs in analyzing the 2016 US Election and correctly predicting the presidential and senate majority outcomes.)
  7. Cognovi Labs will study the presidential debates tonight with their emotional sentiment tool ,TechCrunch, 26 Nov 2016.Wright State University's Kno.e.sis, and APRI collaborated with Cognivi Labs to do a live analysis of the first 2016 presidential debate using the Twitris technology built by Kno.e.sis; TechCrunch liveblogged some of the observations. Note: This was an excellent opportunity to perform real-world and real-time evaluation of the scalability of Twitris technology that was developed in this project.
  8. Video of TV coverage on Nov 10 and associated article discussing the use of Kno.e.sis developed Twitris technology by WSU’s Applied Policy Research Institute to monitor 2016 Election. Also mentions Cognovi Labs, a Dayton-based startup that has licensed Twitris from WSU, and quotes Prof. Sheth.
  9. Atlanta Journal and Constitution, and WXPI: " University researchers predicted Trump victory with Twitter"
  10. Yahoo! Finance/PRNewswire: Cognovi Labs: Twitter Analytics Startup Predicts Trump Upset in Real-Time : http://finance.yahoo.com/news/cognovi-labs-twitter-analytics-startup-181700129.html
  11. Dayton-area lab mines Twitter to make predictions ,Thomas Gnau, Dayton Daily News, 02 Dec 2016.
  12. Local start-up hails big investments, Tom Gnau, Dayton Daily News, 24 May 2017
  13. News on Cognovi Labs receiving a round of investment, set up with the technology licensed from Kno.e.sis with Prof. Amit Sheth as the founder.
  14. Commercial success: Wright State’s Commercialization unit and Technology Transfer Office help bring university inventions to market Wright State University Newsroom, 12 Oct 2017. (Commercialization of Twitris and the PI headlines this story.)PDF
  15. Wright State’s Amit Sheth honored by Ohio Faculty Council for his technology commercialization success, Wright State University Newsroom, 16 Oct 2017. (Story about Ohio Faculty Council’s selection of the PI for Technology Commercialization Award program)
  16. Two Wright State graduate students help create commercialization success story, Wright State University Communications, July 2018. (The story recounts involvement of two of Prof. Sheth’s former students in the NSF supported commercialization effort that has led to an innovative Dayton-area startup, Cognovi Labs.) PDF
  17. WSU researchers monitored Twitter to predict a Trump victory WHIO TV7, 10 Nov 2018.

Contact

Contact Prof. Amit P. Sheth for more details