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Active Hash Tags (past 2 hours)
Active Users (past 2 hours)
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*The results are based on analysis of social data with background knowledge from DBPedia and uses Semantic Web technologies.

[CLICK ON A PUSHPIN TO SEE POPULAR TOPICS FROM A LOCATION, ON CHOSEN DATE.]
This location analysis is based only on tweets which have location metadata (~10% of total volume). Be sure to select both time and region of interest to see spatio-temporal-thematic social signals.

Click a section of the chart to view some sentimental tweets.


The following list shows top 100 influential Twitter users while talking about a topic, which can be multi-faceted - thematic,person,organization etc. The following network shows the connectivity among these influential Twitter users about a topic chosen on left menu. Colors denote the user level characteristics such as sentiment polarity for the topic, political affiliation, profession etc. Here, we specifically decided to show the characteristics and connectivity of top users, because they have potential to drive the community for desired actions. (Please check our Insights tab for such examples) Wondering about Science behind it? Check here.

Due to space constraint here, please check our Insights tab for detailed analysis of the following user interaction networks.
(Click on a user in the list or node in the network to see user profile)

Emerging Community Leaders to engage with
User Interaction Network of emerging leaders

Banks

Democrat

Occupy_Chicago

Occupy_LA

Republican

Tea_Party

User Attribute
  • Journalism
  • Politics
  • Academics
  • Art
  • Blogging
  • Business
  • Tech
  • Medical
  • Sports
  • Others

Interpreting Network Analysis

  1. Why network analysis?
    1. Who are the most influential users talking about Tea Party in OWS?
    2. How do these influential set of users connect among themselves and what type of users they are- journalists, politics enthusiast, activists, etc. [If they are strongly connected, their view-point has power to revolutionize community dynamics]
    3. How and why does community evolve in such movement (*Extension of the current widget) We provide context specific influencers and their interaction network tools to answer these questions.
  2. Why a strongly connected influencer community of people talking about Tea Party as compared to Republican or Democrats?
    1. Tea Party supporters have greater presence in OWS movement http://online.wsj.com/article/SB10001424052970203503204577037980400569026.html
    2. Who are the emerging leaders in such communities?
      • Top 10 are dominated by Politics and Journalism enthusiasts
  3. Barack Obama is ranked lesser than other democrat enthusiasts in democrats topical community, Why?
    • Because community is being driven by other emerging leaders in the current context and not by established but casually engaged leaders!
  4. Why community for OccupyLA is less sparsed than for OccupyChicago?
    • OccupyLA is highly organized and active community as compared to OccupyChicago. Even facebook page for OccupyLA reflects such activism.
  5. How will you assist the coordination of the organizing team using such tool?
    • Lets say, its a scenario of emergency response:
      • Identify influencers in the community of resources providers and needs
      • Identify bridges connecting pre-defined informal communities, which can be leveraged for aid in emergency time
      • Identify the engagement potential of influencer by interaction network

Data Stats

Occupy Wall Street Event (Feb 2 2012)

  • Total number of processed tweets: 4.1 M
  • Total number of spotted entities: 1.6 M
  • Total number of spotted hashtags: 9 M
  • Total number of extracted URLs: 2.2 M
  • Total number of Twitter users: 0.6 M

Exploring OWS

Question 1: "Who are the dead people that are mentioned in the context of OWS movement?"

Answers:

  1. Rosa Parks: 639 times
    "The people looking down on #OWS protesters are the ones who would've looked at Rosa Parks and said "Why can't you just take a seat?""
  2. Howard Zinn: 415 times
    "Howard Zinn on Anarchism and Marxism-: http://t.co/H6YTlf1Z #occupywallstreet #occupyoakland #gbtv #tcot The New Left #ocra RT @boogiefinger"

Question 2: "What are the different professions of the people being mentioned in the OWS movement?"

Answers:

  1. Benjamin Franklin, Politician
    "RT @Think_Quotes_: He that is of the opinion money will do everything may well be suspected of doing everything for money. - Benjamin Franklin #OWS #Occupy"

Tag Cloud Reports News for Occupy Oakland

Context (from http://en.wikipedia.org/wiki/Occupy_Wall_Street#January_2012):

January 29 -- Occupy Oakland protesters marched to occupy a vacant building for a community center and new HQ. The police deployed tear gas, flash grenades, and rubber bullets and protesters responded with flares and bottles. The protesters noted the increased level of violence of police and lack of dispersal orders or exit routes. Later, more than 300 protesters were arrested in an afternoon march. Some who escaped arrest entered City Hall, stole an American flag from the City Council chamber and set it on fire.

What is Twitris+?

A Semantic Social Web application with real-time monitoring and multi-faceted analysis of social signals to provide insights and a framework for situational awareness, in-depth event analysis and coordination, emergency response aid, reputation management etc.

Why Twitris+?

Users are sharing voluminous social data (800M+ active Facebook users, 1B+ tweets/week) through social networking platforms accessible by Web and increasingly via mobile devices. This gives unprecedented opportunity to decision makers-- from corporate analysts to coordinators during emergencies, to answer questions or take actions related to a broad variety of activities and situations: who should they really engage with, how to prioritize posts for actions in the voluminous data stream, what are the needs and who are the resource providers in emergency event, how is corporate brand performing, and does the customer support adequately serve the needs while managing corporate reputation etc. We demonstrate these capabilities using Twitris+.

Key Features

  1. Decision making analytics platform for multi-faceted analyses of social data: spatio-temporal-thematic, people-content-network, sentiment-emotion-subjectivity etc.
  2. Answering questions of interests to corporate analysts and event coordinators
  3. Extraction of insights from social signals: Aggregation and filtering of social data, web resources (news, Wikipedia pages, multimedia), SMS data, followed by applying background knowledge to perform multi-faced analyses.
  4. Applications beyond state-of-the-art research in social-computing, such as in Health 2.0, cyber-physical systems etc.

Research Details

Team:

Alan Smith, Ashutosh Jadhav , Hemant Purohit, Lu Chen, Michael Cooney, Pavan Kapanipathi, Pramod Anatharam, Wenbo Wang (Past Members: Karthik Gomadam, Meena Nagarajan)

Supervision:

Prof. Amit Sheth

Publications & Presentations:

Twitris v3: From Citizen Sensing to Analysis, Coordination and Action
Hemant Purohit, Amit Sheth, ICWSM-13 Demo track.

What Kind of #Communication is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit Sheth, John Flach, Shreyansh Bhatt, Computers in Human Behavior (CHB) journal.

Twitris- a System for Collective Social Intelligence
Amit Sheth, Ashutosh Jadhav, Pavan Kapanipathi, Chen Lu, Hemant Purohit, Gary Alan Smith, Wenbo Wang, Encyclopedia of Social Network Analysis and Mining (ESNAM).

Are Twitter Users Equal in Predicting Elections? A Study of User Groups in Predicting 2012 U.S. Republican Presidential Primaries
Lu Chen, Wenbo Wang and Amit P. Sheth, In Proceedings of the Fourth International Conference on Social Informatics (SocInfo'12), 2012.

Harnessing Twitter 'Big Data' for Automatic Emotion Identification
Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan and Amit P. Sheth, In Proceedings of International Conference on Social Computing (SocialCom), 2012.

Topical Anomaly Detection from Twitter Stream
Pramod Anantharam, Krishnaprasad Thirunarayan, and Amit Sheth, Research Note: In the Proceedings of ACM Web Science 2012, Evanston, Illinois, June 22-24, 2012.

Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter
Lu Chen, Wenbo Wang, Meenakshi Nagarajan, Shaojun Wang and Amit P. Sheth, In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), 2012.

Twitris+: Social Media Analytics Platform for Effective Coordination
A. Smith, A. Sheth, A. Jadhav, H. Purohit, L. Chen, M. Cooney, P. Kapanipathi, P. Anantharam, P. Koneru and W. Wang, NSF SoCS Symposium, 2012.

Discovering Fine-grained Sentiment in Suicide Notes
Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth, Biomedical Informatics Insights, vol. 5 (Suppl. 1) pp. 137-145, 2012.

Prediction of Topic Volume on Twitter
Yiye Ruan, Hemant Purohit, Dave Fuhry, Srini Parthasarthy, Amit Sheth, 4th Int'l ACM Conference of Web Science (WebSci), 2012.

Framework for the Analysis of Coordination in Crisis Response
H. Purohit, A. Hampton, V. Shalin, A. Sheth, J. Flach, Workshop in conjunction with CSCW-2012.

Personalized Filtering of the Twitter Stream
Pavan Kapanipathi, Fabrizio Orlandi, Amit Sheth, Alexandre Passant, 2nd workshop on Semantic Personalized Information Management at ISWC 2011.

Citizen Sensing - Mining Social Signals & Perceptions: Microsoft Research Faculty Summit
Amit Sheth, Invited Talk at Microsoft Research Faculty Summit 2011, Redmond, WA, July 19, 2011.

Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter
H. Purohit, Y. Ruan, A. Joshi, S. Parthasarathy, A. Sheth, Workshop on Social Media Engagement, in conjunction with WWW 2011.

Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications
Meenakshi Nagarajan,Amit Sheth,Selvam Velmurugan, Proc of the WWW 2011, March 28 - April 1, 2011, Hyderabad, India, ACM.

Twarql: Tapping into the Wisdom of the Crowd
P. Mendes, P. Kapanipathi, and A. Passant, Triplification Challenge 2010 at 6th International Conference on Semantic Systems (I-SEMANTICS), Graz, Austria, 1-3 September 2010. (Winner of Triplification Challenge 2010).

Linked Open Social Signals
Mendes PN, Passant A, Kapanipathi P, Sheth AP, WI2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10), Toronto, Canada, Aug. 31 to Sep. 3, 2010.

Understanding User-Generated Content on Social Media
Meenakshi Nagarajan, Understanding User-Generated Content on Social Media, Ph.D. Dissertation, Wright State University, 2010.

Multimodal Social Intelligence in a Real-Time Dashboard System
Daniel Gruhl, Meenakshi Nagarajan, Jan Pieper, Christine Robson, Amit Sheth, VLDB Journal on 'Data Management and Mining for Social Networks and Social Media', 6 (2) 2010.

Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data
A. Jadhav, H. Purohit, P. Kapanipathi, P. Ananthram, A. Ranabahu, V. Nguyen, P. Mendes, A. G. Smith, M. Cooney, A. Sheth, ISWC 2010 Semantic Web Application Challenge.

A Qualitative Examination of Topical Tweet and Retweet Practices
Meenakshi Nagarajan, Hemant Purohit, Amit Sheth, 4th Int'l AAAI Conference on Weblogs and Social Media, ICWSM 2010, pp. 295-298.

Some Trust Issues in Social Networks and Sensor Networks
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, Amit Sheth, Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.

Understanding Events Through Analysis Of Social Media
Amit Sheth, Hemant Purohit, Ashutosh Jadhav, Pavan Kapanipathi and Lu Chen, Technical Report, Kno.e.sis Center, 2010.

Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences
Meenakshi Nagarajan, Karthik Gomadam, Amit Sheth, Ajith Ranabahu, Raghava Mutharaju and Ashutosh Jadhav, Tenth International Conference on Web Information Systems Engineering, October 5-7, 2009, 539 - 553.

Citizen Sensing, Social Signals, and Enriching Human Experience
A. Sheth, IEEE Internet Computing, July/August 2009, pp. 80-85.

Analysis and Monetization of Social Data
Amit Sheth, Panel on 'Semantifying Social Networks,' Semantic Technology Conference, June 16, 2009, San Jose, CA.

Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A Comprehensive Path Towards Event Monitoring and Situational Awareness
Amit Sheth, From E-Gov to Connected Governance: the Role of Cloud Computing, Web 2.0 and Web 3.0 Semantic Technologies, Fall Church, VA, February 17, 2009.

Demo Video

Articles and Resources

SemanticWeb.com (November 8, 2012)

Election 2012: The Semantic Recap

New Tech Post and Technology Voice (April 9, 2012)

twitris: Social Media Analysis with Semantic Web Technology

Wright State University News Room (November 14, 2011)

Wright State research seeks sense from social media to aid in emergencies

Twitris Logos and Images for Media Use

Images

Videos