Twitris By: Bhargabi Chakrabarti 28/03/13. Twitris 28/03/13 “Situation awareness application that care more about knowing what is going on so you can.

Slides:



Advertisements
Similar presentations
Google News Personalization: Scalable Online Collaborative Filtering
Advertisements

Delft University of Technology A Comparative Study of Users’ Microblogging Behavior on Sina Weibo and Twitter Qi Gao Web Information Systems Delft University.
Entity-Centric Topic-Oriented Opinion Summarization in Twitter Date : 2013/09/03 Author : Xinfan Meng, Furu Wei, Xiaohua, Liu, Ming Zhou, Sujian Li and.
WWW 2014 Seoul, April 8 th SNOW 2014 Data Challenge Two-level message clustering for topic detection in Twitter Georgios Petkos, Symeon Papadopoulos, Yiannis.
Spatio-Temporal-Thematic Analysis of Citizen Sensor Data Challenges and Experiences Meenakshi Nagarajan, Karthik Gomadam, Amit Sheth, Ajith Ranabahu, Raghava.
Twarql Tapping Into the Wisdom of the Crowd Pablo N. Mendes, Pavan Kapanipathi, Alexandre Passant I-SEMANTICS Graz, Austria September 2 nd, 2010.
Utilizing Social Media to Understand Human Interaction with Extreme Media Events - The Superstorm Sandy Beta Test Arthur G. Cosby Somya D. Mohanty National.
1 Publishing Linked Sensor Data Semantic Sensor Networks Workshop 2010 In conjunction with the 9th International Semantic Web Conference (ISWC 2010), 7-11.
Text mining Extract from various presentations: Temis, URI-INIST-CNRS, Aster Data …
VISIT: Virtual Intelligent System for Informing Tourists Kevin Meehan Intelligent Systems Research Centre Supervisors: Dr. Kevin Curran, Dr. Tom Lunney,
SNOW Workshop, 8th April 2014 Real-time topic detection with bursty ngrams: RGU participation in SNOW 2014 challenge Carlos Martin and Ayse Goker (Robert.
Mumbai, india. november 26, 2008 another chapter in the war against civilization.
Semantic Web and Web Mining: Networking with Industry and Academia İsmail Hakkı Toroslu IST EVENT 2006.
Semantic Web and Web Mining: Networking with Industry and Academia İsmail Hakkı Toroslu IST EVENT 2006.
Business Intelligence components Introduction. Microsoft® SQL Server™ 2005 is a complete business intelligence (BI) platform that provides the features,
Pierangelo MASSA & Michele CAMPAGNA University of Cagliari, DICAAR COST ACTION TD1202 ESR EVENT 23 – 24 April 2015 IMREDD - Nice, FRANCE Mapping and the.
Emergency Situation Awareness from Twitter for Crisis Management WWW 2012 Workshop on Social Web for Disaster Management CSIRO ICT CENTRE Mark Cameron,
A.Frank Future Gazing Search Engines רואים את הנולד: מנועי חיפוש צופי עתיד Ariel J. Frank Department of Computer Science Bar-Ilan University, Israel
Online real-time tweets extraction, mapping and dissemination Xiannian Chen and Gregory Elmes West Virginia University Chen & West Virginia University2014.
Advances in Technology and CRIS Nikos Houssos National Documentation Centre / National Hellenic Research Foundation, Greece euroCRIS Task Group Leader.
Processing and Analyzing Large log from Search Engine Meng Dou 13/9/2012.
Deriving Topics and Opinions from Microblogs Feng Jiang Supervisors: Jixue Liu & Jiuyong Li.
On Sparsity and Drift for Effective Real- time Filtering in Microblogs Date : 2014/05/13 Source : CIKM’13 Advisor : Prof. Jia-Ling, Koh Speaker : Yi-Hsuan.
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory A. Henson, and Amit P. Sheth Kno.e.sis Center, Ohio Center of Excellence on Knowledge-enabled Computing,
Social scope: Enabling Information Discovery On Social Content Sites
C2-SENSE WP 3 / Task 3.5 (AIT) Bojan Božić, Gerald Schimak, Refiz Duro C2-SENSE WP3 Meeting Paris
Pete Bohman Adam Kunk. What is real-time search? What do you think as a class?
Analysis and Monetization of Social Data Amit P. Sheth Lexis-Nexis Ohio Eminent Scholar Director, Kno.e.sis Center, Wright State University.
Linked-data and the Internet of Things Payam Barnaghi Centre for Communication Systems Research University of Surrey March 2012.
Pete Bohman Adam Kunk. Real-Time Search  Definition: A search mechanism capable of finding information in an online fashion as it is produced. Technology.
Contextual Analysis of User Interests in Social Media Sites – An Exploration with Micro-blogs Nilanjan Banerjee, Dipanjan Chakraborty, Koustuv Dasgupta,
Microblogs: Information and Social Network Huang Yuxin.
Wei Feng , Jiawei Han, Jianyong Wang , Charu Aggarwal , Jianbin Huang
2007. Software Engineering Laboratory, School of Computer Science S E Web-Harvest Web-Harvest: Open Source Web Data Extraction tool 이재정 Software Engineering.
Visual Analytics with Linked Open Data and Social Media for e- Governance Vitaveska Lanfranchi Suvodeep Mazumdar Tomi Kauppinen Anna Lisa Gentile Updated.
Mar del Plata, Argentina, 31 Aug – 1 Sep 2009 ITU-T Kaleidoscope 2009 Innovations for Digital Inclusion José Simões Fraunhofer Institute FOKUS
Figure 1 – Social Media Landscape 2015 (Source: FredCavazza.net)
Local/Global Term Analysis for Discovering Community Differences in Social Networks David Fuhry, Yiye Ruan, and Srinivasan Parthasarathy Data Mining Research.
1 Introduction to Data Mining C hapter 1. 2 Chapter 1 Outline Chapter 1 Outline – Background –Information is Power –Knowledge is Power –Data Mining.
Linked Data Profiling Andrejs Abele National University of Ireland, Galway Supervisor: Paul Buitelaar.
GoRelations: an Intuitive Query System for DBPedia Lushan Han and Tim Finin 15 November 2011
Big Data Quality Challenges for the Internet of Things (IoT) Vassilis Christophides INRIA Paris (MUSE team)
Linked Data Profiling Andrejs Abele UNLP PhD Day Supervisor: Paul Buitelaar.
Pervasive Radar Social Collaborative Augmented Reality Tool Presented By: Muthanna Abdulhussein M7012 Pervasive Computing Final Project Presentation.
Social Mining & Big Data Ecosystem – H2020 Sentiment-enhanced Multidimensional Analysis of Online Social Networks: Perception of the Mediterranean.
Real Time Analysis in Twitter
Event Detection and Opinion Mining
Data Platform and Analytics Foundational Training
Like It or Not: A Survey of Twitter Sentiment Analysis Methods
Name: Sushmita Laila Khan Affiliation: Georgia Southern University
Twitter Data Mining and Sentiment Analysis
Work plan revisited Activity 3 Impact Activity 4 Management
Power of Social Media Analytics
MID-SEM REVIEW.
SMART GROUND platform overview
Stop Data Wrangling, Start Transforming Data to Intelligence
Data Warehousing and Data Mining
Microsoft Boosts Awareness about Juhlsen.com App
Exploratory search: New name for an old hat?
A Network Science Approach to Fake News Detection on Social Media
Qingxia Liu Interactive Hierarchical Tag Clouds for Summarizing Spatiotemporal Social Contents [ICDE 2014] Kang, Wei, Anthony KH Tung,
Text Mining & Natural Language Processing
Text Mining & Natural Language Processing
The Sentient Web: IoT + graphs + AI/ML
Web archives as a research subject
Data Aurora in the MENA Region
Big Data Big Data first appeared towards the end of the 1990’s and has become a buzz word in the last few years.
Presented By: Grant Glass
Topic: Semantic Text Mining
Best Twitter Tools To Embed Twitter Feed On Websites.
Presentation transcript:

Twitris By: Bhargabi Chakrabarti 28/03/13

Twitris 28/03/13 “Situation awareness application that care more about knowing what is going on so you can figure out what to do” - Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences, By Meenakshi Nagarajan, Karthik Gomadam, Amit P. Sheth, Ajith Ranabahu, Raghava Mutharaju, Ashutosh Jadhav

“Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data” By Ashutosh Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Ananthram, Ajith Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, Amit Sheth. Some References

“Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data” By Ashutosh Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Ananthram, Ajith Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, Amit Sheth “Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences” Meenakshi Nagarajan, Karthik Gomadam, Amit P. Sheth, Ajith Ranabahu, Raghava Mutharaju, Ashutosh Jadhav Some References

Overview Background Difficulties What is Twitris? Tweet Extraction Tweet Processing Semantic Context Analysis Semantic Social Data Twitris statistics Demo On-going work Reference 28/03/13

Background 28/03/13 With social networking and microblogging platforms, vast amount of data is being volunteered by users. From this unfiltered, realtime information social perception can be extracted.

Difficulties 28/03/13 Numerous tweets to process and find out what is being said about an event, where and when. Topics of discussion change over time and region. Viewpoints evolve with time or with the occurence of other events. Certain real-world events naturally have a spatial and temporal bias while some others do not.

What is Twitris? 28/03/13 Twitris has been developed with the vision of performing semantic-empowered analysis of a broad variety of social media content. Facilitates understanding social perception by processing event-centric data.

System Overview 28/03/13 Tweet extraction Tweet Processing Tweet Traffic analysis Semantic context Analysis Semantic Social Data

Tweet Extraction 28/03/13 Extracting topically relevant Tweets using : hashtags Set of seed keywords obtained from Google Insights. Two heuristics to enrich the set : Fetching new keywords from Google Insights periodically. TFIDF and n-gram sequence of the Tweet corpus.

Tweet Processing 28/03/13 Clustering Tweets based on their spatial and temporal dimensions, Spatial information : From posters' location. Temporal information : From time of the Tweet obtained through Twitter API.

Tweet Traffic Analysis 28/03/13 Shows an event's popularity over a period of time. A graph of tweet count is generated on a time line for each event

Semantic Context Analysis 28/03/13 Internal Context Analysis Analysing URLs embedded in Tweets. External context Analysis Context obtained from external sources. Mined Internal Context Analysis Sentiment analysis using machine learning techniques(lexicon based classification algorithm). The entity-relationship graph is created using the semantically annotated DBpedia entities.

Semantic Social Data 28/03/13 Tweets are semantically enriched with annotations corresponding to the three types of context. Data is exposed in RDF format and published on LOD

Twitris 2.0 Statistics 28/03/13

Demo 28/03/13

On-going Work 28/03/13 Enabling Twitris to answer time sensitive questions. Twitris is being extended with Twarql for real- time support. Being adapted for cloud platform to increase scalability. Currently it is being integrated with other applications like ushahidi.org for crisis management.

References 28/03/13 Twitris 2.0 : Semantically Empowered System for Understanding Perceptions From Social Data” By Ashutosh Jadhav, Hemant Purohit, Pavan Kapanipathi, Pramod Ananthram, Ajith Ranabahu, Vinh Nguyen, Pablo N. Mendes, Alan Gary Smith, Michael Cooney, Amit Sheth Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences By Meenakshi Nagarajan, Karthik Gomadam, Amit P. Sheth, Ajith Ranabahu, Raghava Mutharaju, Ashutosh Jadhav

Thank You!! 28/03/13