Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web ApplicationsTutorial at WWW2011, Hyderabad, IndiaMarch 28, 2011
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Citizen Sensing
Overview, Social Signals, Enablers
Role of Social Media
Activism, Journalism, Business Intelligence, Global Development
Development-Centric Platforms
Beginnings, Architectures and Possibilities
Systematic Study of Social Media
Spatio-Temporal-Thematic + People-Content-Network Analysis
Trustworthiness in Social Media
Mobile Social Computing
Citizen Sensing @ Real-time
Research Application: Twitris
Conclusion & Future Work
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Outline

Acknowledgements
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Selvam Velmurugan
(Kiirti, eMoksha NGOs)
Meena Nagarajan
(Content Analysis)
Hemant Purohit
(People & Network analysis)
AmitSheth
(Semantic Web)
Ashutosh Jadhav
(Event Analysis)
Lu Chen
(Sentiment Analysis)
Pramod Anantharam
(Social & Sensor web)
Pavan Kapanipathi
(Real Time Web)

Preliminaries
Tutorial description: http://www2011india.com/tutorialstr27.html and http://knoesis.org/library/resource.php?id=1030
Lots of breadth: many examples, some depth: few algorithms, mainly to convey insights
Twitter > Myspace/Facebook > SMS
Each has different reach/focus/importance
Given the time, only parts will be covered today! Citations, further reading at bottom and at the end
Images belong to their copyright holders. Copyright info. for images, where available are at the end.
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Aim
What are research opportunities and technical challenges in gaining insights and use of social media content (esp. citizen sensing)?
Provide a structure to a vast array of issues
Breath, not depth
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Citizen Sensing
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Outline

Citizen Sensing
Common person (citizens of Internet) is able to use Web2.0 and social networks
The human centric activity** of observing, reporting, disseminating information (facts, opinions, views) via text, audio, video and built in device sensor (and smart devices)
** direct/indirect, collective/individual
Human-in-the-loop (participatory) sensing
+ Web 2.0 + Mobile computing
= Emergence of
Citizen-Sensor networks
Image: http://bit.ly/hmZe428
A. Sheth, 'Citizen Sensing, Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.

Understanding meaningful citizen sensor observations
Social Signal Processing: Aggregation, Enhancement, Analysis, Visualization, and Interpretation
Citizen-Sensor network:
Immense potential to disseminate social signals quickly and in real-time
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Social Signals
A. Sheth, 'Citizen Sensing, Social Signals, and Enriching Human Experience', IEEE Internet Computing, July/August 2009, pp. 80-85.
Image:http://bit.ly/gWHSjD

Mobile Platforms Hit Critical Mass, Over 5 billions users
1+B with internet connected mobile devices (2010)
Smartphones> Notebooks + Netbooks (2010E)
500K+ mobile phone applications
74% of mobile phone users (2.4B) worldwide used SMS (2007)
Mobile device might qualify as humankind's primary tool
Redefines the way we engage with people, information, etc.
Enablers: Mobile Devices
& Ubiquitous Connectivity
Mobile is Global
Ubiquity, 24x7
Built in sensors
environmental, biometric/biomedical,...
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Enablers: Web 2.0 & Social Media
500M+ Facebook Users
100M+ Twitter users, 85M+ tweets/day
Internet Users: 1.8 Bln
Large variety of social media and traditional media interact, creating potent mixture
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Types of UGC:
Twitter (text/microblogs)
Facebook (multimedia)
YouTube (videos)
Flicker (images)
Blogs (text)
Ping(Social network for music)
Image: http://bit.ly/euLETT

Citizen Sensing
Role of Social Media (important classes of applications)
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Outline

Citizen Sensors in Action
Mumbai Terror Attack
Iran Election 2009
Haiti Earthquake 2010
US Healthcare Debate 2009
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Image: http://huff.to/hp0OhA

Revolution 2.0
Political/Social Activism
Ghonim, who has been a figurehead for the movement against the Egyptian government, told Blitzer “If you want to liberate a government, give them the internet.”
When Blitzer asked “Tunisia, then Egypt, what’s next?,”
Ghonim replied succinctly “Ask Facebook.”
http://cnn.com/video/?/video/world/2011/02/13/nr.social.media.revolution.cnn
http://cnn.com/video/?/video/tech/2011/02/11/barnett.egypt.social.media.cnn
Egyptian anti-government demonstrator sleeps on the pavement under spray paint that reads 'Al-Jazeera' and 'Facebook' at Cairo's Tahrir square on February 7, 2011.
http://www.cbsnews.com/stories/2011/02/15/eveningnews/main20032118.shtml
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Citizen Journalism
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Twitter Journalism
Images: http://bit.ly/9GVfPQ,
http://bit.ly/hmrTYV

News is increasingly Social
Social News
Social Media and Global Media are inter-twined.
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Business Intelligence:
Trend Spotting, Forecasting, Brand Tracking, Targeted Advertising
Sysomos(http://www.sysomos.com/) - Business intelligence by engaging, measuring and understanding activities in Social Media
Trendspotting(http://trendspotting.com)
- Detecting, analyzingandevaluating trends for business.
Simplify(http://simplify360.com/)
- A collaborativeplatform to monitor, measureandengage
customersusing Social Media.
Shoutlet(http://www.shoutlet.com/)
- Managing social media marketing communication using a single
platform.
Reputation.com(http://www.reputationdefender.com/)
- Preserves privacyanddefendsreputationbyprotectingattacks
onpersonalinformation.
Image: http://bit.ly/eAebBb
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Social Development
(Education, Health, eGov)
LiveMocha (http://www.livemocha.com/)
Online Language learning tool with social engagement
- bridging the gap!!
Soliya(http://www.soliya.net/)
Dialogue between students from diverse backgrounds
across the globe using latest multimedia technologies
ProjectEinstein
(http://digital-democracy.org/what-we-do/programs/)
A photography-based digital penpal program connecting youths in refugee camps to the world
PatientsLikeMe
(http://mashable.com/2010/07/13/social-media-health-trends/)
- Facilitates sharing of health profiles, finding patients with similar
ailments, and learn from discussions.
TrialX(http://trialx.com/)
- Finding clinical trials of new treatments and connecting with clinical trial investigators.
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Image: http://bit.ly/ayyjlU

Citizen Sensing
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Outline

Collaboration
We “simply do not have enough genes to program the brain fully in advance,” we must work together, extending and supporting our own intelligence with “social prosthetic” systems that make up for our missing cognitive and emotional capacities: “Evolution has allowed our brains to be configured during development so that we are ‘plug compatible’ with other humans, so that others can help us extend ourselves.”
- Harvard "Group Brain Project"
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Beginnings
Open Source
Linux, Apache
Social Networks
FaceBook, Twitter, MySpace
Crowd Sourcing
Wikipedia, Kiva, Ushahidi, Kiirti, SwiftRiver, Sahana
Collaborative Governance
Peer-to-Patent, e-Demogracia
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Popular Initiatives
FaceBook + Twitter
Iran post-election protests
Tunisia and Egypt uprisings
Ushahidi
Kenyan post-election violence
India, Lebanon, Afghanistan, and Sudan elections
Haiti Earthquake
Pakistan Floods
Kiirti
BBMP election monitoring
Bangalore AutoWatch
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FixOurCity - Chennai
Built on top of FixMyCity open-source codebase
Stage I
Report by Area/Ward and Street
Integration with Google Map
Displays Ward member name/contact details
Select category of issue, description and severity
Confirmation through email to avoid misuse
Stage II/III
Normalize incoming reports to official wards and categories
Integration with Corporation website to allow auto-forwarding and updating of reports
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Ushahidi
Information Collection: SMS (FrontlineSMS, Clickatell),
Email, Web
Visualization/Interactive Mapping: Timeline, Category,
Geo-spatial
Alerts: Geo-spatial
Admin: User Management, Report Moderation / Creation,
Site Statistics
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SwiftRiver
Filtering and verification of real-time data from channels like Twitter, SMS, Email and RSS feeds. Offers organizations an easy way to apply semantic analysis and verification algorithms to different sources of information.
Speed up the process of managing real-time data streams (email, web, sms, twitter)
Add elusive context (location, historical data) and history (reputation of sources) to online research
Offer a dashboard for monitoring multiple channels of information
Offer advanced aggregation and analytic tools on or offline
Give the user control over advance curation tools and filter
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SwiftRiver Architecture - I
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SwiftRiver Architecture - II
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Free and Open Source Disaster Management system. A web based collaboration tool that addresses the common coordination problems during a disaster between Government, the civil society (NGOs) and the victims themselves.
Sahana

Mapping - Situation Awareness & Geospatial Analysis.
Messaging - Sends & Receives Alerts via Email & SMS.
Document Library - A library of digital resources, such as Photos & Office documents.
Missing Persons Registry: Report and Search for Missing Persons.
Disaster Victim Identification
Requests Management: Tracks requests for aid and matches them against donors who have pledged aid.
Shelter Registry - Tracks the location, distribution, capacity and breakdown of victims in Shelters
Hospital Management System - Hospitals can share information on resources & needs.
Organization Registry - "Who is doing What & Where". Allows relief agencies to coordinate their activities.
Ticketing - Master Message Log to process incoming reports & requests.
Delphi Decision Maker - Supports the decision making of large groups of Experts
Sahana

Peer to Patent
Peer To Patent opens the patent examination process to public participation for the first time. It is an online system that aims to improve the quality of issued patents by enabling the public to supply the USPTO with information relevant to assessing the claims of pending patent applications.
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http://www.peertopatent.org/video/p2p640/VideoPlayer.html
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Peer to Patent - Video

Kiirti
Allows you to set up your own instance of the Ushahidi Platform without having to install it on your own web server.
Provides pre-integrated Voice and SMS reporting capabilities within India.
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Kiirti – Home Page

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Kiirti – User Interaction Flow

Kiirti - Flywheel of Engagement
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Future Possibilities
Online Dispute Resolution
30M+ pending cases in India's courts
Public Policy Reviews
Crisis Management
Effective Local Governance
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Challenges
Challenges
Information overload
Processing and de-duping messages
Accessibility (e.x. network congestion, access points, …)
Incorrect or partial data
Trustworthiness of source (e.x. influence, reputation, …)
Metadata extraction (e.x. geo data, name-entity, sentiment/opinion, …)
Collaboration
Policy discussions
Structure or hierarchy

Citizen Sensing
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Outline

Dimensions of Systematic Study of Social Media
Spatio - Temporal -Thematic+
People - Content - Network
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Social Information
Processing
"Who says what, to whom,
why, to what extent and with what effect?" [Laswell]
Network: Social structure emerges from the aggregate of relationships (ties)
People: poster identities, the active effort of accomplishing interaction
Content : studying the content of communication
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Studying Online Human Social Dynamics
How does the (semantics or style of) content fit into the observations made about the network?

Often, the three-dimensional dynamic of people, content and link structure is what shapes the social dynamic.
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Example: how does the topic of discussion, emotional charge of a
conversation, the presence of an expert and connections between participants; together explain information propagation in a social network?
Image: http://bit.ly/dFzjU2

Why People-Content-Network + Spatial-Temporal-Thematic metadata?(Example of Understanding Crisis Data)
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Metadata/Annotations
Metadata: an organized way to study
Types
Creation/extraction and storage
Use
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Image: http://www.biowisdom.com/tag/metadata/

Metadata Infrastructure:
Example for Tweet Annotation (mapped out tweet)
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Image: http://rww.to/9zyoQa

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http://www.readwriteweb.com/archives what_twitter_annotations_mean.php

`
People Metadata:
Variety of Self-expression Modes
on Multiple Social Media Platforms
Explicit information from user profiles
User Names, Pictures, Videos, Links, Demographic Information, Group memberships...

Implicit information from user attention metadata
Page views, Facebook 'Likes', Comments; Twitter 'Follows', Retweets, Replies..
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People Metadata: Various Types
Identification
Interests
Activity
Network
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People Metadata: Continued
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People Metadata: Continued
Web Presence:
- User affiliations
- KLOUT Score – influence measure (www.klout.com)
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Content Metadata
Content Independent metadata
• date, location, author etc.
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2. Content Dependent metadata
Direct content-based metadata
i. Explicit/Mentioned Content metadata
• named entities in content
ii. Implicit/Inferred Content Metadata
• related named entities from knowledge sources
b. Indirect content-based metadata (External metadata)
• context inferred from URLs in content (images, links to articles, FourSquarecheckins etc.)
V. Kashyap and A. Sheth, 'Semantic Heterogeneity in Global Information Systems: The Role of Metadata,
Context and Ontologies,’ in Cooperative Information Systems: Current Trends and Directions, M. Papazoglou and G. Schlageter (Eds.), Academic Press, 1998, pp. 139-178.

Content Metadata:
Content Independent
For Tweets
Published date and time
Location (where tweet was generated from)
Tweet posting method (smart-phone, twitter.com, clients for twitter)
Author information
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For SMS
Publish date and time
Location (where SMS is generated)
Receiver (NGO, Government organization)
carrier information (available on request)

Content Metadata:
Content Dependent (Tweet)
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Direct Content-based Metadata
Indirect content-based metadata (External metadata)

Content Metadata:
Content Dependent (SMS)
Direct Content-based Metadata
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Network Metadata
Connections/Relationships matter! (foundation for the network)
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Metadata: Creation, Extraction and Storage
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Metadata Creation & Extraction
Extracted Metadata
Directly visible information from the user profile, tweet content & community structure
Created Metadata
After processing information in the user profile, content and/or network structure
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An Example
Length: 109 charactersGeneral topic: Egypt protest
This poor {sentiment_expression: {target: “Lara Logan”, polarity: “negative”}} woman! RT @THRCBS News‘ {entity:{type=“News Agency”}} Lara Logan {entity:{type=“Person”}} Released FromHospital {entity:{type=“Hospital”}} After Egypt {entity:{type=“Country”} Assault {topic} http://bit.ly/dKWTY0 {external_URL}
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Why Semantic Web is a Standard for Social Metadata?
Rich Snippet, open graph: RDFa - Semantic Web based social data standards
Relationships/connections play central role (not just hyperlinks as in Web data)– so relationship as first class object is important
Semantic Web technologies and standards provide better techniques to capture and represent metadata, relationships
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Semantic Web in One Slide
Representing Semantic Web Data
RDF: relationships as first class object <subject, predicate,object>
Representing Knowledge and Agreements
nomenclature, taxonomy, folksonomy, ontology: OWL
Annotation: RDFa, Xlink, model reference
Web of Data: Linked Open Data
Querying: SPARQL
Rules: SWRL, RIF
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How to Save and Use Metadata?
Store metadata as data and use standard database technique
Use filtering and clustering, summarization, statistics - implicit semantics
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Use explicit semantics and Semantic Web standardards and technologies
Semantics = meaning
Richer representation, support for relationships, context
Supports use of background knowledge
Better integration, powerful analysis
Use of RDF data stores/LOD
Semantics- the implicit, the formal and the powerful
Social metadata on the Web [H. Dacquin]

Metadata Creation using Content Analysis
Building on foundations of
Statistical Natural Language Processing
Information Extraction
Semantic Web/ Knowledge Representation
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Metadata Extraction from Informal Text
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Meena Nagarajan,‘Understanding User-Generated Content on Social Media,’ Ph.D. Dissertation, Wright State University, 2010

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Characteristics of Text on Social Media

The Formality of Text
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Content Analysis: Typical Sub-tasks
Sentiment Analysis
What opinions are people conveying via the content?
Author Profiling
What can we infer about the author from the content he posts?
Context (external to content) extraction
URL extraction, analyzing external content
Recognize key entities mentioned in content
Information Extraction (entity recognition, anaphora resolution, entity classification..)
Discovery of Semantic Associations between entities
Topic Classification, Aboutness of content
What is the content about?
Intention Analysis
Why did they share this content?
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Research Efforts, Contributions in this space..
Examining usefulness of multiple context cues for text mining algorithms
Compensating for informal, highly variable language, lack of context
Using context cues: Document corpus, syntactic, structural cues, social medium, external domain knowledge…
In this talk, highlighting sample metadata creation tasks:
NER
Key Phrase Extraction
Intention
Sentiment/Opinion Mining
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Named Entity Recognition
I loved <movie> the hangover </movie>!
Key Phrase Extraction
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Part 1: NER, Key Phrase Extraction

Multiple Context Cues Utilized for NER in Blogs and MySpace Forums
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Multiple Context Cues Utilized for Keyphrase Extraction from Twitter, Facebook and MySpace

Focus, Impact
We focus on techniques that exploit
content and context aspects on social media platforms
Our methods highlight a combination of top-down, bottom-up analysis for informal text
Statistical NLP, ML algorithms over large corpora (bottom-up)
Models and rich knowledge bases in a domain(top-down)
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NAMED ENTITY RECOGNITION
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“I loved your music Yesterday!”
Yesterday is an album
“It was THEHANGOVER of the year..lasted forever..
The Hangover is not a movie
So I went to the movies..badchoice picking “GI Jane”worse now”
GI Jane is a movie
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Task of NER : Identifying and classifying tokens

NER in prior work vs. NER for Informal Text
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Cultural Named Entities
• NER focus in this work: Cultural Named Entities
Artifacts of Culture
Name of a books, music albums, films, video games, etc.
Common words in a language
The Lord of the Rings, Lips, Crash, Up, Wanted, Today, Twilight, Dark Knight…
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What makes cultural entity extraction challenging..
Varied senses, several poorly documented
Star Trek: movies, TV series, media franchise.. and cuisines !!
Changing contexts with recent events
The Dark Knight is a movie, it is also a reference to Obamaand the health care policy
Comprehensive sense definitions, enumeration of contexts, labeled corpora for all senses .. Are Unrealistic expectationswhen building a NER system
NER Relaxing the closed-world sense assumptions
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NER in prior work
vs. NER for Informal Text

A Spot and Disambiguate Paradigm
NER is generally a sequential prediction problem
NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task (PER, LOC, ORGN entities)
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Focus of approach: Spot and Disambiguate Paradigm
Starting off with a dictionary or list of entities we want to spot
Spot, then disambiguate in context (natural language, domain knowledge cues)
Binary Classification
Is this mention of “the hangover” in a sentence referring to a movie?
CoNLL 2003 -- http://www.cnts.ua.ac.be/conll2003/ner/

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NER in prior work
vs. NER for Informal Text

Cultural NER - Two Flavors
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(a) Multiple Senses in the Same Domain
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Algorithm Preliminaries
Problem Definition
– Cultural Entity Identification : Music album, tracks
e.g. Smile (Lilly Allen), Celebration (Madonna)
• Corpus: MySpace comments
– Context-poor utterances
e.g. “Happy 25th Lilly, Alfieis funny”
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• Goal: Semantic Annotation of music named entities (w.r.t MusicBrainz)
MusicBrainz Schema

Using a Knowledge Resource for NER is not straight-forward..
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Approach Overview
Which ‘Merry Christmas’?; ‘So Good’is also a song!
Scoped Relationship graphs
– Using context cues from the content, webpage title, url…
e.g. new Merry Christmas tune
– Reduce potential entity spot size
e.g. new albums/songs
• Generate candidate entities
• Spot and Disambiguate
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Sample Real-world Constraints
Which ‘Merry Christmas’?; ‘So Good’is also a song!
Career Restrictions
- “release your third album already..”
Recent Album restrictions
- “I loved your new album..”
Artist age restrictions
-”happy 25thrihanna, loved alfie btw..”
etc.
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Scoping via Real-world Restrictions
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Scoped Entity Lists
User comments are on MySpace artist pages
– Contextual Restriction: Artist name
– Assumption: no other artist/work mention
Naive spotter has advantage of spotting all possible mentions (modulo spelling errors)
– Generates several false positives
“this is bad news, ill miss you MJ”
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But there are also non-music mentions
Challenge 1: Several senses in the same domain
Scoping relationship graphs narrows possible senses
Solves the named entity identification problem partially
Challenge 2: Non-music mentions
Got your new album Smile. Loved it!
Keep your SMILE on!
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Using Language Features to eliminate incorrect mentions..
Syntactic features
POS Tags, Typed dependencies..
Word-level features
Capitalization, Quotes
Domain-level features
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Supervised Learners
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Hand-labeling - Fairly Subjective
1800+ spots in MySpace user comments from artist pages
Manual annotations for a post: “Keep your <track>SMILE<track>on!”
valid album/track named entity (good spot)invalid named entity (bad spot)hard-to tell (inconclusive)
4-way annotator agreements – shows that agreeing on the accuracy of a spot is hard to do even for domain experts
– Madonna 90% agreement
– Rihanna 84% agreement
– Lily Allen 53% agreement (many named entities of ambiguous nature and usage)
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Combining a Dictionary Spotter + NLP Analytics
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Daniel Gruhl, Meena Nagarajan, Jan Pieper, Christine Robson, AmitSheth,‘Context and Domain Knowledge Enhanced Entity Spotting in Informal Text,’ The 8th International Semantic Web Conference, 2009: 260-276

Lessons Learned - NER on Social Media Text using a Knowledge Base
Intelligent pruning of a knowledge base goes a long way in improving precision
Two stage approach: chaining NL learners over results of domain model based spotters
Improves accuracy up to a further 50%
allows the more time-intensive NLP analytics to run on less than the full set of input data
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Music NER application : BBC SoundIndex (IBM Almaden)Pulse of the Online Music Populace
Daniel Gruhl, MeenakshiNagarajan, Jan Pieper, Christine Robson, Amit Sheth: ‘Multimodal Social Intelligence in a Real-Time Dashboard System,’ special issue of the VLDB Journal on "Data Management and Mining for Social Networks and Social Media", 2010
Project: http://www.almaden.ibm.com/cs/projects/iis/sound/

The Vision
http://www.almaden.ibm.com/cs/projects/iis/sound/
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Several Insights
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Trending popularity of artists
Trending topics in artist pages
Only 4% -ve sentiments, perhaps ignore the Sentiment Annotator on this data source?
Ignoring Spam can change ordering
of popular artists

Predictive Power of Data
Billboards Top 50 Singles chart during the week of Sept 22-28 ’07 vs. MySpace popularity charts.
User study indicated 2:1 and upto 7:1 (younger age groups) preference for MySpace list.
Challenging traditional polling methods!
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KEY PHRASE EXTRACTION
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Key Phrase Extraction - Example
Key phrases extracted from prominent discussions on Twitter around the 2009 Health Care Reform debate and 2008 Mumbai Terror Attack on one day
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Key Phrase Extraction from Social Media Text
Different from Information Extraction
Key phrase extraction does not concern itself with classification into a type
Extracting vs. Assigning Key Phrases
Focus: Key Phrase Extraction
Prior work focus: extracting phrases that summarize a document -- a news article, a web page, a journal article, a book..
Focus here: summarize multiple documents (UGC) around same event/topic of interest
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Key Phrase Extraction on Social Media Content has some differences
1. Need to preserve/isolate the social behind the social data in
summarizing key phrases
What is said in Egypt vs. the USA should be viewed in isolation
2. Need to Accounting for redundancy, variability, off-topic content
“Met up with mom for lunch, she looks lovely as ever, good genes .. Thanks Nike, I love my new Gladiators ..smooth as a feather. I burnt all the calories of Italian joy in one run.. if you are looking for good Italian food on Main, Bucais the place to go.”
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Where is the Social and Cultural Logic in UGC ?
Thematic components
similar messages convey similar ideas
Space, time metadata
role of community and geography in communication
Poster attributes
age, gender, socio-economic status reflect similar perceptions
‘Social applies to data as well as metadata’
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Features used in social Key Phrase extraction (common to prior efforts)
Focus: n-grams, spatio-temporal metadata (social components)
Syntactic Cues: In quotes, italics, bold; in document headers; phrases collocated with acronyms
Document and Structural Cues: Two word phrases, appearing in the beginning of a document, frequency, presence in multiple similar documents etc.
Linguistic Cues: Stemmed form of a phrase, phrases that are simple and compound nouns in sentences etc.
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Key Phrase Extraction Overview
“President Obama in trying to regain control of the health-care debate will likely shift his pitch in September”
1-grams: President, Obama, in, trying, to, regain, ...
2-grams: “President Obama”, “Obama in”, “in trying”, “trying to”...
3-grams: “President Obama in”, “Obama in trying”; “in trying to”...
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A descriptor is an n-gram weighted by:
Thematic Importance
TFIDF, stop words, noun phrases
Redundancy: statistically discriminatory in nature
Variability: contextually important
Spatial Importance (local vs. global popularity)
Temporal Importance (always popular vs. currently trending)
`
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TF-IDF vs. Spatio-temporal-thematic scores rank phrases differently
Foreign relations surfaces up
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: 539-553

Next task : Eliminating Off-topic Content
Frequency based heuristics will not eliminate off-topic content that is ALSO POPULAR
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Popular Key phrases “single”, “Jesus” are unrelated to Madonna’s music
M. Nagarajan et al., Monetizing User Activity on Social Networks - Challenges and Experiences, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99

Elimination off-topic content : Example
• “Yeah i know this a bit off topic but the other electronics forum is dead right now. im looking for a good camcorder, somethin not to large that can record in full HD only ones so far that ive seen are sonys”
• “CanonHV20.Great little cameras under $1000.”
Possible relevant phrases are: ['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']
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• Assume one or more seed words (from domain knowledge base) C1 -['camcorder']
• Extracted Key words / phrasesC2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
• Gradually expand C1 by adding phrases from C2 that are strongly associated with C1
• Mutual Information based algorithm [WISE2009]
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Eliminating off-topic content : Approach Overview

Key Phrases & Aboutness - Evaluations
Are the key phrases we extracted topical and good indicators of what the content is about?
If it is, it should act as an effective index/search phrase and return relevant content
Evaluation Application: Targeted Content Delivery
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Targeted Content Delivery -Evaluations
We took 12K posts from MySpace and Facebook Electronics forums
Extracted Baseline phrases using Yahoo Term Extractor
Extracted phrases using the Key phrase extraction, elimination algorithm described earlier
Generated Targeted Content from Google AdSense
Asked users if the delivered content matched the posts
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Targeted Content for all content vs. extracted key phrases
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User Studies and Results
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Social Key Phrase Extraction : Impact, Contributions
TFIDF + social contextual cues yield more useful phrases that preserve social perceptions
Corpus + seeds from a domain knowledge base eliminate off-topic phrases effectively
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INTENTION MINING
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Why do people share?
Outside of the psychological incentives, broadly, people share to Seek Information OR Share Information
If we understand the intent behind a post, we can build systems that respond to it better
Focus of our work: Understand intent to deliver targeted content
Use case: Online Content-Targeted Advertisements on Social Media Platforms
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Circa 2009 -Content-based Ads
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Today – Content-based Ads on Profiles
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What is going on here..
Ads are targeted on profile interests, demographic data
But Interests on profiles do not translate to purchase intents
– Interests are often outdated..
– Intents are rarely stated on a profile..
• Some profile data does seem to work
– Example: New store openings, sales targeted at location information in a profile
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But Monetizable Intents are Elsewhere, away from their profiles..
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Showing clear intents on MySpace posts but no relevant ads..
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Targeted Content-based Advertizing
–Non-trivial
–Non-policed content
•Brand image, Unfavorable sentiments
–People are there to network
•User attention to ads is not guaranteed
–Informal, casual nature of content
•People are sharing experiences and events
–Main message overloaded with off
topic content
I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(
1Learning from Multi-topic Web Documents for Contextual Advertisement, Zhang, Y., Surendran, A. C., Platt, J. C., and Narasimhan, M.,KDD 2008
124

Focus: Discuss Methodology, Preliminary Results in…
• Identifying intents behind user posts on social networks
– Identify Content with monetization potential
• Identifying keywords for advertizing in user-generated content
– Considering interpersonal communication & off-topic chatter
125
M. Nagarajan et al., ‘Monetizing User Activity on Social Networks - Challenges and Experiences,’ 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99

Investigations
User studies
– Hard to compare activity based ads to s.o.t.a
– Impressions to Clickthroughs
– How well are we able to identify monetizable posts
– How targeted are ads generated using our
keywords vs. entire user generated content
126
M. Nagarajan et al., ‘Monetizing User Activity on Social Networks - Challenges and Experiences,’ 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99

Identifying Intents on SM is different from that on the Web..
Scribe Intent not same as Web Search Intent1
People write sentences, not keywords or phrases
Presence of a keyword does not imply navigational / transactional intents
– ‘am thinking of getting X’ (transactional)
– ‘I like my new X’ (information sharing)
– ‘what do you think about X’ (information seeking)
Useful here would be to identify: Transactional and Information Seeking intents
1B. J. Jansen, D. L. Booth, and A. Spink, “Determining the informational, navigational, and transactional intent of web queries,”Inf. Process. Manage., vol. 44, no. 3, 2008.
127

Not Focusing on the entity but Action Patterns surrounding the entity
“where can I find a chottopspcam”
– User post also has an entity, which is a plus but not the main target of intent identification..
Goal is to study
How questions are asked and
not topic words that indicate what the question is about
128

Conceptual Overview Bootstrapping to learn IS patterns
Take a set of user posts from SNSs
Not annotated for presence or absence of any intent
129

Bootstrapping to learn IS patterns
Generate a universal set of n- gram patterns; freq > f
S = set of all 4-grams; freq > 3
130

Generate set of candidate patterns from seed words
(why,when,where,how,what)
Sc= all 4-grams in S that extract seed words
131

User picks 10 seed patterns from Sc
Sis= ‘does anyone know how’, ‘where do I find’, ‘someone
tell me where’…
132

Gradually expand Sis by adding
Information Seeking patterns from Sc
133

For every pis in Sis generate set of filler patterns
134

‘.* anyone know how’‘ does .* know how’
‘does anyone .* how’ ‘does anyone know .*’
Look for patterns in Sc
Functional compatibility of filler
words used in similar semantic contexts
Empirical support for filler
135

Expanding the Pattern Pool
Functional properties / communicative functions of words
From a subset of LIWC1
– cognitive mechanical (e.g., if, whether, wondering, find)
• ‘I am thinking about getting X’
– adverbs(e.g., how, somehow, where)
–(e.g., someone, anybody, whichever)
• ‘Someone tell me where can I find X’
1Linguistic Inquiry Word Count, LIWC, http://liwc.net
136

Example - Acquiring New Intent Patterns..
• ‘does * know how’
– ‘does someone know how’
• Functional Compatibility -Impersonal pronouns
• Empirical Support –1/3
– ‘does somebody know how’
• Functional Compatibility -Impersonal pronouns
• Empirical Support –0
• Pattern Retained
– ‘does john know how’
• Pattern discarded
Sc= {‘does anyone know how’, ‘where do I find’, ‘someone tell me where’}
• pis= `does anyone know how’
137

Finer Details of the Approach are in the paper..
Iterative algorithm, single-word substitutions, functional usage and empirical support conservatively expand the intent-seeking pool of patterns..
Infusing new patterns and seed words
Stopping conditions
138
M. Nagarajan et al., Monetizing User Activity on Social Networks - Challenges and Experiences, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, Sep 15-18 2009: 92-99

Sample Extracted Patterns
139

Identifying Monetizable Posts
Information Seeking patterns just described are generated offline
Finding the Information seeking intent score of a post
– Extract and compare patterns in posts with extracted information seeking patterns
Transactional intent score of a post
Using LIWC ‘Money’ dictionary : 173 words and word forms indicative of transactions, e.g., trade, deal, buy, sell, worth, price etc.
140

Benchmarking with FB Marketplace
Training corpus
8000 user posts from MySpace Computers, Electronics, Gadgets forum
Information SeekingIntent patterns Extraction
309 unique new patterns, 263 unambiguous
• Testing patterns for recall using ‘To buy’ Facebook Marketplace where all posts are information seeking
– extracted patterns average 81 % recall
141

Next task: Identifying Keywords for Advertizing
Identifying keywords in monetizable posts
– Plethora of work in this space
Off-topic noise removal is our focus
I NEED HELP WITHSONY VEGAS PRO 8!! Ugh and ihave a video project due tomorrow for merrilllynch :(( all ineed to do is simple: Extract several scenes from a clip, insert captions, transitions and thatsit. really. omggicant figure out anything!! help!! and igot food poisoning from eggs. its not fun. Pleasssse, help? :(
142

Conceptual Overview (also see slides in Key Phrase elimination section)
Topical hints
– C1 -['camcorder']
Keywords in post
– C2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
Move strongly related keywords from C2 to C1 one-by-one
– Relatedness determined using information gain
– Using the Web as a corpus, domain independent
143

Example: Off-topic Chatter Elimination
• C1 -['camcorder']
• C2 -['electronics forum', 'hd', 'camcorder', 'somethin', 'ive', 'canon', 'little camera', 'canon hv20', 'cameras', 'offtopic']
• Informative words
['camcorder', 'canon hv20', 'little camera', 'hd', 'cameras', 'canon']
144

Evaluations- User Study
Keywords from 60 monetizable user posts
– Monetizable intent, at least 3 keywords in content
– 45 MySpace Forums, 15 Facebook Marketplace, 30 graduate students
– 10 sets of 6 posts each
– Each set evaluated by 3 randomly selected users
• Monetizable intents?
– All 60 posts voted as unambiguously information seeking in intent
145

Effectiveness of using topical keywords
• Google AdSenseads for user post vs. extracted topical keywords
146

Instructions –User Study
147

Result -2X Relevant Impressions
Users picked ads relevant to the post
– At least 50% inter-evaluator agreement
For the 60 posts
– Total of 144 ad impressions
– 17% of ads picked as relevant
For the topical keywords
– 40% of ads picked as relevant
148

Evaluations: Profile Ads vs. Activity Ads
• Are ads generated from activity more interesting than those generated from user profiles?
Gather user’s profile information
– Interests, hobbies, TV shows.. (non-demographic information)
• Ask them to submit a post (simulating their social media entry)
– Looking to buy and why (induce off-topic content)
• Generate ads from profiles, from post (keywords)
149

Result - 8X more interest for non-profile ads..
• Using profile ads
– 7% of ads generated interest
• Using authored posts
• Using topical keywords from authored posts
150

To note…
User studies small and preliminary, clearly suggest
– Monetization potential in user activity
– Improvement for Ad programs in terms of relevant impressions
Evaluations based on forum, marketplace
– Verbose content
– Status updates, notes, community and event memberships…
– One size may not fit all
151

To note…
A world between relevant impressions and click throughs
– Objectionable content, vocabulary impedance, Ad placement, network behavior
– In a pipeline of other community efforts
No profile information taken into account
– Cannot custom send information to Google AdSense
152

SENTIMENT / OPINION MINING
153

Content Analysis: Sentiment Analysis/Opinion Mining
Two main types of information we can learn from user-generated content: fact vs. opinion
Much of social media text (e.g., blogs, Twitter, Facebook) is a mix of facts and opinions.
For example," Latest news: Mobile web services not working in #Bahrain and Internet is extremely slow #feb14{fact}... looks like they "learned" from #Egypt {opinion}"

Sentiment Analysis: Motivation
Why do people oppose health care reform?
What customers complain about?
Which movie should I see?
155
Image: http://bit.ly/eZtKBF

Sentiment Analysis: Tasks
Example:
“How awful that many #Egypt ian artifacts are in danger of being Destroyed. What ZahiHawassmust be thinking#jan25”
Classification:
Overall sentiment polarity [Pang et al. 2002], [Turney 2002], etc.
the overall polarity is positive, neutral or negative (on the document/sentence/word level)
For the example: overall polarity is negative
Target-specific sentiment polarity [Yi et al. 2003], [Hu et al. 2004], etc.
The polarity toward the given target is positive, neutral or negative
For the Example: polarity is "negative“ for the target "egyptian artifacts“; polarity is "neutral“for target "ZahiHawass"
156

Sentiment Analysis: Tasks
Example:
“How awful that many #Egypt ian artifacts are in danger of being Destroyed. What ZahiHawassmust be thinking #jan25”
Identification & Extraction:
opinion[Dave et al. 2003] etc.
opinion holder [Bethard et al. 2004] etc.
opinion target [Hu et al. 2004] etc.
For the example:
opinion="awful", opinion holder="the author", target="egyptian artifacts are in danger”
opinion="must be thinking", opinion holder="the author", target="ZahiHawass"
157

Sentiment Analysis: Classification
Supervised[Pang et al. 2002] etc.
Labeled training data: e.g., product review, movie review, etc.
Features: e.g., term-based, part-of-speech, syntactic relations, etc.
Learning strategies: e.g.,SVMs), Naive Bayes, ..
Unsupervised [Turney 2002] etc.
lexicon-based approach [Hu et al. 2004], [Ding et al. 2008] etc.
Using a sentiment lexicon of positive/negative sentiment words
Bootstrapping [Thelen et al. 2002] etc.
Iteratively trains and evaluates a classifier, starting from an unannotated corpus and a few predefined seed words,
The task of extracting the opinion/holder/target is similar to the traditional IE task. Key distinction- the relations between opinion and opinion target are considered important.
158

159
Sentiment Analysis:
Identification & Extraction
Information extraction, utilizing the relations between opinion and opinion target
Proximity[Hu et al. 2004] etc.
extract the nearby adjectives modifying the target topic as opinion clues
Syntactic dependency [Popescu et al. 2005] etc.
employed language parser to compute the syntactic dependencies to extract the opinion clues with a given target topic
Co-occurrence[Choi et al. 2009]etc.
heuristics: the more frequently a candidate opinion target co-occurs with any opinion clues, the more likely it is the real opinion target
Prepared patterns/rules [Kobayashi et al. 2004] etc.
using a set of predefined extraction patterns/rules

Sentiment Analysis: From Tweets to polls[O’Connor et al. 2010]
Findings:
A relatively simple sentiment detector based on tweets replicates consumer confidence and presidential job approval polls.
Highlight the potential of text streams as a substitute and supplement for traditional polling.
Connect public opinion measured from polls with sentiment measured from tweets.
Lexicon-based approachfor sentiment analysis of tweets
Within topic tweets, count messages containing positive and negative words defined by the sentiment lexicon
160

Sentiment Analysis: Predicting the Future With Social Media [Asur et al. 2010]
Use tweets to forecast box-ofﬁce revenues for movies.
Traina language model classifier for sentiment classification of tweets.
Findings:
The prediction model using the rate at which tweets are created about a movie outperforms the market-based methods.
The sentiments present in tweets can be used to improve the prediction.
161

Sentiment Analysis: Target-specific Opinion Identification & Classification of Tweets-Unsupervised Approach [kno.e.sis ongoing work]
Simple lexicon-based method doesn't work well.
Target of “sexy” is “Helena”
Target of “terrific” is “reviews”
“free” is not opinionated in
movie domain.
Target of “loving” is “telling”
“well” in “as well” is not
opinionated
Observations:
The opinion clues may not be toward the given target (1,2,3,6)
The opinion clues are domain and context dependent (5,7)
Single words are not enough (4,7,8)
162

Domain and context-aware sentiment lexicon generation (here take the movie domain as example)
General subjective lexicon
Commonly used subjective lexicon + polar slangs learned from dictionary
Select candidate opinion clues from the domain-specific corpus based on the general lexicon
word + surrounding context
E.g., {“free”, “free movie”, “free movie streaming”... }, {“must”, “must see”, “a must see”, “must see movie”…} , {“well”, “as well”, “well done”… }
Identify the opinion clues and their polarity
Utilize information from multiple sources, including the corpus, domain knowledge (e.g., freebase, imdb), general lexicon, etc.
Bootstrapping + statistical model
E.g., <“must”, “must see”, positive>; <“well”, “well done”, positive>

163

164
Target-specific opinion identification/extraction
Predefined rules
When generating the domain and context-aware sentiment lexicon, use a set of predefined rules to select toward-target candidate opinion clues
Syntactic dependencies
When using the generated lexicon to extract target-specific opinion, for each pair of <target, opinion clues> in one tweet, determine whether the opinion clues is toward the target based on their syntactic dependency.
E.g., Lovedthe King's Speech. Funny, moving...Colin Firth is so amazing. I know, you already knew that. (“amazing” won’t be extracted since nsubj(amazing, Firth) )
We also use predefined rules and proximity for complement

PEOPLE ANALYSIS
Deriving People Metadata
from Content Analysis
from Network Analysis
Merge of two approaches
People Analysis showing use of Merger approach (Content+Network) and derived metadata
Finding Influential Users
Finding User Types & Affiliation
Measuring Social Engagement
165

People Analysis: Extracting People Metadata
166

People Analysis: Using Content to Derive People Metadata
Personality Signals
Extrovert, agreeable, open etc
Blogs, Style of Writing
Loose and periodic sentence, connotation etc.
Psychometric analysis of content
Knowledge, abilities, attitude etc.
Sample study: Gendered writing styles online [Ellison et al. 2006, Nagarajan et al. 2009, ICWSM etc.]
Self-expression tends towards attempting homophily in online dating profiles, given the tendency to 'imitate and impress' in courtship
167
Image: http://bit.ly/JZ6eF
Read: ‘How’ people write @Kno.e.sis

People Analysis: Using Network to Derive People Metadata
Interesting questions to ask:
Who are the most popular people* in the network
Who are the most influential people in the network
What are the types of people in the network
Who are the most active people in the communities
Who are the bridges between communities in the network, etc.
(*People may also refer to an organization)
168
Metadata from Network:
Not sufficient to answer the above questions for specific context and time, and hence merger approach (Content + Network) is better
e.g., An Influential node in the network will be function of time and interest of his audience.

People Analysis: Influence
Adding Flavor of Context Analysis
Popularity NOT = Influence!
For individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. [Romero et al. 2010]
Interest Similarity
Homophily causing Reciprocity on Twitter [TwitterRank, Weng et al. 2010]
Klout Score - True Reach, Amplification [http://klout.com]
By Link Analysis Algorithms
Hits [Kleinberg 1999] & variants
PageRank[Brin et al. 1998] & variants etc..
Links not sufficient!
Audience size doesn’t prove influence on twitter [Million Follower Fallacy,Cha et al. 2010]
169
Image: http://bit.ly/9pfTO4

People Analysis: User types & Affiliation
Blogger, Scientist, Journalist, Artist, Trustee, Company X in Domain Y..
- Multiple types and affiliations!
User interest mining
Key Phrase Extraction followed by semantic association on user bio, tweets, lists, favorite posts
Twitter Study [Banerjee et al. 2009]
Semantic analysis of profile description*
Web Presence: Use of Web & Knowledge bases (Wikipedia, Blogs) to build context for user types
Entity Spotting & Extraction, followed by Semantic Association and Similarity with user-type context
170
Image: kahunainstitute.com
*Read Semantics driven Social Media Analysis@ Kno.e.sis

People Analysis: Social Engagement
171
Imagine a crisis scenario such as Haiti (2010) or Japan (2011) Earthquake
emergency teams are looking for ways to help the victims
How effectively the community of people talking about this event online, can grow to reach potential donors and people in need of resources (food, water, first aids etc.)?
What are the best possible ways to communicate between resource providers and people in need of resources?
How teams can coordinate well between volunteers at a victim site, to managers in organizational structure, sitting in offices?

People Analysis: Social Engagement
Can we find levels of user engagement*
Not just limiting to Passive vs. Active
Involvement in conversation, discussion & coordination
Mining Linguistics patterns & Platform specific handles in the conversations
Unsupervised learning approach [Ritter et al. 2010]
Frequency Distribution Analysis of user activity with added Domain Knowledge
posting, retweet, reply, mentions, lists etc.
172
Image: http://www.syscomminternational.com/
*More at Semantics driven Social Media Analysis@ Kno.e.sis

NETWORK ANALYSIS
- Deriving Network Metadata
Interesting questions
Network Analysis – Methods
Models
Metrics
Network Analysis – Algorithms
Graph Partitioning, Traversal
Community Discovery, Evolution
Social Network Analysis
Diffusion
Homophily
Study of 3-D Dynamics (People-Content-Network)
- Analysis & Visualization tools
173

Network Analysis
“To Discover How A, Who is in Touch with B and C,
Is Affected by the Relation Between B & C”
-John Barnes
Interesting questions to ask:
How communitiesform around topics- growth & evolution
What are the effectsof presence of influential participants in the communities
What are the effectsof content nature (or sentiment, opinions) flowing in network on the community life
What is the community structure: degree of separation and sub-communities
174
Foundation of network:
Nodes
Connections/Relationships
Image: http://www.onasurveys.com/

Network Analysis: Methods
Network Modeling Approaches
Random graph model (Erdos-Renyi model)
start with n vertices and add edges between them at random
Small-world model
most nodes are not neighbors of one another, but they can be reached from every other by a small number of hops or steps (Small World Phenomenon)
Scale-free model
degree distribution follows a power law, i.e., frequency of degree varies as a power of its size
175
Image: http://www.kudosdynamics.com/
Important Literature:
[Wasserman et al. 1992, Watts et al. 1998, Albert et al. 2002, Newman et al. 2006, Marin et al. 2010, Easley et al. 2010]

Network Analysis: Methods
176
Network Structure metrics
Centrality, Connected Component, Avg. Degree, Clustering Coefficient, Avg. Path Length, Bridge, Cohesion, Prestige, Reciprocity etc.
Social Network Analysis methods
Centrality (Degree, Eigenvector, Betweenness, Closeness)
Clusters (Cliques and extensions, Communities)
Image: http://www.kudosdynamics.com/
Important Literature:
[Wasserman et al. 1992, Watts et al. 1998, Albert et al. 2002, Newman et al. 2006, Marin et al. 2010, Easley et al. 2010]

Network Analysis: Algorithms
Graph Partitioning & Traversal
Goal: Best time-complexity & reachability
Generally follows Greedy paths
e.g., K-way multilevel Partitioning,
Bron-Kerbosch, K-plex, K-core or N-cliques, DFS, BFS, MST
Community Discovery, growth, evolution
Based on relationship types (e.g., signed network), geography/location based, interest based etc.
Generally follow cluster analysis
e.g., Hierarchical clustering algorithms – Top-down, bottom-up
Further Reading:
Modularity Maximization [Newman et al. 2006]
Algorithms comparison survey [Balakrishnan et al. 2006]
Online Communities [Preece 2001]
177
"We dream in Graph and We analyze in Matrix”
- Barry Wellman, INSNA

Social Network Analysis: Diffusion & Homophily
Social Network Analysis (Interested in information flow)
Can we predict user actions?
Understanding dynamics is challenging!
Why to study Diffusion
Maximizing Spread (Opinion, Innovation, Recommendation)
Outbreak Detection (e.g., disease)
Diffusion Behavior
Power Law distribution[Leskovec et al. 2007]
Factors impacting Diffusion
User Homophily – similar behavior tendency [McPherson et al. 2001]
Sampling strategy [Choudhury et al. 2010], content nature[Nagarajan et al. 2010]etc.
178
Image: http://bit.ly/fGkIBK

Study of 3-D Dynamics- People, Content, Network
Intra Community Activity and connectivity
How well connected are individual nodes (People)
What keeps them strongly connected over time (Relationship types - Knowledge of Content)
179
Will the two communities coordinate well during an event- crisis or disaster?
- Interplay between all three dimensions – P, C, N
Inter-Community Connectivity
Any bridges to connect to the other community? (People)
Any Similarity in actions with the other community (Can Content help?)
Image: http://themelis-cuiper.com

Study of 3-D Dynamics-People, Content, Network
Metadata a powerful tool to explore this dynamics*
Studies in this direction
A Qualitative Examination of Topical Tweet and Retweet Practices [Nagarajan et al. 2010]
How content dictates the network flow
User-Community Engagement by Multi-faceted Features: A Case Study on Twitter [Purohit et al. 2011] [TO BE PRESENTED TOMORROW IN SoME'11]
What factors impact user engagement in topic discussion
180
*Read People-Content-Network Analysis @ Kno.esis

Graphs showing sparse (A) and dense (B) RT networks and their corresponding follower graphs for 'call for action' and 'information sharing' type of tweets
M. Nagarajan, H. Purohit, and A. Sheth, ’A Qualitative Examination of Topical Tweet and Retweet Practices,’ 4th Int'l AAAI Conference on Weblogs and Social Media, ICWSM 2010
181

Analysis & Visualization Tools
Network WorkBench (NWB)
Truthy
Graph-tool
Orange
Pajek
Tulip
…. Many tools!!
Resource:
http://en.wikipedia.org/wiki/Social_network_analysis_software
182
Image:http://truthy.indiana.edu/

Citizen Sensing
183
Outline

Why?
Social media used for critical tasks
In Disaster scenarios (e.g. Haiti earthquake, Gulf oil spill)
For Political revolution (e.g. Egypt political crisis)
In Political and Social policies (e.g. health care reforms)
What?
Two step process for trustworthiness
Remove off-topic content
(How?) Detect spam and misleading content.
Assess data quality of on-topic content
(How?) Trust models to assess trustworthiness of content.
184

Spam in Social Networks
Spamming is a major issue on social media
Email spam drops while social media spam surges2
e.g. spam on twitter
2% of [100 million]1 spam tweets per day!
Graph depicting % of spam tweets
per day on twitter against time.
Image: http://blog.twitter.com/2010/03/state-of-twitter-spam.html
1http://techcrunch.com/2010/09/14/twitter-seeing-90-million-tweets-per-day/
1http://blog.twitter.com/2011/03/numbers.html
2http://www.allspammedup.com/2011/03/email-spam-drops-as-social-media-spam-surges/
185

Spamming on twitter:
Gaining Popularity
Use of popular topic related keywords (e.g. hashtags of trending topics) to propagate off topic content.
Launching malicious attacks
Phishing attacks, virus, malware etc.
Misleading the masses
Propagating false information [Mustafaraj et al. 2010]
Astroturf campaigns during political elections1.
1http://truthy.indiana.edu/
186

“By incorporating the hashtag #Cairo, which was being used to share updates about
the political protests taking place in Egypt, it appeared to many that he was using the
widely followed hashtag to selfishly promote the company’s clothing.”
- by Elisabeth Giammona1
Trending topic “abuse”
Egypt Protests
Image: http://lat.ms/gHHfPg
Will topical consistency between the tweet and the URL help detect this?
Off-topic; Spam? Intelligent marketing?
1http://text100.com/hypertext/2011/02/what-not-to-do-with-hashtags-the-kenneth-cole-debacle/
187

Spam Detection
Content-based features
Content Size and repetition, URL usage, spam words [Benevenuto et al. 2010].
Metadata-based features
Account information, behavior [Ratkiewicz et al. 2010].
Network-based features
Provenance. (e.g. content from a reliable source)
Community voting. (e.g. RT in case of twitter).
Author status in the network (e.g. number of followers, friends, PageRank[Brin et al. 1998], influence [Weng et al. 2010]).
Removal of spam doesn’t guarantee trustworthiness
188

Trust in Social Networks
Reputation, Policy, Evidence, and Provenance used to derive trustworthiness [Artz et al. 2007].
Illustrative examples of online cues used for trust assessment
Wikipedia: article size, number of references, author, edit history, age of the article, edit frequency, etc [Dondi et al. 2006, Moturu et al. 2009].
Product Reviews: number of helpful, very helpful ratings, author expertise, sentiments in comments received for a review, etc [Liu et al. 2008].
189

Gleaning primitive (edge) trust
Trust value between two nodes is quantified using numbers. E.g., [0,1] or [-1,1] or partial ordering [Thirunarayan et al. 2009, Jøsang et al. 2002, Ganeriwal et al. 2008].
Gleaning composite (path) trust
Propagation via chaining and aggregation (transitivity) [Golbeck et al. 2006, Sun et al. 2006]
Some popular algorithms for trust computation
Eigentrust[Kamvar et al. 2003], Spreading Activation [Ziegler et al. 2004], SUNNY [Kuter et al. 2007], etc.
190

Trust Ontology1
Captures semantics of trust.
Enables representation and reasoning with trust.
Semantics of Trust specifies, for a given trustor and trustee, the following features.
{type, value, scope, process}
trustor
trustee
1P. Anantharam, C. A. Henson, K. Thirunarayan, and A. P. Sheth, 'Trust Model for Semantic Sensor and Social Networks: A Preliminary Report', National
Aerospace & Electronics Conference (NAECON), Dayton Ohio, July 14-16th, 2010.
191

Trust Ontology
Trust Type1 - E.g., Referral Trust, Functional Trust and Non-Functional Trust.
Referral Trust – Agent a1 trusts agent a2’s ability to recommend another agent.
Functional Trust – Agent a1 trusts agent a2’s ability.
Non-Functional Trust – Agent a1 distrusts agent a2’s ability.
Trust Value - E.g., Star rating, numeric value or partial ordering.
Trust Scope -E.g., recommendation for a Car Mechanic, Baby sitter or a movie.
1K. Thirunarayan, Dharan K. Althuru, Cory A. Henson, and Amit P. Sheth, “A Local Qualitative Approach to Referral and Functional Trust,”
In: Proceedings of the The 4th Indian International Conference on Artificial Intelligence (IICAI-09), pp. 574-588, December 2009
192

Trust Ontology
Trust Process - Represents the process by which the Trust Value is computed and maintained.
Reputation – based on past behavior.
Policy – based on explicitly stated constraints.
Evidence – based on seeking/verifying evidence.
Provenance – based on lineage information.
193

Trust Ontology
Anna’s car is in terrible shape
Dick is a certified mechanic
Bob has experience with cars
type: referral
process: reputation
scope: car mechanic
value: 8
Bob
type: functional
process: policy
scope: car mechanic
value: 10
ASE certified
type: non-functional
process: reputation
scope: car mechanic
value: 3
Ben
Dick
194
Anna

Citizen Sensing
195
Outline

Instant Discovery: Geo-tagging and location-aware services, in combination with search, have made discovery a two-way street.
Compressed Expression: Mobile makes social networking even more compelling
Outsourced Memory: Cloud-based servers to store all of their mobile applications and databases
196
Automated Decisions: Smart apps helps to make faster decisions or even apps makes decisions for us
Peer Power: Mobiles can create social movements based on peer influence
http://www.technologyreview.com/business/26654/?a=f

Mobile Social Computing (Cont.)
Personalized Branding: advertising are rapidly becoming personalized based on individual's needs and preferences
Mobiles in social development becoming an integral part of development
Coordination in disaster situations
Health care delivery, especially in developing countries
Elections and other forms of political expression
Mobile payments will be widespread
Social Networking Accelerating Growth of Mobile
197

Integrating Social And Sensor Networks
Machine sensor observations are quantitative in nature, while human observations can be both qualitative and quantitative.
Benefits of combining observations from humans and machine sensors
Complementary evidence.
Corroborative evidence.
Applications of integrating heterogeneous sensor observations
Situation Awareness by using human observations to interpret machine sensor observations.
Enhancing trustworthiness using corroborative evidence.
198

Citizen Sensing
199
Outline

200

Real-Time Motivation
People can’t wait for Information
500 years ago
Single life time
20 years ago
Next day or two
Television, News papers
Presently
Minutes are not considered
fast enough
Digital media, Social media
201
Image: http://bit.ly/fg8EI3

Real-Time Social Media
Is Real-Time the future of Web?
Social Media for Real-Time Web
Disaster Management
Ushahidi (www.ushahidi.org)
Real-Time Markets
RealTimeMarkets (http://www.realtimemarkets.com/)
Brand Tracking
Twarql (http://wiki.knoesis.org/index.php/Twarql)
Movie reviews
Flicktweets (www.flicktweets.com)
202

Scenario
CNN - IBN
Feb 2011
Journalist
203
Scenario
Image: http://bit.ly/hlIutz
203

Challenges
Information Overload
Number of tweets that contained the words "Egypt," "Yemen," or "Tunisia" increased more than tenfold after January 23rd: there were 122,319 tweets between January 16 and 23 containing these terms, and 1.3 million tweets between January 24 and January 30.
Real Time
Can we extract social signals (through analysis) as data is generated?
http://blog.sysomos.com/2011/01/31/egyptian-crisis-twitte/
204

A Semantic Web Approach
Expressive description of Information need
Using SPARQL (Instead of traditional keyword search)
Flexibility on the point of view
Ability to "slice and dice" the data in several dimensions: thematic, spatial, temporal, sentiment etc..
Streaming data with Background Knowledge
Enables automatic evolution and serendipity
Scalable Real-Time delivery
Using sparqlPuSH (SFSW'10)
205

Concept Feed
206

Architecture
Linked Open Social Signals @ Kno.e.sis
P. Mendes, A. Passant, P. Kapanipathi, and A. Sheth ‘Linked Open Social Signals,’ WI2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10), Toronto, Canada, Aug. 31 to Sep. 3, 2010.
207

Social Sensor Server
208

Metadata Extractions (Social Sensor Server)
2 Million Entities from DBPedia
Load as Trie for efficiency
N-grams matched
Example: Obama, Barack Obama
URL, HashTag Extraction
Regex extraction
Resolution
URL Resolution: Follows http redirects for resolution
HashTag Resolution: Tagdef, Tagal,WTHashTag.com
209
Other Metadata provided by Twitter
User profile: User Name, Location, Time etc..
Tweet: RT, reply etc..

Structured Data(Social Sensor Server)
RDF Annotation
Common RDF/OWL Vocabularies
FOAF - (foaf-project.org) Friend of a Friend
SIOC - (sioc-project.org) Semantically Interlinked Online Communities
OPO - (online-presence.net) Online Presence Ontology
MOAT - (moat-project.org) — Meaning Of A Tag
210
A snippet of the annotation
<http://twitter.com/ bob/statuses/123456789>
rdf:type sioct:MicroblogPost ;
sioc:content ”Fingers crossed for the upcoming #hcrvote”
sioc:hascreator <http://twitter.com/bob> ;
foaf:maker <http://example.org/bob> ;
moat:taggedWith dbpedia:Healthcare_reform .
<http://twitter.com/bob> geonames:locatedIn
Dbpedia:Ohio .

Semantic Publisher
211

Semantic Publisher
Virtuoso to filter triples
Queries formulated by the users are stored
SPARQL protocol over the HTTP to access rdf from the store
Combine data from tweet with the background knowledge in the rdf store
212

Application Server & Distribution Hub
213

Application Server & Distribution Hub
Distribution Hub
PUSH Model - Pubsubhubbub protocol
Pushes the tweets to the Application Server
Application Server
Delivers data to the Clients
RSS Enable Concept feeds
214

Use Case 4: Competitors
Brand Tracking - Competitors
Background Knowledge (e.g. DBpedia)
IPhone
HPTabletPC
category:Wi-Fi
@anonymized
This Ipad is super cool. Is it better than
category:Touchscreen
?category
skos:subject
skos:subject
?competitor
skos:subject
moat:taggedWith
dbpedia:IPad
?tweet
215

Demonstration
Social Media & News
Topic - HealthCare
1242 Articles from Nytimes
Around 800,000 tweets
President Obama lays out plan for Health care reform in Speech to Joint Session of Congress (10th Sept Timeline.com)
Obama taking an active role in Health talks in pursuing his proposed overhaul of health care system. (13th Aug Nytimes)
216
216

Twarql on Linked Open Data
217
Image: http://bit.ly/aTOV1r
217

Twarql (Real-Time Event Following)
Motivation - Rapidly Evolving Events
Example : Egypt protests, Health care debate, Iran Election

Adapt to the changes of the event
219
Approach – Continuous Semantics
Continuous Semantics @ Kno.e.sis
Sheth, C. Thomas, and P. Mehra, Continuous Semantics to Analyze Real-Time Data,
IEEE Internet Computing, 14 (6), November-December 2010, pp. 84-89.

Twarql (Real-Time Event Following) Twarql (Real-Time Event Following)
Twarql
Delivers tweets in Real-Time
Leverages background knowledge
Constraint (Sparql – Query)
Doozer
Mines Wikipedia
Automatically created domain model
http://wiki.knoesis.org/index.php/Twarql
http://knoesis.wright.edu/research/ModelCreation/

Continuous Semantics(Cycle)
Filter tweets (Twarql)
Leverage automatically created domain models
Extract Event descriptors (Twarql)
From the filtered tweets.
Create Domain models (Doozer)
Use extracted event descriptors and Back to (1).
Continuous Semantics(Cycle)

Twarql(Real-Time Event Following)
Architecture
New addition

Citizen Sensing
223
Outline

224

Twitris - Motivation
Image: http://bit.ly/etFezl
1. Information Overload
Multiple events around us
WHAT to be aware of
Multiple Storylines about same event!!
225

2. Evolution of Citizen Observation
with location and time
226

3. Semantics of Social perceptions
What is being said about an event (theme)
Where (spatial)
When (temporal )
Twitrislets you browse citizen reports using social perceptions as the fulcrum
227

Twitris: Semantic Social Web Mash-up
Facilitates understanding of multi-dimensional social perceptions over SMS, Tweets, multimedia Web content, electronic news media
228
228

Twitris: Architecture
229
MeenakshiNagarajan, KarthikGomadam, AmitSheth, AjithRanabahu, RaghavaMutharaju and AshutoshJadhav, ‘Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences,’ Tenth International Conference on Web Information Systems Engineering, 539 - 553, Oct 5-7, 2009.

Twitris: Functional Overview
230

Twitris: Event Summarization 1
231

Sentiment Analysis
using statistical and machine learning techniques
232

Entity-relationship graph
using semantically annotated DBpedia entities mentioned in the tweets
233

Twitris: Demo, Quick Show
http://twitris.knoesis.org/
Many other interesting efforts – Eg: VivekK. Singh, MingyanGao, and Ramesh Jain. 2010. From microblogs to social images: event analytics for situation assessment. In Proceedings of the international conference on Multimedia information retrieval (MIR '10). ACM, New York, NY, USA, 433-436.
234

Citizen Sensing
235
Outline

Citizen Sensing
236

Twitris: Coordination
Great role in military and NGO rescue operations during emergencies: Haiti and Chile Earthquakes
237

Twitris: Coordination
Coordinating needs and resources in disaster situation
Analyze SMS and Web reports from disaster location
Use domain models for efficient and timely coordination
238
Image: http://bit.ly/hcp4PG
238

Twitris: CommunityFormation
Homophily in society
Bond of common interest
TRUST factor
239

Do you have a sense of immense opportunity of analyzing citizen sensing for useful social signals?
Do you appreciate the broad range of issues and challenges? Did we present examples and a few insights into how to address some unique challenges?
Did spatio-temporal-thematic and people-content-network dimensions present reasonable way to organize cast number of relevant research challenges and techniques?
Do you have more material to follow up on topics of interest?
240
Conclusions

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252

About us
MeenaNagarajan is a research staff scientist at the IBM Almaden Research Center. Meena completed her dissertation on "Understanding User-Generated Content on Social Media" in 2010 at Wright State University Center of Excellence on Knowledge-enabled Computing (Kno.e.sis). She has collaborated with IBM Research, Microsoft Research, Marti Hearst at University of Berkeley and HP Labs. She has extensively published and served on Program Committees for key conferences. She also has the rare distinction of being invited to give a keynote (http://knoesis.org/library/resource.php?id=731), chair a panel before completing her Ph.D., and was selected for the prestigious NSF CI Fellows award. Her research has played a key role in state of the art social media and analytics systems such as BBC SoundIndex.

Amit Sheth is an educator, researcher and entrepreneur. He is the LexisNexis Ohio Eminent Scholar at the Wright State University, Dayton OH. He directs Kno.e.sis - the Ohio Center of Excellence in Knowledge-enabled Computing (http://knoesis.org, http://bit.ly/coe-k) which works on topics in Semantic, Social, Sensor, and Services computing over the Web, with the goal of advancing from the information age to meaning age. Prof. Sheth is an IEEE fellow and is one of the most highly cited authors in Computer Science (h-index = 68, http://bit.ly/CS-h) and World Wide Web (http://bit.ly/mas-www). He is Editor-in-Chief of the International Journal of Semantic Web & Information Systems, joint- Editor-in-Chief of Distributed & Parallel Databases, series co-editor of two Springer book series and serves on several editorial boards. By licensing his funded university research, he has also founded and managed two successful companies. Several commercial products and many operationally deployed applications have resulted from his R&D. More about Amit: http://knoesis.org/amit

SelvamVelmurugan is a technology entrepreneur, social entrepreneur and a leader/evangelist in use of social technology for development. He served in technology and leadership positions at Amazon and led the establishment of Amazon's development center in India. Then he founded eMoksha (emoksha.org), a non-partisan non-profit organization focused on enabling citizen awareness and engagement using internet, mobile technologies and Kiirti (kiirti.org), a platform to enable effective governance by promoting awareness and citizen engagement by engaging citizens through phone, SMS, email and the web. eMoksha and Kiirti projects in developing countries are being studied by organizations around the world (including Harvard and Columbia) to understand the role and impact of technology in enabling transparency and civic engagement. Selvam was selected as a Center for Internet and Society (India) Fellow and was invited to attend TEDIndia in 2009. More about Selvam: http://www.ted.com/profiles/view/id/306995
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Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications

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Citizen Sensor Data Mining, Social Media Analytics and Development Centric Web Applications Presentation Transcript