Personalized and Adaptive Semantic Information Filtering for Social Media - Pavan Kapanipathi's Defense

Personalized and Adaptive Semantic Information
Filtering for Social Media
Pavan Kapanipathi, PhD Candidate
Kno.e.sis Center, Wright State University
Committee: Drs. Amit Sheth (Advisor), Krishnaprasad Thirunarayan,
Derek Doran, and Prateek Jain
Ohio Center of Excellence in Knowledge-Enabled Computing

Information Consumption on Social
Media
• Updates of Friends and
Acquaintances
3
Introduction

Media
Acquaintances
• News [1]
– 86% of Twitter
users surveyed
4
Introduction

Media
Acquaintances
• News [1]
– 86% of Twitter
users surveyed
• Medical Information [2]
– 1 in 3 use social media
5
Introduction

Media
Acquaintances
• News [1]
– 86% of Twitter
users surveyed
• Medical Information [2]
– 1 in 3 use social media
• Disaster Management [3]
– 20 million tweets on Hurricane Sandy
– Most crisis management agencies
monitor social media 6
Introduction

Information Overload on Social
Media
• Users often complain of
getting overwhelmed with
the information on social
media
• 5 billion posts per day
– Real-time information
• 1000+ in my social network
7
“...a wealth of information creates a poverty of attention...”
Herbert A. Simon
Introduction

Need for Information Filtering
• Scenario
– Address information overload
– Enormous data stream has to be
filtered
• Information Filtering Systems
– Emails, News, and Blogs
– Functionality
• Understand user interests
• Deliver relevant information
8
Introduction

Traditional Information
Filtering
9
User Interest
Identification/User
Modeling
Filtering Module
Streaming Data
User
Generated
Content
Filtered
Data
Hanani, Uri, Bracha Shapira, and Peretz Shoval. "Information filtering: Overview of issues, research and systems." User
Modeling and User-Adapted Interaction 11.3 (2001): 203-259.Introduction

Traditional Information
Filtering
10
User Interest
Identification/User
Modeling
Filtering Module
Streaming Data
User
Generated
Content
Filtered
Data
Hanani, Uri, Bracha Shapira, and Peretz Shoval. "Information filtering: Overview of issues, research and systems." User
Modeling and User-Adapted Interaction 11.3 (2001): 203-259.
NBA
Basketball
Sports
Relevance: 0.9
Introduction

Challenges
1. Lack of Context
• Lack of context for processing short-text
– Short-Text
• Average length of social media posts (Facebook, Twitter, Google+, etc.)
are 100-160 characters
• Identifying topics from short-text is important
– We can infer the author’s interest and deliver the tweet to interested
users in the topic
– Traditional techniques are shown to have not perform well on social
media [Sriram 2010, Derczynski 2013]
11
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.

Introduction
Challenges
2. Continuously Changing Vocabulary
• Social media is a real-time platform with information about
latest activities in the real-world
• Hurricane Sandy
– Mitigation, preparedness, recovery, and response phases
– #Frankenstorm and #Sandy, at the start, to #StaySafe and #RedCross during the
disaster and #ThanksSandy and #RestoreTheShore after the hurricane
• Indian Elections
– the announcement of prime ministerial candidates, issues
regarding corruptions, and polls in different states
– #modikisarkar, #NaMo, #VoteForRG, and #CongBJPQuitIndia
12
Civil Unrest Election Natural Disaster

Challenges
3. Scalability
• Practical aspects of the filtering system
• Popularity of social media is increasing
– Facebook has more than 1 billion users
– Twitter has more than 500 million users
• Disseminate information to a huge set of users
– Centralized disseminating systems either overload the client of
the server. (Push or Pull model)
13
Introduction

Introduction
Knowledge Bases
• A common theme across the methodologies developed is
the use of background knowledge and Semantic Web
technologies.
• Background knowledge to process short-text leverage
knowledge bases
14
“If a program is to perform a complex task well, it must
know a great deal about the world in which it operates.”
Lenat & Feigenbaum
Mariners with Humber’s perfect
game.
BaseballJason Herward
Kris Bryant
Chicago Cubs
Sports

Wikipedia as a Knowledge Base
• Requirements for a Knowledge base to be used for filtering
social data
– Diversity and Comprehensiveness: Large set of diverse users on social
media such as Twitter and Facebook
– Real-time updates: Social media is a real-time platform the discusses
dynamic topics
• Wikipedia as the Knowledge base
– Semi structured – Extract the structure
– Diverse: Collaborative effort of 80,000 users with 5 million articles
– Near real-time updates with unbiased views on topics [Ferron 2011]
15
Introduction

Thesis Statement
16
To build an effective information filtering system, background
knowledge and Semantic Web technologies can be used to
address lack of context, dynamic changing vocabulary and
scalability challenges introduced by social media’s short-text
and real-time nature.
Introduction

Outline
• Short-Text: Lack of context for processing
– Hierarchical Interest Graphs
– Built a hierarchical context for tweets leveraging Wikipedia category
structure. This hierarchical context is utilized for user modeling and
recommendations.
– Publications [ESWC 2014, WWWCOMP 2014, TR-JRNL 2016]
• Real-time and dynamic nature: Continuously changing
vocabulary
– A novel methodology that utilizes the evolving Wikipedia hyperlink
structure to detect topic-relevant hashtags for continuous filtering
– Publications [TR-CNF 2016, ESWC 2015]
• Popularity: Scalability
– Scalable distributed dissemination system that utilizes Sematic Web
technologies.
– Publications [ISWC 2011, SPIM 2011, ISWCDEM 2011]
17
Introduction

Outline
– Hierarchical Interest Graphs
– Built a hierarchical context for tweets leveraging Wikipedia category
structure. This hierarchical context is utilized for user modeling and
recommendations.
• Real-time and Dynamic Nature: Continuously Changing
Vocabulary
structure
technologies.
18
Lack of context

Baseball
• User generated content is processed to understand user
interests and filtering
– Tweets are used for these experiments
• Wikipedia category structure comprises taxonomical information
that can be leveraged
– Build context for short text for user interest identification
Processing Short-text for User
Interest Identification
19
“You are what you share”
Charles W. Leadbeater
Lack of context
ESWC 2014

Content Based User Interests
Identification from Social Data
20Semantics
Term Frequency
Based
Techniques
Lower Dim Space
as latent
semantics
Entity Based
Techniques
[Tao 2012][Ramage 2010]
Not sure who the Reds will look too
replace Dusty.some very interesting
jobs open (Cubs, Mariners, Reds, poss
Yanks) Girardi the domino sports
[Yan 2012]
Term Freq
great 1
day 1
sports 2
cubs 2
…
Dim Dist
1dim 0.3
2dim 0.2
3dim 0.2
4dim 0.1
5dim 0.4
Wiki-Entities Freq
Chicago Cubs 2
Cinci Reds 2
White Sox 1
NY Yankees 1
…
Knowledge
Enabled
Approaches
Lack of context
ESWC 2014

Implicit Information from Social Data
21
BroaderRelated
Interests
Major League
Baseball
Major League
Baseball Teams
Baseball
game.
jobs open (Cubs, Mariners, Reds,
poss Yanks) Girardi the domino
San Francisco Giants
Oakland Athletics
Baseball Organizations
Lack of context
ESWC 2014

22
BroaderRelated
Interestsfrom
WikipediaCategory
Structure
Major League
Baseball
Major League
Baseball Teams
Baseball
Methodology: Structured
Hierarchical Knowledge
0.6 1.0 0.3 0.3
Seattle
Mariners
White Sox
Cincinnati
Reds
Chicago Cubs
Transformed
Wikipedia Category
Structure to a
Wikipedia Hierarchy
Lack of context
ESWC 2014

23
SpreadingActivation
Major League
Baseball
Major League
Baseball Teams
Baseball
Methodology: Scoring the Inferred
Hierarchical Knowledge
0.6 1.0 0.3 0.3
Seattle
Mariners
White Sox
Cincinnati
Reds
Chicago Cubs
0.5
0.4
0.1
Lack of context
ESWC 2014

Designing an Activation Function
• Design parameters to adapt to the structure of Wikipedia
Hierarchy
– Uneven distribution of nodes in the hierarchy
• 16 hierarchical levels – most categories between 5-9 hierarchical level
– Raw Normalization 𝐹𝑛𝑖
= 1 𝑛𝑜𝑑𝑒𝑠(𝑖+1)
– Log Normalization 𝐹𝐿 𝑛𝑖
= 1 𝑙𝑜𝑔10 𝑛𝑜𝑑𝑒𝑠(𝑖+1)
– Many-many for category-subcategory relationships
• Boston Red Sox – Major League Baseball Teams , 1901 Establishments
in Massachusetts
– Preferential Path Constraint 𝑃𝑖𝑗= 1 𝑝𝑟𝑖𝑜𝑟𝑖𝑡𝑦𝑗𝑖
– Boosting common ancestors
• More entities activating the concept, better is its importance
– Intersect Booster 𝐵𝑖 = 𝑁𝑒𝑖
𝑁𝑒𝑖𝑐𝑚𝑎𝑥
24
Lack of context
ESWC 2014

Activation Functions
• Bell (Raw Normalization)
𝐴𝑗 = 𝐴𝑖 × 𝐹𝑗
𝑛
𝑖=0
• Bell Log (Log Normalization)
𝐴𝑗 = 𝐴𝑖 × 𝐹𝐿𝑗
𝑛
𝑖=0
• Priority Intersect (Log Normalization , Preferential Path, Intersect
Booster)
𝐴𝑗 = 𝐴𝑖 × 𝐹𝐿𝑗 × 𝑃𝑗𝑖 × 𝐵𝑗
𝑛
𝑖=0
25
i is the child node
j is the category
Ai is the activated value of i
Lack of context
ESWC 2014

26
ActivationFunctions
Major League
Baseball
Major League
Baseball Teams
Baseball
Hierarchical Interest Graph
0.6 1.0 0.3 0.3
Seattle
Mariners
White Sox
Cincinnati
Reds
Chicago Cubs
0.5
0.4
0.1
BELL
BELL LOG
PRIORITY
INTERSECT
Lack of context
ESWC 2014

Hierarchical Interest Graph
Evaluation – User Study
Tweets Entities Distinct
Entities
Categories
in HIG
37 31,927 29,146 13,150 111,535
27
Users Tweets Distribution
Lack of context
ESWC 2014

Evaluation Results of Hierarchical
Interests
28
Graded Precision
Mean Average Precision
Relevant Irrelevant Maybe
k Bell Bell Log Priority
Intersect
Bell Bell Log Priority
Intersect
Bell Bell Log Priority
Intersect
10 0.53 0.67 0.76 0.34 0.23 0.16 0.13 0.10 0.08
20 0.54 0.66 0.72 0.34 0.22 0.19 0.12 0.12 0.09
30 0.53 0.64 0.69 0.34 0.24 0.21 0.13 0.12 0.10
40 0.52 0.61 0.68 0.35 0.26 0.22 0.13 0.13 0.10
50 0.52 0.61 0.67 0.36 0.28 0.24 0.12 0.11 0.09
k Bell Bell Log Priority
Intersect
10 0.64 0.72 0.88
20 0.61 0.7 0.82
30 0.59 0.69 0.79
40 0.58 0.68 0.77
50 0.57 0.67 0.75
Numbers in Bold
portray better
performance
Lack of context
ESWC 2014

On this day in 1934, Major League Baseball
announced it would host its first night games
game, Bulls win and Hawks stay alive
Implicit Interests Evaluation
• Implicit interests are categories of interest that were not
explicitly mentioned in tweets but inferred from the knowledge-
base
29
Category: Major
League Baseball
Explicit
Implicit
Lack of context
ESWC 2014

Summary
Hierarchical Interest Graphs
• Addressed the “Lack of Context” challenge in tweets using
Hierarchical Knowledge base.
– More than 70% of hierarchical interests are implicit.
• A new way to represent Twitter user interests
– Hierarchical Interest Graph with interest scores at each nodes
– Activation Function (models) to determine interest scores
What’s the use?
30
Lack of context
ESWC 2014

HIG-based
Tweet Recommendation Approach
31
Incoming Tweet
Semantic Web: 0.2
World Wide Web: 0.09
Ontology: 0.7
Technology: 0.01
Semantic Search: 0.3
World Wide Web: 0.9
Technology: 0.7
Sports: 0.6
Baseball: 0.4
India: 0.2
United States: 0.2
Semantic Web: 0.2
Pearson
Correlation
Recommend
Y/N?
Lack of context
TR-JRNL 2016

Content-based Tweet
Recommendation Approaches
• Term Frequency based approaches
– User profiles: Built on scoring important terms
• TF, TF-IDF
• Entity Frequency [Tao 2012]
– User profiles: Built on scoring important entities
• Wikipedia Entities
• Extracted using Zemanta
• Support Vector Machines (SVMrank) [Duan 2010]
– User Models built using content and tweet based features
– Tweet content features: Similarity to users tweets, similarity of hashtags,
tweet length, mention of URLs, mention of hashtags.
• Latent Dirichlet Allocation [Ramage 2010]
– User profiles: Distribution of 5 latent topics.
32
Lack of context
TR-JRNL 2016

Experimental Setup
• Utilized the same dataset from the user study
• Training and testing datasets using two assumptions
– Tweets what users share are interesting to them and can be
recommended (UGC Assumption)
• 80% to create user profiles
• 20% (~6,000) to test recommendation
– Retweets of users are interesting to them and can be recommended
(Retweet Assumption and is more popular in literature)
• 30% (~9,000) were retweets, hence used to test recommendation
• 70% to create user profiles
33
Users Tweets Entities
37 31,927 29,146
Lack of context
TR-JRNL 2016

Evaluation Methodology
• Transformed to a top-N recommendation evaluation
– Popular top-N evaluation methodology by Cremonesi et al. [Cremonesi
2010] for Precision/Recall
• Methodology
– For every test tweet – pick random 1000 tweets not tweeted/retweeted
by the author of the test tweet
• Random tweets are considered to be irrelevant to the user
– Score and rank the test tweet with the 1000 random tweets using the
recommendation algorithm
• TF, TFIDF, Entity-based, SVMrank, LDA, and HIG
– If the test tweet is within the top-N, its considered to be a hit otherwise
not ( T is the total number of test tweets)
𝑟𝑒𝑐𝑎𝑙𝑙 = ℎ𝑖𝑡𝑠 𝑇
34
Lack of context
TR-JRNL 2016

Retweet Assumption Evaluation
Results
• Term frequency performs the best for recommending
retweets tweets [Ramage et al 2010]
35
Lack of context
TR-JRNL 2016

UGC Assumption Evaluation Results
• HIG performed better for most top-N but at Top-20 TF-
based approaches performed better.
36
Lack of context
TR-JRNL 2016

Lack of context
Content + Knowledge based
Approach
• TF performed the best in content based approaches
• Merged TF and HIG which augments content with
knowledge bases and recommend using Pearson Correlation
37
World Wide Web: 0.4
Technology: 0.007
Sports: 0.06
Baseball: 0.34
India: 0.102
United States: 0.2
Semantic Web: 0.2
world: 3
great: 10
cricket: 24
slim: 13
good: 40
united: 34
states: 30
T
F
H
I
G
NORMALIZED
world: 0.075
great: 0.25
cricket: 0.6
slim: 0.325
good: 1
united: 0.85
states: 0.75
World Wide Web: 1
Technology: 0.017
Sports: 0.15
Baseball: 0.85
India: 0.25
United States: 0.5
Semantic Web: 0.5
MERGED
world: 0.075
great: 0.25
cricket: 0.6
slim: 0.325
good: 1
united: 0.85
states: 0.75
World Wide Web: 1
Technology: 0.017
Sports: 0.15
Baseball: 0.85
India: 0.25
United States: 0.5
Semantic Web: 0.5
TR-JRNL 2016

Retweet Assumption Evaluation
Results
• TF + HIG performs the best and provides an improvement
of more than 40% at top-20
38
Lack of context
TR-JRNL 2016

UGC Assumption Evaluation Results
• TF + HIG performs the best and provides an improvement
of more than 20% at top-20
39
Lack of context
TR-JRNL 2016

Summary
Hierarchical Interest Graphs
• A new way to represent Twitter user Interests
– Hierarchy Interest Graphs
• Addressed the “Lack of Context” challenge in tweets using
hierarchical knowledge base.
• HIG (knowledge base) augments content to provide
superior performance for tweet recommendation.
40
Lack of context
TR-JRNL 2016

Outline
– Augmented content with hierarchical knowledge from Wikipedia
• 70% of the top-50 interests were implicit (not mentioned in users’
tweets)
• Improved content based tweet recommendation by more than 40%.
Vocabulary
structure to update filters for streaming topic-relevant information
technologies.
41
Lack of context

Outline
• 70% of the top-50 interests were implicit (not mentioned in users’
tweets)
• Improved tweet recommendation by more than 40%.
Vocabulary
structure to update filters for streaming topic-relevant information
technologies.
42
Dynamic vocabulary

• Dynamic topics of interest that continuously evolve over
time
– Indian Elections
• the announcement of prime ministerial candidates, issues
regarding corruptions, and polls in different states
– Hurricane Sandy
• Mitigation, preparedness, recovery, and response phases
Social media: Real-time and Dynamic
Platform
43
Indian Election Hurricane Sandy
Dynamic vocabulary
TR-CNF 2016

• Keyword-based filtering
– Twitter streaming API
• Keywords are dynamically changing based on the
happenings in the real-world
– Necessary to track these keywords to be up-to-date regarding
the topic of interest
Filtering Dynamic Topics on Social
Media
44
#indianelection #sandy
#modikisarkar, #NaMo,
#VoteForRG, and
#CongBJPQuitIndia
#Frankenstorm ,#Sandy,
#RedCross,
#RestoreTheShore
Dynamic vocabulary
TR-CNF 2016

Topic-relevant hashtags that can be used
to crawl all the tweets co-occur with
each other
(1) Colorado Shooting (2) Occupy Wall Street
Analysis with over 6 million tweets
Hindsight Analysis of Topic-relevant
Hashtags
45
<1% of the topic-relevant hashtags can
crawl up to 85% of the tweets
Dynamic vocabulary
TR-CNF 2016

Approach for Detecting Topic-
Relevant Hashtags
46
Co-occurring:
Threshold δ
#indianelection2014
#modikisarkar
Manually started filter
Indian General
Election,_2014
Dynamically Updated
Background Knowledge
One hop from Topic
Page
Entity scoring based
on relevance to the Event
Indian General Elec: 1.0
India: 0.9
Elections: 0.7
UPA: 0.6
BJP: 0.3
NDA: 0.3
Narendra Modi: 0.3
Narendra Modi: 0.9
BJP: 0.7
NDA: 0.6
India: 0.4
Elections: 0.2
Rahul Gandhi: 0.2
Congress: 0.2
Entity Extraction
and Scoring
Normalized
Frequency
Scoring
Latest K (200,500)
Similarity
Check
Extract, Periodically
Update Hyperlink structure
Dynamic vocabulary
TR-CNF 2016

• Dataset – 2 Dynamic topics
– 2012 U S Presidential Elections
– Hurricane Sandy
• δ – Top 25 co-occurring hashtags
– Manual annotation for relevance
Evaluation
47
Event Tags Tweets Co-occ Tags (Distinct) Wiki Entities
US Elections 2012 #election2012 4,855 12,361 (1,460) 614
Hurricane Sandy #sandy 4,818 6,592 (837) 419
Event Tags Tweets (Distinct) Relevant Irrelevant Tweets Entities
US Elections
2012
25 11,504 (10,084) 7,086 2,998 27,558 (4255)
Hurricane Sandy 25 4,905 (4,850) 2,691 2,159 10,719 (2359)
Total 50 15,409 1,4934 9,777 38,219
Dynamic vocabulary
TR-CNF 2016

Evaluation Results
48
Hurricane Sandy 2012 U S Presidential Elections
Subsumption Cosine Jaccard Cooccurance Subsumption Cosine Jaccard Cooccurance
𝑁𝐷𝐶𝐺10 0.93 0.86 0.85 0.65 0.91 0.85 0.89 0.83
𝑁𝐷𝐶𝐺20 0.97 0.93 0.92 0.89 0.98 0.95 0.97 0.94
NDCG
MAP
Dynamic vocabulary
TR-CNF 2016

• Hashtag analysis
– Co-occurrence technique can be used to detect event relevant hashtags
– More popular hashtags are easier to be detected via co-occurrence
• Continuously changing vocabulary for dynamic topics and coverage
– Wikipedia as a dynamic knowledge-base for events
– Determining relevant hashtags using asymmetric similarity measure
– More hashtags in turn increase the coverage of tweets for events
• Content-based location prediction of Twitter users (ESWC 2015)
– Similar framework of relevancy detection was used for location prediction
Dynamic Hashtag Filter
49
Dynamic vocabulary
TR-CNF 2016

Outline
• 70% of the top-50 interests were implicit (not mentioned in users’ tweets)
• Real-time and Dynamic Nature: Continuously Changing Vocabulary
– Hindsight analysis insight: co-occurrence can be used as a starting point
– Utilized Wikipedia as an evolving knowledge base for dynamic topics
• top-5 detected, increased the coverage by more than 3,500 tweets instantly
with a mean average precision of 0.92
technologies.
50
Dynamic vocabulary

Outline
• Real-time and Dynamic Nature: Continuously Changing Vocabulary
– Hindsight analysis insight: co-occurrence can be used as a starting point
– Utilized Wikipedia as an evolving knowledge base for dynamic topics
technologies.
51
Scalability

Content Dissemination
• Centralize content dissemination suffers from scalability
issues
– Server (publisher) or the Client (subscriber) are overwhelmed
– Server for Push and Client for Pull
• Distributed dissemination protocol
– Pubsubhubbub
• Introduced by Google in 2009
• 117 million users and 5.5 billion posts broadcasted by 2011
52
Scalability
ISWC 2011

• PubSubHubbub
– Simple, Open, web-hook based pubsub protocol
– Extension to RSS, Atom.
535353
Publisher SubscriberHub
I have new
content for
feed X
Give me the
latest content for
feed X
Here it is
Subscriber
Subscriber
Subscriber
Subscriber
Here is the
latest content
for feed X
Scalability
ISWC 2011

54
PubSubHubbub Protocol Extension
Pub
Sub - A
Sub - B
Sub - C
Sub - D
Hey I have new
content for feed
topics/preference
Social Graph
and User
Profiles
Get the subscribers
of Pub whose profile
matches
topic/preference
Here is the
new content
of feed X
Give me
the new
content
Here it
is
Semantic Hub
Scalability
ISWC 2011

Publisher – Social Data Annotation
• Preliminary processing of text for filtering
– Information extraction (entities, hashtags, urls, etc.)
• Representing as RDF using vocabulary used by SMOB
– Comprises
• SPARQL Queries representing the subset of subscribers from the Social
Graph in the hub
55
Scalability
<http://twitter.com/rob/statuses/123456789>
rdf:type sioct:MicroblogPost ;
sioc:content "Great day for Chicago sports as
well as Cubs beat the Reds, Sox beat the Mariners with
Humber’s perfect game #chicago“ ;•
sioc:has_creator <http://example.com/rob> ;
moat:taggedWith dbpedia:Chicago ;
moat:taggedWith dbpedia:Chicago_Cubs ;
moat:taggedWith dbpedia:Cincinnati_Reds ;
sioc:topic <http://example.com/tags/chicago> .
ISWC 2011

Semantic Hub
• Performs the matching of processed post to user profiles
– Flexible to different matching techniques
• Pearson correlation or other similarity measures
• Delivers information to relevant subscribers.
56
Scalability
SELECT ?user WHERE {
{ ?user foaf:interest dbpedia:Chicago } UNION
{ ?user foaf:interest dbpedia:Chicago_Cubs } UNION
{ ?user foaf:interest dbpedia:Cincinnati_Reds }
}
ISWC 2011

Semantic Hub: Conclusion
• Framework for distributed dissemination of content using
PubSubHubbub
– Hub takes the load of the filtering module and dissemination of
content
• PubSubHubbub
– 117 million subscriptions by 2011
– 5.5 billion unique feeds by 2011
• Semantic Hub
– Privacy-aware dissemination for distributed social networks
– Real-time filtering
57
Scalability
ISWC 2011

• To build an effective information filtering system, background
knowledge and Semantic Web technologies can be used to
address lack of context, dynamic changing vocabulary and
scalability challenges introduced by social media’s short-text
and real-time nature.
– Augmented content with hierarchical knowledge from Wikipedia to
improve context of short-text
– Utilized Wikipedia as an evolving knowledge base for dynamic topics to
detect topic-descriptors for filtering
• Hindsight analysis insight: co-occurrence can be used as a starting point
– Extended PubSubHubbub, a distributed content dissemination protocol
with Semantic Web technologies for filtering and dissemination
58
Conclusion
Thesis Conclusion

Graduate Journey
• Hierarchical Interest Graphs
– Internship work – IBM TJ Watson Research Center 2013
• Location Prediction of Twitter users
– Alleviates the dependence on training data
• Determining Twitter User Hobbies
– Internship work – Samsung Research America 2014 (Patent
Pending)
• Tweet Filtering and Recommendation
– Addressing the problem of dynamic topic drift. 59
Conclusion

Conclusion
Graduate Journey
• Research Internships
– 2011 DERI, Ireland (ISWC 2011, SPIM 2011, WebSci 2011)
– 2013 IBM TJ Watson Research Center (WWWCOMP 2014,
ESWC2014)
– 2014 Samsung Research America (Patent Pending)
• Invited talks
– IBM TJ Watson Research Center, Frontiers of Cloud
Computing and Big Data Workshop
– EMC CTO Office, Bangalore, Invited Speaker Series
– WSU Advisory Board
• Proposals and Projects
– Twitris – NSF Commercialization
– Ohio State University – NSF Hazards SEES ($2M)
– CITAR (Epidemiology) – NIH EdrugTrends ($1.6M)
• Development of Research Systems
– Twarql – A semantic tweet filtering system.
• Winner of Triplification Challenge (ISem2010)
– Scalable content dissemination on distributed social
networks. (ISWC2011)
– Twitris – A social semantic web for analyzing events.
60
COLLABORATIONS
CITAR

Publications
• [NOISE 2015] Raghava Mutharaju, and Pavan Kapanipathi. Are We Really Standing on the
Shoulders of Giants? 1st Workshop on Negative or Inconclusive Results in Semantic Web
2015, ESWC, 2015.
• [KNOW 2015] Siva Kumar Chekula, Pavan Kapanipathi, Derek Doran, Amit Sheth. Entity
Recommendations Using Hierarchical Knowledge Bases. 4th International Workshop on
Knowledge Discovery and Data Mining Meets Linked Open Data, 2015.
• [ESWC 2015] Pavan Kapanipathi, Revathy Krishnamurthy (Joint first author), Amit Sheth,
Krishnaprasad Thirunarayan. Knowledge Enabled Approach to Predict the Location of Twitter
Users. In Extended Semantic Web Conference, 2015. (acceptance rate 23%).
• [ESWC 2014] Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit Sheth. User
Interests Identification on Twitter Using a Hierarchical Knowledge Base. In Extended Semantic
Web Conference 2014, Crete Greece. (acceptance rate 23%)
• [WWWComp 2014] Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit Sheth.
Hierarchical Interest Graph from Twitter. 23rd International conference on World Wide Web
companion 2014 (WWW companion 2014), Seoul, South Korea.
• [WI 2013] Fabrizio Orlandi, Pavan Kapanipathi, Alexandre Passant, Amit Sheth. Characterising
concepts of interest leveraging Linked Data and the Social Web. The 2013 IEEE/WIC/ACM
International Conference on Web Intelligence, Atlanta, USA, United States, 2013.
• [SPIM 2011] Pavan Kapanipathi, Fabrizio Orlandi, Amit Sheth, Alexandre Passant.
Personalized Filtering of the Twitter Stream. 2nd workshop on Semantic Personalized
Information Management at ISWC 2011, September 2011.
• [ISWC 2011] Pavan Kapanipathi, Julia Anaya, Amit Sheth, Brett Slatkin, Alexandre Passant.
Privacy-Aware and Scalable Content Dissemination in Distributed Social Network. 10th
International Semantic Web Conference 2011, Bonn, Germany, September 2011. (acceptance
rate 22%)
61
Conclusion

Conclusion
Publications• [ISWCDEM 2011] Pavan Kapanipathi, Julia Anaya, Alexandre Passant . SemPuSH: Privacy-
Aware and Scalable Broadcasting for Semantic Microblogging. 10th International Semantic
Web Conference 2011,
• [FSWE 2011] Pavan Kapanipathi. SMOB: The Best of Both Worlds. Federated Social Web
Europe Conference, Berlin, June 3rd -5th 2011.
• [WEBSCI 2011] Alexandre Passant, Owen Sacco, Julia Anaya, Pavan Kapanipathi. Privacy-By-
Design in Federated Social Web Applications, Websci 2011, Koblenz, Germany June 14-17,
2011.
• [ISEM 2010] Pablo Mendes, Pavan Kapanipathi, Alexandre Passant. Twarql: Tapping into the
Wisdom of the Crowd. Triplification Challenge 2010 at 6th International Conference on
Semantic Systems (I-SEMANTICS), [WI 2010]
• [WI 2010] Pablo Mendes, Alexandre Passant, Pavan Kapanipathi, Amit Sheth. Linked Open
Social Signals.WI2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI-10),
• [WEBSCI 2010] Pablo Mendes, Pavan Kapanipathi, Delroy Cameron, Amit Sheth. Dynamic
Associative Relationships on the Linked Open Data Web. In Proceedings of the WebSci10:
Extending the Frontiers of Society On-Line
• [TR-CNF 2016] Pavan Kapanipathi, Krishnaprasad Thirunarayan, Fabrizio Orlandi, Amit Sheth,
Pascal Hitzler. A Real-Time #approach for Continuous Crawling of Events on Twitter by
Leveraging Wikipedia. Technical Report.
• [TR-JRNL 2016] Pavan Kapanipathi, Siva Kumar, Derek Doran, Prateek Jain, Chitra
Venkataramani, Amit Sheth. Hierarchical Knowledge Base enabled Twitter User Modeling and
Recommendation. (Journal).
• [TR-CNFC 2016] Siva Kumar, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit Sheth.
Exploring Taxonomical Interests for Entity Recommendations. Technical report, 2015.
• [TR-CNFC 2016] Sarasi Sarangi, Pavan Kapanipathi, Amit Sheth. Domain-specific Sub graph
Generation. Technical report, 2015. 62

Conclusion
References
• [1] How Do People Use Social Media for Business/Finance News?
http://blog.marketwired.com/2013/11/12/how-do-people-use-social-media-for-businessfinance-news/
• [2] What is the role of social media in healthcare? http://worldofdtcmarketing.com/role-social-media-
healthcare/social-media-and-healthcare/
• [3] Social media use during disaster management http://www.emergency-management-degree.org/crisis/
• [Tao 2012] Tao, K., Abel, F., Gao, Q., and Houben, G.-J. (2012a). Tums: Twitter-based user
modeling service.
• [Ramage 2010] Ramage, D., Dumais, S., and Liebling, D. (2010). Characterizing microblogs with
topic models. AAAI’ 10.
• [Yan 2012] Yan, R., Lapata, M., and Li, X. (2012). Tweet recommendation with graph co-ranking. In
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics.
• [Duan 2010] Duan, Y., Jiang, L., Qin, T., Zhou, M., and Shum, H.-Y. (2010). An empirical study on
learning to rank of tweets. COLING ’10
• [Cremonesi 2010]Cremonesi, P., Koren, Y., and Turrin, R. (2010). Performance of recommender
algorithms on top-n recommendation tasks. RecSys2010
• [Sriram 2010] Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., and Demirbas, M. (2010).
Short text classification in twitter to improve information filtering. SIGIR ’10
• [Derczynsk 2013] Derczynski, L., Maynard, D., Aswani, N., and Bontcheva, K. (2013). Microblog-
genre noise and impact on semantic annotation accuracy. HT ’13,
• [Ferron 2011] Ferron, M. and Massa, P. (2011). Collective memory building in wikipedia: the case
of north african uprisings. WikiSys2011 63

Acknowledgements
64
Funding Agencies Internships and Collaborations
CITAR
Conclusion

Acknowledgements
65
Conclusion

Personalized and Adaptive Semantic Information Filtering for Social Media - Pavan Kapanipathi's Defense

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