personalized and adaptive semantic information filtering for social media
TRANSCRIPT
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
Social Media
2
Introduction
Information Consumption on Social Media
• Updates of Friends and Acquaintances
3 Introduction
Information Consumption on Social Media
• Updates of Friends and Acquaintances
• News [1]
– 86% of Twitter users surveyed
4
Introduction
Information Consumption on Social Media
• Updates of Friends and Acquaintances
• News [1]
– 86% of Twitter users surveyed
• Medical Information [2]
– 1 in 3 use social media
5 Introduction
Information Consumption on Social Media
• Updates of Friends and 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
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect
game.
Baseball Jason 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
• 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.
• Real-time and Dynamic Nature: Continuously Changing
Vocabulary – A novel methodology that utilizes the evolving Wikipedia hyperlink
structure
• Popularity: Scalability
– Scalable distributed dissemination system that utilizes Sematic Web 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
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.
“You are what you share” Charles W. Leadbeater
Lack of context
ESWC 2014
Content Based User Interests Identification from Social Data
20 Semantics
Term Frequency Based
Techniques
Lower Dim Space as latent
semantics
Entity Based Techniques
[Tao 2012] [Ramage 2010]
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.
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
Bro
ader R
elated
Interests Major League Baseball
Major League Baseball Teams
Baseball
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect
game.
Not sure who the Reds will look too
replace Dusty.some very interesting
jobs open (Cubs, Mariners, Reds,
poss Yanks) Girardi the domino
San Francisco Giants
Oakland Athletics
Baseball Organizations
Lack of context
ESWC 2014
22
Bro
ader
Rel
ated
In
tere
sts
fro
m
Wik
iped
ia C
ateg
ory
St
ruct
ure
Major League Baseball
Major League Baseball Teams
Baseball
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.
Not sure who the Reds will look too
replace Dusty.some very interesting
jobs open (Cubs, Mariners, Reds,
poss Yanks) Girardi the domino
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
Spre
adin
g A
ctiv
atio
n
Major League Baseball
Major League Baseball Teams
Baseball
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.
Not sure who the Reds will look too
replace Dusty.some very interesting
jobs open (Cubs, Mariners, Reds,
poss Yanks) Girardi the domino
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 𝐹𝐿𝑛𝑖 = 1 𝑙𝑜𝑔10𝑛𝑜𝑑𝑒𝑠(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
Act
ivat
ion
Fu
nct
ion
s Major League
Baseball
Major League Baseball Teams
Baseball
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect game.
Not sure who the Reds will look too
replace Dusty.some very interesting
jobs open (Cubs, Mariners, Reds,
poss Yanks) Girardi the domino
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
Great day for Chicago sports as well
as Cubs beat the Reds, Sox beat the
Mariners with Humber’s perfect
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
TF
HIG
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
• Short-Text: Lack of context for processing – 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%.
• Real-time and Dynamic Nature: Continuously Changing
Vocabulary – A novel methodology that utilizes the evolving Wikipedia hyperlink
structure to update filters for streaming topic-relevant information
• Popularity: Scalability
– Scalable distributed dissemination system that utilizes Sematic Web technologies.
41 Lack of context
Outline
• Short-Text: Lack of context for processing – Augmented content with hierarchical knowledge from Wikipedia
• 70% of the top-50 interests were implicit (not mentioned in users’ tweets)
• Improved tweet recommendation by more than 40%.
• Real-time and Dynamic Nature: Continuously Changing
Vocabulary – A novel methodology that utilizes the evolving Wikipedia hyperlink
structure to update filters for streaming topic-relevant information
• Popularity: Scalability
– Scalable distributed dissemination system that utilizes Sematic Web 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
• Short-Text: Lack of context for processing – 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%.
• 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
• Popularity: Scalability – Scalable distributed dissemination system that utilizes Sematic Web
technologies.
50 Dynamic vocabulary
Outline
• Short-Text: Lack of context for processing – 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%.
• 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
• Popularity: Scalability – Scalable distributed dissemination system that utilizes Sematic Web
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.
53 53 53
Publisher Subscriber Hub
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 • 70% of the top-50 interests were implicit (not mentioned in users’ tweets) • Improved content based tweet recommendation by more than 40%.
– 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 • top-5 detected, increased the coverage by more than 3,500 tweets instantly
with a mean average precision of 0.92
– 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
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63
Acknowledgements
64 Conclusion