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Personalized and Adaptive Semantic Information
Filtering for Social Media
Pavan Kapanipathi, PhD Candidate
Kno.e.sis Cente...
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
...
Information Consumption on Social
Media
• Updates of Friends and
Acquaintances
• News [1]
– 86% of Twitter
users surveyed
...
Information Consumption on Social
Media
• Updates of Friends and
Acquaintances
• News [1]
– 86% of Twitter
users surveyed
...
Information Overload on Social
Media
• Users often complain of
getting overwhelmed with
the information on social
media
• ...
Need for Information Filtering
• Scenario
– Address information overload
– Enormous data stream has to be
filtered
• Infor...
Traditional Information
Filtering
9
User Interest
Identification/User
Modeling
Filtering Module
Streaming Data
User
Genera...
Traditional Information
Filtering
10
User Interest
Identification/User
Modeling
Filtering Module
Streaming Data
User
Gener...
Challenges
1. Lack of Context
• Lack of context for processing short-text
– Short-Text
• Average length of social media po...
Introduction
Challenges
2. Continuously Changing Vocabulary
• Social media is a real-time platform with information about
...
Challenges
3. Scalability
• Practical aspects of the filtering system
• Popularity of social media is increasing
– Faceboo...
Introduction
Knowledge Bases
• A common theme across the methodologies developed is
the use of background knowledge and Se...
Wikipedia as a Knowledge Base
• Requirements for a Knowledge base to be used for filtering
social data
– Diversity and Com...
Thesis Statement
16
To build an effective information filtering system, background
knowledge and Semantic Web technologies...
Outline
• Short-Text: Lack of context for processing
– Hierarchical Interest Graphs
– Built a hierarchical context for twe...
Outline
• Short-Text: Lack of context for processing
– Hierarchical Interest Graphs
– Built a hierarchical context for twe...
Baseball
• User generated content is processed to understand user
interests and filtering
– Tweets are used for these expe...
Content Based User Interests
Identification from Social Data
20Semantics
Term Frequency
Based
Techniques
Lower Dim Space
a...
Implicit Information from Social Data
21
BroaderRelated
Interests
Major League
Baseball
Major League
Baseball Teams
Baseba...
22
BroaderRelated
Interestsfrom
WikipediaCategory
Structure
Major League
Baseball
Major League
Baseball Teams
Baseball
Gre...
23
SpreadingActivation
Major League
Baseball
Major League
Baseball Teams
Baseball
Great day for Chicago sports as well
as ...
Designing an Activation Function
• Design parameters to adapt to the structure of Wikipedia
Hierarchy
– Uneven distributio...
Activation Functions
• Bell (Raw Normalization)
𝐴𝑗 = 𝐴𝑖 × 𝐹𝑗
𝑛
𝑖=0
• Bell Log (Log Normalization)
𝐴𝑗 = 𝐴𝑖 × 𝐹𝐿𝑗
𝑛
𝑖=0
• Pr...
26
ActivationFunctions
Major League
Baseball
Major League
Baseball Teams
Baseball
Great day for Chicago sports as well
as ...
Hierarchical Interest Graph
Evaluation – User Study
Tweets Entities Distinct
Entities
Categories
in HIG
37 31,927 29,146 1...
Evaluation Results of Hierarchical
Interests
28
Graded Precision
Mean Average Precision
Relevant Irrelevant Maybe
k Bell B...
On this day in 1934, Major League Baseball
announced it would host its first night games
Great day for Chicago sports as w...
Summary
Hierarchical Interest Graphs
• Addressed the “Lack of Context” challenge in tweets using
Hierarchical Knowledge ba...
HIG-based
Tweet Recommendation Approach
31
Incoming Tweet
Semantic Web: 0.2
World Wide Web: 0.09
Ontology: 0.7
Technology:...
Content-based Tweet
Recommendation Approaches
• Term Frequency based approaches
– User profiles: Built on scoring importan...
Experimental Setup
• Utilized the same dataset from the user study
• Training and testing datasets using two assumptions
–...
Evaluation Methodology
• Transformed to a top-N recommendation evaluation
– Popular top-N evaluation methodology by Cremon...
Retweet Assumption Evaluation
Results
• Term frequency performs the best for recommending
retweets tweets [Ramage et al 20...
UGC Assumption Evaluation Results
• HIG performed better for most top-N but at Top-20 TF-
based approaches performed bette...
Lack of context
Content + Knowledge based
Approach
• TF performed the best in content based approaches
• Merged TF and HIG...
Retweet Assumption Evaluation
Results
• TF + HIG performs the best and provides an improvement
of more than 40% at top-20
...
UGC Assumption Evaluation Results
• TF + HIG performs the best and provides an improvement
of more than 20% at top-20
39
L...
Summary
Hierarchical Interest Graphs
• A new way to represent Twitter user Interests
– Hierarchy Interest Graphs
• Address...
Outline
• Short-Text: Lack of context for processing
– Augmented content with hierarchical knowledge from Wikipedia
• 70% ...
Outline
• Short-Text: Lack of context for processing
– Augmented content with hierarchical knowledge from Wikipedia
• 70% ...
• Dynamic topics of interest that continuously evolve over
time
– Indian Elections
• the announcement of prime ministerial...
• Keyword-based filtering
– Twitter streaming API
• Keywords are dynamically changing based on the
happenings in the real-...
Topic-relevant hashtags that can be used
to crawl all the tweets co-occur with
each other
(1) Colorado Shooting (2) Occupy...
Approach for Detecting Topic-
Relevant Hashtags
46
Co-occurring:
Threshold δ
#indianelection2014
#modikisarkar
Manually st...
• Dataset – 2 Dynamic topics
– 2012 U S Presidential Elections
– Hurricane Sandy
• δ – Top 25 co-occurring hashtags
– Manu...
Evaluation Results
48
Hurricane Sandy 2012 U S Presidential Elections
Subsumption Cosine Jaccard Cooccurance Subsumption C...
• Hashtag analysis
– Co-occurrence technique can be used to detect event relevant hashtags
– More popular hashtags are eas...
Outline
• Short-Text: Lack of context for processing
– Augmented content with hierarchical knowledge from Wikipedia
• 70% ...
Outline
• Short-Text: Lack of context for processing
– Augmented content with hierarchical knowledge from Wikipedia
• 70% ...
Content Dissemination
• Centralize content dissemination suffers from scalability
issues
– Server (publisher) or the Clien...
• PubSubHubbub
– Simple, Open, web-hook based pubsub protocol
– Extension to RSS, Atom.
535353
Publisher SubscriberHub
I h...
54
PubSubHubbub Protocol Extension
Pub
Sub - A
Sub - B
Sub - C
Sub - D
Hey I have new
content for feed
topics/preference
S...
Publisher – Social Data Annotation
• Preliminary processing of text for filtering
– Information extraction (entities, hash...
Semantic Hub
• Performs the matching of processed post to user profiles
– Flexible to different matching techniques
• Pear...
Semantic Hub: Conclusion
• Framework for distributed dissemination of content using
PubSubHubbub
– Hub takes the load of t...
• To build an effective information filtering system, background
knowledge and Semantic Web technologies can be used to
ad...
Graduate Journey
• Hierarchical Interest Graphs
– Internship work – IBM TJ Watson Research Center 2013
• Location Predicti...
Conclusion
Graduate Journey
• Research Internships
– 2011 DERI, Ireland (ISWC 2011, SPIM 2011, WebSci 2011)
– 2013 IBM TJ ...
Publications
• [NOISE 2015] Raghava Mutharaju, and Pavan Kapanipathi. Are We Really Standing on the
Shoulders of Giants? 1...
Conclusion
Publications• [ISWCDEM 2011] Pavan Kapanipathi, Julia Anaya, Alexandre Passant . SemPuSH: Privacy-
Aware and Sc...
Conclusion
References
• [1] How Do People Use Social Media for Business/Finance News?
http://blog.marketwired.com/2013/11/...
Acknowledgements
64
Funding Agencies Internships and Collaborations
CITAR
Conclusion
Acknowledgements
65
Conclusion
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Personalized and Adaptive Semantic Information Filtering for Social Media - Pavan Kapanipathi's Defense

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Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o

Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.

Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.

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

  1. 1. 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
  2. 2. Social Media 2 Introduction
  3. 3. Information Consumption on Social Media • Updates of Friends and Acquaintances 3 Introduction
  4. 4. Information Consumption on Social Media • Updates of Friends and Acquaintances • News [1] – 86% of Twitter users surveyed 4 Introduction
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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.
  12. 12. 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
  13. 13. 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
  14. 14. 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. BaseballJason Herward Kris Bryant Chicago Cubs Sports
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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] 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
  21. 21. Implicit Information from Social Data 21 BroaderRelated 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. 22. 22 BroaderRelated Interestsfrom WikipediaCategory Structure 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. 23. 23 SpreadingActivation 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
  24. 24. 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
  25. 25. 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. 26. 26 ActivationFunctions 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. Retweet Assumption Evaluation Results • Term frequency performs the best for recommending retweets tweets [Ramage et al 2010] 35 Lack of context TR-JRNL 2016
  36. 36. 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
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. 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
  43. 43. • 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
  44. 44. • 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
  45. 45. 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
  46. 46. 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
  47. 47. • 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
  48. 48. 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
  49. 49. • 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
  50. 50. 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
  51. 51. 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
  52. 52. 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
  53. 53. • 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. 54. 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
  55. 55. 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
  56. 56. 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
  57. 57. 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
  58. 58. • 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
  59. 59. 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
  60. 60. 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
  61. 61. 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
  62. 62. 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
  63. 63. 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
  64. 64. Acknowledgements 64 Funding Agencies Internships and Collaborations CITAR Conclusion
  65. 65. Acknowledgements 65 Conclusion

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