The document discusses detecting and modeling user temporal intention in social media. It presents a dissertation plan to analyze how shared resources change over time as links and content are updated. The plan includes collecting data from archives and social media, analyzing factors like shortened URLs and intention, developing models to predict intention, and creating applications to maintain temporal consistency. The goal is to better understand how users' intentions change as shared content changes and to build tools that preserve the intended version.

1. Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
Hany M. SalahEldeen
Old Dominion University
Advisor: Dr. Michael L. Nelson
JCDL ‘12 Doctoral Consortium

2. Michael Jackson Dies
Snapshot on: June 25th 2009
http://web.archive.org/web/20090625232522/http://www.cnn.com/

3. Jeff tweets about it…
Published on: June 25th 2009
https://twitter.com/mdnitehk/status/2333993907

4. Jenny is off the grid
Jeff’s friend Jenny was on a vacation in Hawaii
for a month…

5. Jenny starts catching up a month later
Read on: July26th 2009
When she came back she checked Jeff’s tweets and was
shocked!
https://twitter.com/mdnitehk/status/2333993907

6. Jenny follows the link on July 26th
CNN page on: July 26th 2009
http://web.archive.org/web/20090726234411/http://www.cnn.com/

7. Jenny is confused!
• Implication:
– Jenny thought Jeff is making a joke about her
favorite singer and she got mad at him
• Problem:
– The tweet and the resource the tweet links to
have become unsynchronized.

8. The Egyptian Revolution

9. Reading about it on Storify in
March 2012….
http://storify.com/maq4sure/egypts-revolution

10. I noticed some shared images are missing
http://storify.com/maq4sure/egypts-revolution

11. Some tweets are still intact…
https://twitter.com/miss_amy_qb/status/32477898581483521

12. …and some lost their meaning with the
disappearance of the images
https://twitter.com/aishes/status/32485352102952960
Missing ?
https://twitter.com/omar_chaaban/status/32203697597452289

13. The tweet remains but the shared
image disappeared…
http://yfrog.com/h5923xrvbqqvgzj

14. Cairo….we have a problem
• Implication:
– The reader cannot understand what the author of
the tweet meant because the image is not
available.
• Problem:
– The post is available but the linked resource
(image) is completely missing.

15. The Anatomy of a Tweet

16. The Anatomy of a Tweet
Author’s username
Other user mention
Social
Post Tweet Body
Interaction Publishing Shortened URL Hash Tag
options timestamp to resource
Shared Resource

17. 3 URIs = 3 Chances to fail

18. Explanation in MJ’s example

19. t3 t4 t5 t7 t8 t9 … tn
t1 t2 t6

20. User’s Temporal Intention
The Focus of our research Instrumented shortener
Share time Implicit Explicit
Click time Implicit Explicit
Instrumented web client
Out of our scope
Purview of Facebook, Engineering problem
Twitter, Google, …etc
Solved by providing
tools

21. Sometimes you want a
previous version
The Correct Temporal
Intention
CNN.com at the closest time to the tweet: 25th June 2009 ~ 7pm

22. Sometimes you want the
current version
The Correct Temporal
Intention
In this case the current state of the press releases page

23. Research Question
Can we estimate the users’
intention at the time of posting
and reading to predict and
maintain temporal consistency?

24. Research Goals
• Detect the temporal intention of the:
1. Author upon sharing time
2. The reader upon dereferencing time
• Model this intention as a function of time, nature of the resource,
and its context.
• Predict how resources change with time and the intention behind
sharing them to minimize inconsistency.
• Implement the prediction model to automatically preserve
vulnerable social content that is prone to change or loss.
• Create an environment implementing this framework that
provides a smooth temporal navigation of the social web.

25. Related Work
• User’s Web Search Intention • Persistence of shared resources
– A. Ashkan ECIR ’09 – M. Nelson D-Lib ‘02
– C. Lee AINA ‘05 – R. Sanderson OR’11
– A. Loser IRSW ‘08 – F. McCown JCDL ‘07
– L. Azzopardi ECIR ‘09
– R. Baeza-Yates SPIR‘06
– N. Dai HT ’11
• URL Shortening
– D. Antoniades WWW ’11
• Commercial Intention
– Q. Guo SIGIR ’10 • Tweeting, Micro-blogging and Popularity
– A. Benczur AIRWeb ’07
– S. Wu WWW ’11
– A. Java SNA-KDD ’07
• Sentiment Analysis
– H. Kwak WWW ’10
– G. Mishne AAAI ‘06
– J. Bollen JCS ‘11
• Social Networks Growth and Evolution
• Access to Archives
– B. Meeder WWW ’11
– H. Van de Sompel OR‘09

26. Dissertation Plan
BEGIN
Read Literature
Collect Datasets
Analyze Archives Coverage
Analyze Shortened URIs
Prototype Application
Analyze Shared Resources Persistence and Coverage
Current
Analyze Contextual Intention
State
Create Intention-based dataset
Extract Intention Features
Train a Parametric Model to predict intention
Evaluate, test, cross-validate the model
Create a mockup application
Extend the model to induce preservation
Finish Writing the Dissertation
PhD Defense

27. Dissertation Plan
BEGIN
Read Literature
Collect Datasets
Analyze Archives Coverage
Analyze Shortened URIs
Prototype Application
Analyze Shared Resources Persistence and Coverage
Analyze Contextual Intention
Create Intention-based dataset
Extract Intention Features
Train a Parametric Model to predict intention
Evaluate, test, cross-validate the model
Create a mockup application
Extend the model to induce preservation
Finish Writing the Dissertation
PhD Defense

28. Estimating Web Archiving Coverage
• Goal: Estimate how much of the public web is present in the public archives
and how many copies are available?
• Action:
– Getting 4 different datasets from 4 different sources:
• Search Engines Indices
• Bit.ly
• DMOZ
• Delicious.
• Results: *
• Publications:
– How much of the web is archived? JCDL '11
* Table Courtesy of Ahmed AlSum JCDL 2011

29. Dissertation Plan
BEGIN
Read Literature
Collect Datasets
Analyze Archives Coverage
Analyze Shortened URIs
Prototype Application
Analyze Shared Resources Persistence and Coverage
Analyze Contextual Intention
Create Intention-based dataset
Extract Intention Features
Train a Parametric Model to predict intention
Evaluate, test, cross-validate the model
Create a mockup application
Extend the model to induce preservation
Finish Writing the Dissertation
PhD Defense

30. Shortened URI analysis
• Goal: Have a better understanding of URI shortening and resolving,
understand the effect of time on this process and the correlation between
the page’s features and characteristics, and its resolution.
• Action:
– Fresh Bit.lys
– Get hourly clicklogs, rate of change, social networking spread, and other
contextual information
– Longitudinal study
• Evaluation:
– Compare results with frequency of change analysis of Cho and Garcia-
Molina.
– Compare results with Antoniades et al. WWW 2011.

31. Dissertation Plan
BEGIN
Read Literature
Collect Datasets
Analyze Archives Coverage
Analyze Shortened URIs
Prototype Application
Analyze Shared Resources Persistence and Coverage
Analyze Contextual Intention
Create Intention-based dataset
Extract Intention Features
Train a Parametric Model to predict intention
Evaluate, test, cross-validate the model
Create a mockup application
Extend the model to induce preservation
Finish Writing the Dissertation
PhD Defense

32. Estimating Loss of Shared Resources
in Social Media
• Goal: Estimate how much of the public web is present in the public archives
and how many copies are available?
• Action:
– Sampling from 6 public events
– Events spanning 3 years
– Existence in the current web
– Existence in the public archives
– Find relation with time
• Results:
– After 1st year ~11% will be lost
– After that we will continue on losing 0.02% daily
• Publications:
– A year after the Egyptian revolution, 10% of the social media documentation is gone.
http://ws-dl.blogspot.com/2012/02/2012-02-11-losing-my-revolution-year.html
– Losing my revolution: How Many Resources Shared on Social Media Have Been Lost?
TPDL '12

33. Dissertation Plan
BEGIN
Read Literature
Collect Datasets
Analyze Archives Coverage
Analyze Shortened URIs
Prototype Application
Analyze Shared Resources Persistence and Coverage
User Intention Analysis
Create Intention-based dataset
Extract Intention Features
Train a Parametric Model to predict intention
Evaluate, test, cross-validate the model
Create a mockup application
Extend the model to induce preservation
Finish Writing the Dissertation
PhD Defense

34. User Intention Analysis
• Goal: Have a better understanding of User Intention and what factors affect
it. Also create a new testing and training set.
• Action:
– Get a sample set of tweets selected at random
– Extract the URIs
– Get closest Memento
– Download the snapshot & current version
– Use Amazon’s Mechanical Turk in choosing the best version
• Evaluation:
– Measure cross-rater agreement and confidence.

35. Proposed Work
• Data Gathering
• Feature Extraction
• Modeling the intention engine
• Evaluation
• Application: Prediction and Preservation

36. Possible Solution for Jenny

37. Possible Solution for Jenny
The resource has changed since last time it was shared
Do you wish to see the version the author intended or
the current version?
Current Version Intended Version

38. Proposed Framework
Archived Version
Feature
Classifier
Extraction
Example Features: Current Version
- Tweet Content
- Click Logs
- Other Tweets
- Shared Resource
- Timemaps

40. Extra Slides

41. Archive Shortener Application

42. Estimating Shared Resources Loss in Social Media

43. Estimating Shared Resources Loss in Social Media

44. My Publications
• S. G. Ainsworth, A. Alsum, H. SalahEldeen, M. C. Weigle, and M. L. Nelson. How
much of the web is archived? In Proceedings of the 11th annual international
ACM/IEEE joint conference on Digital libraries, JCDL '11, pages 133{136, 2011.
• H. SalahEldeen and M. L. Nelson. Losing my revolution: How much social media
content has been lost? Accepted in TPDL 2012
• H. SalahEldeen and M. L. Nelson. Losing my revolution: A year after the Egyptian
revolution, 10% of the social media documentation is gone. http://ws-
dl.blogspot.com/2012/02/2012-02-11-losing-my-revolution-year.html.

45. References
• D. Antoniades, I. Polakis, G. Kontaxis, E. Athanasopoulos, S. Ioannidis, E. P. Markatos, and T. Karagiannis. we.b: the web of short
urls. In Proceedings of the 20th international conference on World wide web, WWW '11, pages 715 {724, New York, NY, USA,
2011. ACM.
• A. Ashkan, C. L. Clarke, E. Agichtein, and Q. Guo. Classifying and characterizing query intent. In Proceedings of the 31th
European Conference on IR Research on Advances in Information Retrieval, ECIR '09, pages 578{586, Berlin, Heidelberg, 2009.
Springer-Verlag.
• L. Azzopardi and M. de Rijke. Query intention acquisition: A case study on automatically inferring structured queries. In
Proceedings DIR-2006, 2006.
• R. Baeza-Yates, L. Calderon-Benavides, and C. Gonzalez-Caro. The intention behind web queries. In F. Crestani, P. Ferragina, and
M. Sanderson, editors, String Processing and Information Retrieval, volume 4209 of Lecture Notes in Computer Science, pages
98{109. Springer Berlin / Heidelberg, 2006. 10.1007/11880561 9.
• A. Benczur, I. Bro, K. Csalogany, and T. Sarlos. Web spam detection via commercial intent analysis. In Proceedings of the 3rd
international workshop on Adversarial information retrieval on the web, AIRWeb '07, pages 89{92, New York, NY, USA, 2007.
ACM.
• J. Bollen, H. Mao, and X.-J. Zeng. Twitter mood predicts the stock market. CoRR, abs/1010.3003, 2010.
• N. Dai, X. Qi, and B. D. Davison. Bridging link and query intent to enhance web search. In Proceedings of the 22nd ACM
conference on Hypertext and hypermedia, HT '11, pages 17{26, New York, NY, USA, 2011. ACM.
• N. Dai, X. Qi, and B. D. Davison. Enhancing web search with entity intent. In Proceedings of the 20 th international conference
companion on World wide web, WWW '11, pages 29{30, New York, NY, USA, 2011. ACM.
• K. Durant and M. Smith. Predicting the political sentiment of web log posts using supervised machine learning techniques
coupled with feature selection. In O. Nasraoui, M. Spiliopoulou, J. Srivastava, B. Mobasher, and B. Masand, editors, Advances in
Web Mining and Web Usage Analysis, volume 4811 of Lecture Notes in Computer Science, pages 187{206. Springer Berlin /
Heidelberg, 2007. 10.1007/978-3-540-77485-3 11.

46. References
• Q. Guo and E. Agichtein. Ready to buy or just browsing?: detecting web searcher goals from interaction data. In Proceedings of the 33rd
international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 130{137, New York, NY, USA,
2010. ACM.
• A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th
WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, WebKDD/SNA-KDD '07, pages 56{65, New York, NY,
USA, 2007. ACM.
• H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international
conference on World wide web, WWW '10, pages 591{600, New York, NY, USA, 2010. ACM.
• C.-H. L. Lee and A. Liu. Modeling the query intention with goals. In Proceedings of the 19th International Conference on Advanced
Information Networking and Applications - Volume 2, AINA '05, pages 535{540, Washington, DC, USA, 2005. IEEE Computer Society.
• A. Loser, W. M. Barczynski, and F. Brauer. What's the intention behind your query? a few observations from a large developer community.
In IRSW, 2008.
• F. McCown, N. Diawara, and M. L. Nelson. Factors aecting website reconstruction from the web infrastructure. In JCDL '07: Proceedings of
the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pages 39{48, 2007.
• B. Meeder, B. Karrer, A. Sayedi, R. Ravi, C. Borgs, and J. Chayes. We know who you followed last summer: inferring social link creation times
in twitter. In Proceedings of the 20th international conference on World wide web, WWW '11, pages 517{526, New York, NY, USA, 2011.
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• G. Mishne. Predicting movie sales from blogger sentiment. In In AAAI 2006 Spring Symposium on Computational Approaches to Analysing
Weblogs (AAAI-CAAW), 2006.
• M. L. Nelson and B. D. Allen. Object persistence and availability in digital libraries. D-Lib Magazine, 8(1), 2002.
• R. Sanderson, M. Phillips, and H. Van de Sompel. Analyzing the persistence of referenced web resources with memento. CoRR,
abs/1105.3459, 2011.
• H. Van de Sompel, M. L. Nelson, R. Sanderson, L. Balakireva, S. Ainsworth, and H. Shankar. Memento: Time travel for the web. CoRR,
abs/0911.1112, 2009.
• S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on twitter. In Proceedings of the 20th international conference
on World wide web, WWW '11, pages 705{714, New York, NY, USA, 2011. ACM.