A talk I gave at Old Dominion University to new students from PES University in Bangalore

1. Old Dominion University
Department of Computer Science
Hany SalahEldeen
Hany SalahEldeen Khalil hany@cs.odu.edu
Zen & the Art of Data Mining
07-08-14
Social Media Data Collection and the path to
Modeling & Predicting User Intention
Web Science & Digital Libraries Lab
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2. Before we start..
here is a lil bit about me…
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3. Hany SalahELdeen
Education:
• PhD Candidate
• Web Science and Digital Libraries Group
• Masters Degree in Computer Vision and Artificial Intelligence
• Universitat Autonoma de Barcelona
• Bachelors of Computer Systems Engineering
• University of Alexandria
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4. Research & Technical Experience
• Microsoft Research Cairo
• Google GmBH Zurich
• Microsoft Inc. Mountain View
• National University of Singapore
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5. Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
Web Mining Pattern Analysis Machine Learning
Human Behavioral Analysis
Social Media Analysis
So what am I investigating?
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6. Publications
Hany SalahEldeen
Shanghai CIKM 2014 Conference
- 1 first author paper
- 1 second author paper
London DL 2014 Conference
- 1 third author paper
Malta TPDL 2013 Conference
- 1 first author paper
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7. Publications
Hany SalahEldeen
Indianapolis JCDL 2013 Conference
- 1 first author paper
Rio de Janeiro WWW 2013 Conference
- 1 first author paper
Cyprus TPDL 2012 Conference
- 1 first author paper
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8. Beside the perks of
travelling, our research has
been popular…
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9. MIT Technology Review
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10. MIT Technology Review
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11. MIT Technology Review
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12. Mashable
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13. Popular Mechanics
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14. BBC
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15. The Virginian Pilot
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16. Our Research’s Popularity
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• Local newspaper: The Virginia Pilot
• 4 x MIT Technology Review
• BBC
• Mashable
• The Atlantic
• Yahoo News
• Articles in > 11 different languages
• We have been called:
• The Internet Archeologists
• Web Time Travelers
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17. My goal:
Detect, model, and predict
user intention in social media
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18. Ok hold on, let’s go back to
the basics…
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19. Web 2.0
Definition: Web 2.0 is a concept that
takes the network as a platform for
information sharing, interoperability,
user-centered design, and collaboration
on the World Wide Web.*
* http://en.wikipedia.org/wiki/Web_2.0
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20. Web 2.0
• Yes, Web 2.0 is about “user-generated
content”
• But explicit content contributed by
users is just 20% of what “matters”
• 80% is in the implicitly contributed
data*
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*Toby Segaran, Programming Collective Intelligence, 2007

21. Systems & Web 2.0
• Google: Utilizes PageRank which is a
technique for extracting intelligence from
the link structure
• Flickr: Utilizes “interestingness” algorithm
• Amazon: Utilizes “people who bought this
product also bought” feature
• Pandora: Utilizes “similar artist radio”
• eBay: Utilizes “reputation system”
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22. So why do we even care
about all that?
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23. Power to the People!
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24. Power to the People!
• Because analyzing a huge dataset of
millions of users will yield a lot of potential
insights into:
• User Experience
• Marketing
• Personal Taste
• Human Behavior in general.
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25. So what is Data Mining?
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26. Data Mining
• Definition: It is the computational process of
discovering patterns in large data
sets involving methods at the intersection
of artificial intelligence, machine
learning, statistics, and database systems. The
overall goal of the data mining process is to
extract information from a data set and
transform it into an understandable structure
for further use.
http://en.wikipedia.org/wiki/Data_mining
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27. Back to my goal:
Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
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28. Let’s breakdown the title first…
Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
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29. Let’s breakdown the title first…
Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
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30. Scenario 1:
Jenny reading Jeff’s tweets
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31. Michael Jackson Dies
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Snapshot on: June 25th 2009
http://web.archive.org/web/20090625232522/http://www.cnn.com/
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32. Jeff tweets about it…
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Published on: June 25th 2009
https://twitter.com/mdnitehk/status/2333993907
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33. Jeff’s friend Jenny was on a vacation in Hawaii for a
month
Jenny is off the grid…
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34. When she came back she checked Jeff’s tweets and
was shocked!
Jenny starts catching up a month later
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Read on: July26th 2009
https://twitter.com/mdnitehk/status/2333993907
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35. She quickly clicked on the link in the tweet…
Jenny follows the link on July 26th
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http://web.archive.org/web/20090726234411/http://www.cnn.com/
CNN page on:
July 26th 2009
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36. • 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.
Jenny is confused!
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37. Scenario 2:
The Egyptian Revolution
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38. The Egyptian Revolution Jan 2011
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39. Reading about it in Storify.com a year
later in March 2012
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http://storify.com/maq4sure/egypts-revolution
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40. I noticed some shared images are missing
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http://storify.com/maq4sure/egypts-revolution
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41. Some tweets are still intact
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https://twitter.com/miss_amy_qb/status/32477898581483521
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42. …and some lost their meaning with
the disappearance of the images
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Missing ?
https://twitter.com/aishes/status/32485352102952960
https://twitter.com/omar_chaaban/status/32203697597452289
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43. The tweet remains but the shared
image disappeared…
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http://yfrog.com/h5923xrvbqqvgzj
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44. • 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.
Cairo….we have a problem!
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45. …back to the title
Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
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46. …back to the title
Hany SalahEldeen
Detecting, Modeling, & Predicting
User Temporal Intention
in Social Media
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The Anatomy of a Tweet
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The Anatomy of a Tweet
Author’s username
Other user mention
Tweet Body
Hash TagShortened URL
to resource
Publishing
timestamp
Social
Post
Shared Resource
Interaction
options
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3 URIs = 3 Chances to fail
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http://news.blogs.cnn.com/2012/04/26/norwegian
s-sing-to-annoy-mass-killer/
https://twitter.com/KentEiler/status/19553574
9754527745
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…
t1
t4
t2
t3 t5
t7 t8 t9 tn
t6
Explanation in MJ’s example
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If I click on a link in a tweet, which
version should I get?
ttweet or tclick ?
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Sometimes you want a
previous version
The Correct Temporal
Intention
CNN.com at the closest time to the tweet: 25th June 2009 ~ 7pm
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Sometimes you want the
current version
The Correct Temporal
Intention
In this case the current state of the press releases page
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Research Question
Can we estimate the users’
intention at the time of posting
and reading to predict and
maintain temporal consistency?
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People rely on social media for most
updated information
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56. Hany SalahEldeen
So if you are posting a tweet about
your cat…
…No one cares!
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57. Hany SalahEldeen
Regardless how cool your cat was!
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58. All tweets are equal…
…but some are more equal than the others
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59. Preliminary Research Questions:
1. How long would these last?
2. And if lost, are they archived?
3. Is this what the author intended?
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Since tweets are considered the first draft
of history… the historical integrity of the
tweets could be compromised.
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Historical Integrity
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The life cycle of a social post
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The life cycle of a social post
tweets
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The life cycle of a social post
tweets Links to
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The life cycle of a social post
tweets
What the
reader
receives
Links to
Same state
the author
intended
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The life cycle of a social post
tweets
What the
reader
receives
Links to
Same state
the author
intended
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The resource
has disappeared
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The life cycle of a social post
tweets
What the
reader
receives
Links to
Same state
the author
intended
The resource
has disappeared
The resource
has changed
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Same state
the author
intended
The Resource’s Possibilities
a bigger problem since the reader might not know.
What the
reader
receives
The resource
has disappeared
The resource
has changed
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We could lose the linked resource
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The attack on the embassy was in February
2013
Or the resource could change
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Why do we want to detect the
Author’s Temporal Intention?
• Match: and convey the intended information.
• Notify:
– the author that the resource is prone to change.
– the reader that the resource has changed.
• Preserve: the resource by pushing snapshots into the
archive automatically.
• Retrieve: the closest archived version to maintain the
consistency.
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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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
– http://ws-dl.blogspot.com/2011/06/2011-06-23-how-much-of-web-is-
archived.html
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16%-79% Archived according
to the source
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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The timeline of the resource
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http://ws-dl.blogspot.com/2013/04/2013-04-19-carbon-dating-web.html

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Timestamps Accumulation
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Actual Vs. Estimated Dates
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• Successfully estimated the creation date
>75% of the resources
• >33% we estimated the exact date
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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79. • From Twitter, Websites, Books:
• The Egyptian revolution
• From Twitter Only:
• Stanford’s SNAP dataset:
• Iranian elections
• H1N1 virus outbreak
• Michael Jackson’s death
• Obama’s Nobel Peace Prize
• Twitter API:
• The Syrian uprising
Six Socially Significant Events
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80. Resources Missing & Archived
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81. Revisiting after a year…
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• There is a nearly linear relationship between the amount
missing from the web and time.
• After 1 year ~11% is gone, and 0.02% is lost every day
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82. Measured Vs. Predicted
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83. First Attempts to Shared Content
Replacement
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• We performed an experiment to gauge how many
of the resources that are missing could be
replaced with other similar resources.
• Collected a dataset with available resources
which we assumed to be missing
• Used our method to extract the replacement
resources
• Measured the similarity with the original resource

84. First Attempts to Shared Content
Replacement
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We were able to extract another resource with >70% similarity
to the missing resource in >40% of the cases
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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Temporal Intention Relevancy Model
(TIRM)
Between ttweet and tclick:
The linked resource could have:
• Changed
• Not changed
The tweet and the linked resource could be:
• Still relevant
• No longer relevant
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Resource is changed but relevant
• The resource changed
• But it is still relevant
 Intention: need the current version of the resource at any time
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Relevancy and Intention Mapping
Current
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Resource is changed and not relevant
 Intention: need the past version of the resource at any time
• The resource changed
• But it is no longer relevant
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Past
Relevancy and Intention Mapping
Current
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Resource is not changed and relevant
 Intention: need the past version of the resource at any time
• The resource is not changed
• And it is relevant
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Past
Relevancy and Intention Mapping
Current
Past
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Resource is not changed and not relevant
 Intention: I am not sure which version of the resource I need
• The resource is not changed
• But it is not relevant
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Past
Relevancy and Intention Mapping
Current
Past Not Sure
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Our investigation angles
1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
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Feature extraction
• For each tweet we perform:
– Link analysis
– Social Media Mining
– Archival Existence
– Sentiment Analysis
– Content Similarity
– Entity Identification
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1- Link analysis
• Since the tweets have embedded resources shortened by
Bit.ly we can extract:
– Total number of clicks
– Hourly click logs
– Creation dates
– Referring websites
– Referring countries
• We calculate the depth of the resource in relation to its domain
(either it is a leaf node or a root page)
– We calculated the number of backslashes in the resource’s URI
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2- Social Media Mining
• Twitter:
– Using Topsy.com’s API to
extract:
• Total number of tweets.
• The most recent 500.
• Number of tweets by
influential users.
The collection of tweets extracted provided an extended context of the
resource authored by users in the twittersphere.
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2- Social Media Mining
• Facebook:
– Mined too for likes, shares, posts, and clicks related to each
resource.
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3- Archival Existence
• Using Memento Time
Maps we get:
– Total mementos
available
– Different archives count.
– The closest archived
version to the tweet
time.
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4- Sentiment Analysis
• Using NLTK libraries of natural language text processing
• Extract the most prominent sentiment in the text
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5- Content Similarity
• Steps:
– We download the content HTML using Lynx browser.
– We apply boilerplate removal algorithm and full text extraction.
– Calculate the cosine similarity between the two pages.
 70% similarity 
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6- Entity Identification
• By visual inspection we observed that the majority of tweets about
celebrities are related to current events.
• We harvested Wikipedia for lists of actors, politicians, and athletes.
• Checked the existence of a celebrity mention in the tweets.
Actor: Johnny Depp
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The trained classifier
• From the feature extraction phase we extracted 39
different features to train the classifier.
• Using 10-fold cross validation, the Cost Sensitive Classifier
Based on Random Forests gave the highest success rate =
90.32%
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What’s Next for Hany?
• Finish up my dissertation
• Defend.
• Get a research/Data scientist position
• Interests:
– L3S Research Center Germany
– Microsoft Research
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1. The state of the archived content
2. The age of the shared resource
3. The states of the resource:
1. Missing from the live web
2. Changed from what the author intended to share
4. Detect the author’s intention and collect a dataset
5. Model this intention
6. Create a time-based navigation tool to match the
predicted intention
Hany SalahEldeen
Summary:
Email: hany@cs.odu.edu
Office: 3102
Website: http://www.cs.odu.edu/~hany/
Twitter: @hanysalaheldeen
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