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Old Dominion UniversityDepartment 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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Before we start..here is a lil bit about me…

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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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Research & Technical Experience• Microsoft Research Cairo• Google GmBH Zurich • Microsoft Inc. Mountain View• National University of Singapore

Hany SalahEldeen

5Hany SalahEldeen

Detecting, Modeling, & Predicting User Temporal Intention

in Social Media

Web Mining Pattern Analysis Machine Learning

Human Behavioral AnalysisSocial Media Analysis

So what am I investigating?

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Publications

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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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Publications

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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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Beside the perks of travelling, our research has been popular…

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MIT Technology Review

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MIT Technology Review

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MIT Technology Review

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Mashable

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Popular Mechanics

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BBC

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The Virginian Pilot

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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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My goal:Detect, model, and predict

user intention in social media

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Ok hold on, let’s go back to the basics…

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Web 2.0Definition: 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_2.0Hany SalahEldeen

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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

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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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So why do we even care about all that?

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Power to the People!

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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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So what is Data Mining?

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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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Back to my goal:

Hany SalahEldeen

Detecting, Modeling, & Predicting User Temporal Intention

in Social Media

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Let’s breakdown the title first…

Hany SalahEldeen

Detecting, Modeling, & Predicting User Temporal Intention

in Social Media

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Let’s breakdown the title first…

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Detecting, Modeling, & Predicting User Temporal Intention

in Social Media

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Scenario 1:Jenny reading Jeff’s tweets

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Michael Jackson Dies

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

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Jeff tweets about it…

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Published on: June 25th 2009https://twitter.com/mdnitehk/status/2333993907

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Jeff’s friend Jenny was on a vacation in Hawaii for a month

Jenny is off the grid…

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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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She quickly clicked on the link in the tweet…

Jenny follows the link on July 26th

Hany SalahEldeenhttp://web.archive.org/web/20090726234411/http://www.cnn.com/

CNN page on: July 26th 2009

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• 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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Scenario 2:The Egyptian Revolution

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The Egyptian Revolution Jan 2011

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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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I noticed some shared images are missing

Hany SalahEldeenhttp://storify.com/maq4sure/egypts-revolution

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Some tweets are still intact

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https://twitter.com/miss_amy_qb/status/32477898581483521

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…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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The tweet remains but the shared image disappeared…

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http://yfrog.com/h5923xrvbqqvgzj

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• 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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…back to the title

Hany SalahEldeen

Detecting, Modeling, & Predicting User Temporal Intention

in Social Media

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…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 TweetAuthor’s username

Other user mention

Tweet Body

Hash TagShortened URL to resource

Publishing timestamp

SocialPost

Shared Resource

Interactionoptions

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3 URIs = 3 Chances to fail

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http://news.blogs.cnn.com/2012/04/26/norwegians-sing-to-annoy-mass-killer/

https://twitter.com/KentEiler/status/195535749754527745

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…t1

t4

t2

t3 t5t7 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 ~ 7pmHany SalahEldeen

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Sometimes you want the current version

The Correct Temporal Intention

In this case the current state of the press releases pageHany SalahEldeen

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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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So if you are posting a tweet about your cat…

…No one cares!

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Regardless how cool your cat was!

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All tweets are equal…

…but some are more equal than the others

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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 content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. Create a time-based navigation tool to match the predicted

intention

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Our investigation angles

1. The state of the archived content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. 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.htmlHany SalahEldeen

16%-79% Archived according to the source

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Our investigation angles

1. The state of the archived content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. 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 content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. Create a time-based navigation tool to match the predicted

intention

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• 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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Resources Missing & Archived

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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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Measured Vs. Predicted

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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

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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 content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. 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 content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. 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 content2. The age of the shared resource 3. The states of the resource:

1. Missing from the live web2. Changed from what the author intended to share

4. Detect the author’s intention and collect a dataset5. Model this intention6. Create a time-based navigation tool to match the predicted

intention

Hany SalahEldeen

Summary:

Email: hany@cs.odu.eduOffice: 3102Website: http://www.cs.odu.edu/~hany/Twitter: @hanysalaheldeen