Doctoral Defense: Hany SalahEldeen

2015 Hany SalahEldeen Dissertation Defense 1

Detecting, Modeling, & Predicting User Temporal Intention in Social Media

Hany M. SalahEldeenDoctor of PhilosophyDissertation Defense

Old Dominion UniversityDepartment of Computer Science

Advisor: Dr. Michael L. Nelson

Dr. Michele C. WeigleDr. Hussein M. Abdel-WahabDr. M’Hammed Abdous

Committee:

May 5th, 2015


All tweets are equal…

…but some are more equal than the others


It is imperative to know…

1. How long would these last?2. And if lost, is there a backup somewhere?3. Is this what the author intended?


To maintain historical integrity

Since tweets are considered the first draft of history… the historical integrity of the tweets could be compromised.


Motivation

Background

Related Research

Research Question

User-Time-Shared Resource

Conclusions


People rely on social media for most updated information


Social media is more than kitty photos

Marie ColvinJanuary 12, 1956 – February 22, 2012

Rémi Ochlik16 October 1983 – 22 February 2012

Ahmed Assem1987 – July 8, 2013


For the web is dark, and full of missing content…

Accessed in July 2014

3 out 8 external links on Remi’sWikipedia page return 404


even for content shared in social media



News sites are also prone to change



So are specialized sites



Research Problem:Author’s Intention ≠ Reader’s Experience


Research ImplicationAuthor’s Intention ≠ Reader’s Experience

Broken Inconsistent Weband Historical Records


Motivation

Background

Related Research

Research Question


Conclusions


Social Post


The anatomy of a tweet

Author’s username

Other user mention

Tweet Body

Hash TagShortened URL to resource

Publishing timestamp

SocialPost

Shared Resource

Interactionoptions


3 URIs = 3 Chances to fail


URL shortening and aliasing

curl -L -I http://bit.ly/losing_revolution

HTTP/1.1 301 Moved Permanently

Server: nginx

Date: Mon, 07 Jul 2014 18:19:48 GMT

Cache-Control: private; max-age=90

Location:

http://ws-dl.blogspot.com/2012/02/2012-02-11-

losing-my-revolution-year.html

Mime-Version: 1.0

Set-Cookie: _bit=53bae4c4-00328-04f10-

cb1cf10a;domain=.bit.ly;expires=Sat Jan 3

18:19:48 2015;path=/; HttpOnly

Content-Type: text/html;charset=utf-8Content-Length: 167

HTTP/1.1 200 OK

Expires: Mon, 07 Jul 2014 18:19:52 GMT

Date: Mon, 07 Jul 2014 18:19:52 GMT

Cache-Control: private, max-age=0

Last-Modified: Mon, 07 Jul 2014 18:19:07

GMT

ETag: "e3555826-b103-4daa-a3f2-

d0509ebab51f"

X-Content-Type-Options: nosniff

X-XSS-Protection: 1; mode=block

Server: GSE

Alternate-Protocol: 80:quic

Content-Type: text/html;charset=UTF-8Content-Length: 0

http://bit.ly/losing_revolution


Life cycle of a social post



tweets



tweets Links to



tweets

What the reader

receives

Links to

Same state the author intended



tweets

What the reader

receives

Links to


Ideally!



tweets

What the reader

receives

Links to


After a period of time



tweets

What the reader

receives

Links to


The resource has disappeared




tweets

What the reader

receives

Links to


The resource has disappeared

The resource has changed



Memento framework

* http://mementoweb.org/guide/rfc/

http://mementoweb.org/guide/rfc/


Motivation

Background

Related Research

Research Question


Conclusions


Related Work

• Social media analysis:• Understanding Microblogging

• Zhao 2009• Yang 2010• Newman 2003• Kwak 2010• Java 2007• Cha 2009

• History Narration• Vieweg 2010• Starbird 2010-2012• Qu 2011• Neubig 2011• Lehman and Lalmas 2012-

2013

• User’s Web Search Intention• Ashkan 2009

• Lee 2005

• Loser 2008

• Azzopardi 2009

• Baeza-Yates 2006

• Dai 2011

• Commercial Intention• Guo 2010

• Benczur 2007

• Sentiment Analysis• Mishne 2006

• Bollen 2011

• Access to Archives• Van de Sompel 2009

• Persistence of shared resources– Nelson 2002

– Sanderson 2011

– McCown 2007

• URL Shortening– Antoniades 2011

• Tweeting, Micro-blogging and Popularity– Wu 2011

– Java 2007

– Kwak 2010

• Social Networks Growth and Evolution– Meeder 2011

Further details: refer to chapter 3


Motivation

Background

Related Research

Research Question


Conclusions


Research Question:Can we estimate the users’

intention at the time of posting and reading to predict and

maintain temporal consistency?


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 and provide a smooth temporal navigation of the social web.






Motivation

Background

Related Research

Research Question


Conclusions


Shared Resource Time User

Our analysis covers three angles



Loss and Persistence of Shared Resources



Alive

First: Estimate social media content loss


Six socially significant events

Event Source Year

Iranian Election SNAP Dataset 2009

H1N1 Virus Outbreak SNAP Dataset 2009

Michael Jackson’s Death SNAP Dataset 2009

Obama’s Nobel Peace Prize SNAP Dataset 2009

The Egyptian Revolution Twitter, Websites, Books 2011

The Syrian Uprising Twitter API 2012


Twitter tag expansion and filtration


Twitter tag expansion increases precision


What are people sharing?


Existence on the live web and in the archives

• For each unique URL we resolved the final HTTP response and considered 2 classes:• Success: 200 OK• Failure: 4XX, 50X families and the 30X loop redirects or soft 404s.

• Utilize the memento aggregator:• Archived: if it has at least one memento in the timemap


Resources Missing and Archived

Collection Percentage Missing Percentage Archived

23.49%H1N1 Outbreak 41.65%

36.24%Michael Jackson 39.45%

26.98%Iran 43.08%

24.59%Obama 47.87%

10.48%Egypt 20.18%

7.04%Syria 5.35%

31.62% 30.78%

24.47% 36.26%

25.64% 43.87%

26.15% 46.15%



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Second: Can we measure existence and disappearance as a function of time?


Resources Missing and Archived


23.49%H1N1 Outbreak 41.65%


26.98%Iran 43.08%

24.59%Obama 47.87%

10.48%Egypt 20.18%

7.04%Syria 5.35%

31.62% 30.78%

24.47% 36.26%

25.64% 43.87%

26.15% 46.15%


Timeline of Events


Social Events Having a Bimodal Time Distribution


Timeline of Events


Social Events Having a Bimodal Time Distribution


Existence as a function of time


• Results:

• Publications and Articles:1. H. M. SalahEldeen. 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 , 2012.

2. H. M. SalahEldeen and M. L. Nelson. Losing my revolution: how many resources shared on social media have been lost? In Proceedings of the Second international conference on Theory and Practice of Digital Libraries, TPDL'12, 2012.

Conclusion: Existence could be estimated as a function of time

• Measured 21,625 resources from 6 data sets in archives & live web.

• After a year from publishing about 11% of content shared on social media will be gone.

• After this we are losing roughly 0.02% daily.

http://ws-dl.blogspot.com/2012/02/2012-02-11-losing-my-revolution-year.html


Revisiting Existence after a year

MJ Iran H1N1 Obama Egypt Syria

Measured 37.10% 37.50% 28.17% 30.56% 26.29% 31.62% 32.47% 24.64% 7.55% 12.68%Predicted 31.72% 31.42% 31.96% 30.98% 30.16% 29.68% 29.60% 28.36% 19.80% 11.54%

Error 5.38% 6.08% 3.79% 0.42% 3.87% 1.94% 2.87% 3.72% 12.25% 1.14%

MJ Iran H1N1 Obama Egypt SyriaMeasured 48.61% 40.32% 60.80% 55.04% 47.97% 52.14% 48.38% 40.58% 23.73% 0.56%Predicted 61.78% 61.18% 62.26% 60.30% 58.66% 57.70% 57.54% 55.06% 37.94% 21.42%Error 13.17% 20.86% 1.46% 5.26% 10.69% 5.56% 9.16% 14.48% 14.21% 20.86%

Average Prediction Error = 11.57%

in all cases, our archival predictions were too optimistic

Missing

Archived

Average Prediction Error = 4.15%

in all cases, our missing predictions were acceptable



Alive

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Replaced

Third: Can we use social context to find replacements of missing resources?


Context discovery and shared resource replacement

Problem:

140 characters limits the description of the linked resource. If it went missing, can we get the next best thing?

Solution:

• Shared links typically have several tweets, responses, and retweets

• We can mine these traces for context and viable replacements


Context Discovery

Linking to: http://beta.18daysinegypt.com/

http://beta.18daysinegypt.com/


What if the resource disappeared?

Linking to: http://beta.18daysinegypt.com/

http://beta.18daysinegypt.com/


Use Topsy to discover tweets sharing the same link


Social Context Extraction{

"URI": "http://beta.18daysinegypt.com/",

"Related Tweet Count": 500,

"Related Hashtags": "#tran #citizensx #arabspring #visualstorytelling

#collaborativerevolution #feb11http://t.co/qxusp70 ...",

"Users who talked about this": "@petra_stienen: @waleedrashed:

@omarsamra @ungormite: @dcisbusy @webdocumentario: ...",

"All associated unique links:": "http://t.co/63X1f3f1

http://t.co/reBh6c4V http://t.co/B3GuhQN4 http://t.co/X2sjf4Rf

http://t.co/P9iR28fH http://t.co/1C4EPh8h ...",

"All other links associated:": "http://vimeo.com/35368376

http://mashable.com/2012/01/21/18daysinegypt-2/ ",

"Most frequent link appearing:": "http://t.co/2ke0rEjP",

"Number of times the Most frequent link appearing:": 49,

"Most frequent tweet posted and reposted:": "Check out 18DaysInEgypt -

A crowd sourced documentary project ================= via

@18daysinegypt",

"Number of times the Most frequent tweet appearing:": 46,

"The longest common phrase appearing:": "RT 2ke0rEjP is an interactive

documentary website that YOU can help create Get your Jan25 stories

ready! Pl RT",

"Number of times the Most common phrase appearing:": 18

}


Build a Tweet Document

A tweet document represents the concatenation of all extracted tweets:

do you have a story to tell about your 18 days of revolution? share it or contact sara 18days brand new interactive storytelling project on egyptian revolution a very creative platform to tell your story daysinegypt marches heading to tahrir square now from all over cairoit's all over again use the website to document your revolutionary stories and share them with the world! check out awesome documentary project crowdsourcing a people's narrative of the egyptian revolution … ”

“


Tweet Signature

Tweet Document:

do you have a story to tell about your 18 days of revolution? share it or contact sara 18days brand new interactive storytelling project on egyptian revolution a very creative platform to tell your story daysinegypt marches heading to tahrir square now from all over cairoit's all over again use the website to document your revolutionary stories and share them with the world! check out awesome documentary project crowdsourcing a people's narrative of the egyptian revolution … ”

“

Tweet Signature = top 5 most frequent terms from Tweet Document

documentary project daysinegypt check sourced


Query Google with the Tweet Signature


Search Engine Results

The original resource


Search Engine Results

The original resource

The others are good replacement

candidates


Recommendation Evaluation

We extract a dataset of resources that are currently available:• Pretend these resources no longer exist (for a baseline)

• Each of the resources are textual based

• Each resource has at least 30 retrievable tweets.

Extracted 731 unique resources

We use boiler plate removal library to remove the template from the:• linked resources

• top 10 retrieved results from Google

We use cosine similarity to compare the documents


Similarity measures in resource replacement

----70% similarity----

41% of the cases we found a replacement with >=70% similarity


Conclusion: We can find viable replacements for missing shared resources

• Results:• 41% of the test cases we can find a replacement page with at least 70% similarity to the original

missing resource• The search results provide a mean reciprocal rank of 0.43

• Publications:1. H. SalahEldeen and M. L. Nelson. Resurrecting my revolution: Using social link

neighborhood in bringing context to the disappearing web. In Research and Advanced Technology for Digital Libraries- International Conference on Theory and Practice of Digital Libraries, TPDL 2013, 2013.


Now we finished analyzing the shared resource…what’s next?



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Footprints on the web


The tweet, the resource…and time

time

Posted a tweet

Read the tweetRelevancy of the resource to the tweet changed through time

we need to measure that

Another tweet posted

And another

…

We need to measure tweet relevance through time



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Rate of Change

Longitudinal Study: Rate of change of shared content


Pilot 1: Resource change in the first 80 hours after tweeting


Pilot 2: Delta days from Bitly creation for just tweeted content

Dataset size = 4,000


Pilot 3: Dataset of 1,000 freshly created Bitlys

http://www.cnn.com depth = 0

http://www.cnn.com/world depth = 1

http://www.cnn.com/2009/SHOWBIZ/Music/06/25/jackson depth = 6


What domains do users link to?


What categories* do users link to?

* Extracted from Alexa.com


Summation of Intention in Social Content Through Time

Longitudinal study: We record the change over an extended period of time:• Content: we download a snapshot of the resource every 45 minutes

• Metadata: we collect meta data about the resource• Facebook likes, posts• Tweets in the last hour• Bitly clicklogs and shares

• Average data size: ~1 TB per month


Hourly analysis over an extended period of time


There is a difference between ttweet and tclick

• After just one hour, 4% of the resources have changed by 30%.• After six hours, the percentage doubled to be 8% changed by 40%.• After a day the change rate slowed to be 12% of the resources

changed by 40%.• After that it almost stabilizes at 17% of the resources to be

changed by 40%.



Alive

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Rate of Change

Archive & Creation

First: Resource – Time – Public Archives


Revisited: Resources Missing and Archived


23.49%H1N1 Outbreak 41.65%


26.98%Iran 43.08%

24.59%Obama 47.87%

10.48%Egypt 20.18%

7.04%Syria 5.35%

31.62% 30.78%

24.47% 36.26%

25.64% 43.87%

26.15% 46.15%


But on a more general notion we want to know…


How much of the web is archived?

• 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.


Conclusion: It depends on the source

• Results:

• Publication: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, New York, NY, USA, 2011. ACM.


Conclusion: It depends on the source

• Results:

• Publication: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, New York, NY, USA, 2011. ACM.

Changes since 2011:

no more free SE APIs;

greatly reduced IA

quarantine period; 15

public web archives

2013

95%

92%

23%

26%


Side Experiment: Analyzing the quality of the archives and the archived content

• Goal:• Assessing the quality of the web archives• Better discussed in Justin Brunelle’s work

• Publications:1. J. F. Brunelle, M. Kelly, H. SalahEldeen, M. C. Weigle, and M. L. Nelson. Not All Mementos

Are Created Equal: Measuring The Impact Of Missing Resources. In Proceedings of the 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL), 2014 (Best student paper award)


A question emerged: When did a certain resource first appear on

the web?



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Rate of Change

Archive & Creation

Second: When was the resource created?


Idea

Web pages leave trails as well since the day they were created…


WebResource

Web trails

A web page could leave a trail of one of the following denoting its existence:

• References

• Links (anchors)

• Social media likes and interactions.

• URL shortening.

• Backlinks

• The creation date of any of the associated events/trails could be an estimate of the creation date.


Resource’s timeline


Observations Recorded

1.Last modified date from the response header.2.First Appearance of a backlink.3.First Tweet published.4.First Bitly Shortened URL created.5.Time stamp of first memento in the archives.6.Date of the last crawl by the search engine.


Carbon Date service


Carbon Dating API{

"self": "http://cd.cs.odu.edu/cd?url=http://www.cnn.com","URI": "http://www.cnn.com","Estimated Creation Date": "1998-12-06T04:02:33","Last Modified": "","Bitly.com": "2008-06-08T12:00:00","Topsy.com": "2015-01-25T23:31:42","Backlinks": "2003-03-12T05:35:44","Google.com": "2005-01-11T00:00:00","Archives": [

["Earliest","1998-12-06T04:02:33"

],[

"By_Archive",{

"http://archive.today/20000815052826/http://www.cnn.com/": "2000-08-15T05:28:26","http://arquivo.pt/wayback/wayback/20000815052826/http://www.cnn.com/": "2000-08-15T05:28:26","http://wayback.vefsafn.is/wayback/20011106102722/http://www.cnn.com/": "1998-12-06T04:02:33","http://web.archive.org/web/20131218180509/http://www.cnn.com/": "2013-12-18T18:05:09"

}]

]}


Evaluation Dataset

From each we randomly selected 100 unique URLs to create our gold standard dataset


Evaluation

• Applied our 6 methods on 1200 resources.

• Get leftmost estimate.

Number of Resources Percentage

An estimate found 910 76%

Exact matching estimate 393 33%

No estimate found 290 24%

Total Resources 1200 100%


Actual Vs. Estimated Dates


Conclusion: We can estimate the creation date of resources correctly

• Results:• Succeeded in estimating the creation date accurately in 75.90% of the resources.

• Publications:1. H. M. SalahEldeen and M. L. Nelson. Carbon dating the web: Estimating the age of web

resources. In Proceedings of the 22nd International Conference on World Wide Web Companion, TempWeb03, WWW '13, 2013


Alexander Nwala did an awesome job releasing the second version of Carbon Date which is more reliable, multithreaded, faster, can handle multiple requests, has caching capabilities.

http://cd.cs.odu.edu/

http://cd.cs.odu.edu/


Alexander Nwala did an awesome job releasing the second version of Carbon Date which is more reliable, multithreaded, faster, can handle multiple requests, has caching capabilities.

Yes, it’s better than mine… I admit it



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Replaced

Rate of Change

Archive & Creation

User’s Temporal Intention


Problem: There is an inconsistency between what the tweet’s author intended

to share at time ttweet

and what the reader might actually read upon clicking on the link at time tclick .



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Rate of Change

Archive & Creation

Detecting

What is Intention and how to detect it?


Amazon’s Mechanical Turk

• Crowdsourcing Internet marketplace

• Co-ordinates the use of human intelligence to perform tasks that computers are currently unable to do.*

* http://en.wikipedia.org/wiki/Amazon_Mechanical_Turk

http://en.wikipedia.org/wiki/Amazon_Mechanical_Turk


Goal: Understand and collect user intention data via MT

Tweets dataset Intention Classification Tasks User Intention Data

Classifier

Train


Goal: Understand and collect user intention data via MT

Tweets dataset Intention Classification Tasks User Intention Data

Classifier

Train

• Problem:• It is not as easy as it seems!


How NOT to classify temporal intention 101

• The tweet is presented along with the two snapshots:

at ttweet at tclick


And compared MT results with Experts

• Experts: Manually assigning a version to each tweet via a face to face meeting with WS-DL members.

• For 9 MT assignments per tweet:• If we allowed 4-5 splits we have 58% match with WS-DL.

• If we allowed 3-6 splits or better we got 31% match

Which is worse than flipping a coin!


Idea: We need to transform the problem from intention to relevance.


Relevance tasks are simpler

• MT workers are more accustomed to classification tasks and it requires minimum amount of explanation

• Transform a hard problem to an easy one

Is that a cat?

- Yes

- No


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


Resource is changed but relevant

• The resource changed• But it is still relevant

Intention: need the current version of the resource at any time


Relevancy and Intention mapping

Current


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



PastCurrent


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



PastCurrent

Past


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



PastCurrent

Past Not Sure


Validation: Update the MT experiment

• MT workers ≡ judgments of the experts (WS-DL members)

✓

Is the content still relevant to the tweet?


Mechanical Turk Workers Vs. Experts

• For 100 tweets, WS-DL members % of agreement:

• Cohen’s K = 0.854 almost perfect agreement

Agreement in 3-2 split or more votes 93%

Agreement in 4-1 split or more votes 80%

Agreement with 5-0 votes 60%



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Rate of Change

Archive & Creation

Detecting

Modeling

Can we model this temporal intention?


Data Collection

• From SNAP dataset we extracted:• Tweets in English

• Each has an embedded URI pointing to an external resource.

• The embedded URI is shortened via Bit.ly

• The external resource:• Still persists.

• Has at least 10 mementos.

• Is unique.

We extracted 5,937 unique instances


Time delta between the tweet and the closest memento

Randomly selected 1,124 instancesTime delta range: 3.07 minutes to 56.04 hours Average: 25.79 hours ~ 1 day

Tweet time

After Tweet time

Before Tweet time


Training Dataset

• Rcurrent: The state of the resource at current time.

• Rclick: The state of the resource at click time.

Relevant Assignments 929 82.65%

Non-Relevant Assignments 195 17.35%

5 MT workers agreeing (5-0 split) 589 52.40%


3 MT workers agreeing (3-2 close call split) 226 20.11%


Training Dataset

• Rcurrent: The state of the resource at current time.

• Rclick: The state of the resource at click time.

Relevant Assignments 929 82.65%

Non-Relevant Assignments 195 17.35%



3 MT workers agreeing (3-2 close call split) 226 20.11%


Intention modeling: Feature extraction

• For each tweet we perform:• Link analysis• Social media mining• Archival existence• Sentiment analysis• Content similarity• Entity identification


Training the 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%


Most significant features sorted by information gain

Rank Feature Gain Ratio

1 Existence of celebrities in tweets 0.149

2 Number of mementos 0.090

3 Tweet similarity with current page 0.071

4 Similarity: Current & past page 0.053

5 Similarity: Tweet & past page 0.044

6 Original URI’s depth 0.032


Testing the model

• We tested against:• The remaining 4,813 from the original 5,937 instances after extracting the 1,124 used

in training.

• The Tweet Collections based on historic events. (MJ, Obama, Iran, Syria, & H1N1)

Dataset Status 200 Status 404 or other Relevant % Non-Relevant %

Extended 4,813 instances 96.77% 3.23% 96.74% 3.26%

MJ’s Death 57.54% 42.46% 93.24% 6.76%

H1N1 Outbreak 8.96% 91.04% 97.48% 2.52%

Iran Elections 68.21% 31.79% 94.69% 5.31%

Obama’s Nobel Prize 62.86% 37.14% 93.89% 6.11%

Syrian Uprising 80.80% 19.20% 70.26% 29.75%


Idea: We need to transform the problem from intention to relevance.

Now we need to transform it back!

Recap…


Recap: Relevancy and Intention mapping

PastReading

the wrong history


Mapping TIRM

• We used 70% similarity as a threshold of relevancy.

Reading the wrong

historyIn up to

25% of the cases


Conclusion: We can model users’ temporal intention accurately and efficiently

• Results:• We successfully transformed the complicated problem of intention to a simpler one of relevance.• We successfully collected a gold standard dataset of temporal user intention.• We found a temporal inconsistency in the shared resource up to 25% of the cases according to the

dataset.

• Publications:1. H. M. SalahEldeen and M. L. Nelson. Reading the correct history?: Modeling temporal

intention in resource sharing. In Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '13, 2013.


So we modeled intention… can we make it better?


Enhancing TIRM

• Extending and tuning the features:• Linguistic feature analysis• Semantic similarity analysis using latent topic modeling• Dataset balancing• Feature selection and minimization


A whole lot of features!39 65 different features in extended TIRM



TIRM enhancement and minimization results


Point of Confusion: C

Point of Certainty: S

Strongest Current Intention

From binary to probabilistic strength



Intention strength formulation

Intention strength magnitude of the new resource:

Generalization in regards of class:


Intention strength across instances in dataset



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Rate of Change

Archive & Creation

Detecting

Modeling

Pre

dic

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Can we find a relation between the modeled intention and time

…to predict it?


Remember: Data Collection

• From SNAP dataset we extracted:• Tweets in English

• Each has an embedded URI pointing to an external resource.

• The embedded URI is shortened via Bit.ly

• The external resource:• Still persists.

• Has at least 10 mementos.

• Is unique.

We extracted 5,937 unique instances


Intention strength across time

time

Resource = Closest

memento

Resource = current versionWe have 10 mementos of the resource uniformly distributed

…

We can calculate intention strength at every point


Intention strength across time

Dataset collection and calculation framework


Behavior of instances in different classes

time

time

time

Inte

nti

on

str

engt

h

Inte

nti

on

str

engt

h

Inte

nti

on

str

engt

h

Steady Current Intention

Steady Past Intention


Behavior of instances in different classes


Given the features we already collected can we classify tweets

according to their behavioral class?


Classifying intention behavior across time


If we can limit the features to the ones that exist before tweet time

can we perform a prediction?


Classifying intention behavior across time

We can perform a prediction!


Intention behavior prediction classifier


Conclusion: We can predict the author’s temporal intention

• Results:• We can predict for the author whether the intention conveyed to the readers will be

consistent or will it change with 77% accuracy.

• Publications:1. H. M. SalahEldeen and M. L. Nelson. Predicting Temporal Intention in Resource Sharing. In

Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '15, 2015.


At this time, we successfully detected, modeled and predicted

User’s Temporal Intention in Shared Content



Alive

Mis

sin

g

Replaced

Rate of Change

Archive & Creation

Detecting

Modeling

Pre

dic

tin

g

Use

r Te

mp

ora

l In

ten

tio

n

Temporal Intention Model


So we built an awesome prediction model for Temporal

Intention… what next?


A Framework of Temporal Intention

time

Posted a tweet

Read the tweet

• Tools for authors• Enrich the archives with current content

for posterity


Prediction API


Tools for Authors


Temporal Intention Implementation

time

Posted a tweet

Read the tweet

• Tools for readers• Maintain the temporal consistence of

content


Tools for readers


Tools for readers

1. Temporal preservation of

vulnerable content

2. Version recommendation

based on temporal intention

estimation

Target Publication: Utilizing Temporal Intention

Prediction for Just-in-time Preservation and

Recommendation of Vulnerable Social Media

Content. WSDM 2016


Motivation

Background

Related Research

Research Question


Conclusions


Accomplished 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 and provide a smooth temporal navigation of the social web.






Also, our work reached fame…


The Virginian Pilot


http://www.bbc.com/future/story/20120927-the-decaying-web

BBC.com

http://www.bbc.com/future/story/20120927-the-decaying-web


Popular MechanicsFebruary 2014 issue, page 20


3 x MIT Technology

Review

http://www.technologyreview.com/view/513996/how-to-carbon-date-a-web-page/

http://www.technologyreview.com/view/519391/internet-archaeologists-reconstruct-lost-web-pages/

http://www.technologyreview.com/view/429274/history-as-recorded-on-twitter-is-vanishing-from-the-web-say-computer-scientists/

http://www.technologyreview.com/view/513996/how-to-carbon-date-a-web-page/

http://www.technologyreview.com/view/519391/internet-archaeologists-reconstruct-lost-web-pages/

http://www.technologyreview.com/view/429274/history-as-recorded-on-twitter-is-vanishing-from-the-web-say-computer-scientists/


Mashable


Mashable

Yes I am Indiana Jones of the

internet


Publications

Published Submitted In preparation Planned

JCDL 2011 TPDL 2015 WWW 2016 IJDL 2016

TPDL 2012 SIGIR 2016 WSDM 2016

JCDL 2013

TPDL 2013

WWW 2013

DL 2014

AAAI 2015

IJDL 2015

JCDL 2015


Remember Rémi Ochlik?

Rémi Ochlik16 October 1983 – 22 February 2012


… and the missing content about him?



We can maintain the consistency of history

Our Temporal Intention Relevancy Model

Doctoral Defense: Hany SalahEldeen

Science

Transcript of Doctoral Defense: Hany SalahEldeen