Reading the Correct History? Modeling Temporal Intention in Resource Sharing

Reading the Correct History? Modeling Temporal Intention in

Resource Sharing

Hany SalahEldeen & Michael Nelson Reading the Correct History?

Hany M. SalahEldeen & Michael L. Nelson

Old Dominion University Department of Computer Science

Web Science and Digital Libraries Lab.

Hany SalahEldeen & Michael Nelson 1 Reading the Correct History?

• We share web pages

What I share might not be what my readers read Possible Scenario:

• Web pages change

• Readers explore shared pages

Motivation

A temporal inconsistency can arise in the intention of the author regarding the state of the resource between the

tweet time and the read time…


Can we detect and model this difference in intention?

The game plan


Problem Illustration

Training data collection attempts

The TIRM model

Ground truth validation

Data collection

Feature extraction and modeling

Model evaluation

Example: Obama’s press conference on 14th of Jan 2013


Clicking on the link in the tweet …


Using the Twitter expanded interface


The attack on the embassy was in February 2013

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 .



Implication: Since tweets are considered the first draft of history… the historical

integrity of the tweets could be compromised.

Solution: Detect the correct intention


Option 1 Option 2 Option 3

The game plan




The TIRM model


Data collection


Model evaluation

Amazon’s Mechanical Turk (MT) • 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: Collect user intention data via MT

Hany SalahEldeen & Michael Nelson 11

Reading the Correct History?

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

• Given a tweet, is the intended state of the link is in:


past state? current state? No information?

Ground truth collection

• A dataset of 100 tweets classified by:

– Our Web Science and Digital Libraries (WS-DL) research group members

– MT workers


The agreement was very low…

• Reliability of agreement between:

– WS-DL members = Fleiss’ ϰ = 0.14

– MT workers = Fleiss’ ϰ = 0.07

• Inter-rater agreement between the collective WS-DL members and MT workers = Cohen’s ϰ = 0.04

Slight agreement


So we removed the guessing part: • The tweet is presented along with the two snapshots:


at ttweet at tclick

… and classified the 100 tweets again

• Via a face to face meeting with WS-DL members.

• Resubmitted the new experiment to MT.


The tweet, current and past snapshots


Past Version Current Version

The results remained very low

• 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 that flipping a coin!


Observations

• Assigning a temporal intention is not a trivial task.

• MT workers are accustomed to more straightforward tasks.

• The concept of “time on the web” is foreign to MT workers.


The game plan




The TIRM model


Data collection


Model evaluation

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

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

Past



Current


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

Past



Current

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

Past



Current

Past Not Sure

The game plan




The TIRM model


Data collection


Model evaluation

Next step: validation

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


✓

Is the content still relevant to the tweet?

Filtering the results

• We accepted raters with: – At least 1000 accepted HITs

– 95% acceptance rate

• Average completion time = 61 seconds

• We removed:

– Any assignments that took <10 seconds hasty decision

– Low quality repetitive assignments and banned the raters


Mechanical Turk Workers Vs. Experts

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

• Cohen’s ϰ = 0.854 almost perfect agreement


Agreement in three or more votes 93%

Agreement in four or more votes 80%

Agreement with all five votes 60%

The game plan




The TIRM model


Data collection


Model evaluation

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


Get the closest memento


… t1 t2

tn

t4 t3

Δ1 Δ2 < Pick Memento @ t1

Sorted Time Delta between tweet and closest memento


Randomly selected 1,124 instances Time 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%

The game plan




The TIRM model


Data collection


Model evaluation

Feature extraction

• For each tweet we perform:

– Link analysis

– Social Media Mining

– Archival Existence

– Sentiment Analysis

– Content Similarity

– Entity Identification


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


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.

Social Media Mining

• Facebook:

– Mined too for likes, shares, posts, and clicks related to each resource.


Archival Existence

• Using Memento Time Maps we get: – Total mementos

available

– Different archives count.

– The closest archived version to the tweet time.


Sentiment Analysis • Using NLTK libraries of natural language text processing

• Extract the most prominent sentiment in the text


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

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

• To remove confusion we removed the close calls

898 instances remaining

Relevant Assignments 929 82.65%

Non-Relevant Assignments 195 17.35%



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

Modeling and Classification


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%


Testing the model


10-Fold Cross-Validation Testing

Classifier Mean Absolute Error

Root Mean Squared Error

Kappa Statistic

Incorrectly Classified %

Correctly Classified %

Cost sensitive classifier based on Random Forest

0.15 0.27 0.39 9.68% 90.32%

Classifier Precision Recall F-measure Class

Cost sensitive classifier based on Random Forest

0.93 0.53

0.96 0.37

0.95 0.44

Relevant Non-Relevant

Weighted Average 0.89 0.90 0.90

Feature significance

• Since we have 39 features, we needed to understand the effect of each feature and which are the strongest ones affecting the classification

• We applied an attribute evaluator supervised algorithm based on Ranker search to find the strongest features


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 0.0527

5 Similarity: Tweet & past page 0.04401

6 Original URI’s depth 0.0324

The game plan




The TIRM model


Data collection


Model evaluation

Model Evaluation

• Next step was to test the trained model against other datasets and examine the results.

• 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)


Results of testing the model against multiple datasets


Dataset Status 200 Status 404 of 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.

Recap…

Now we need to transform it back!

Mapping TIRM

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


Conclusions • TIRM successfully transfers the temporal intention

problem to a temporal relevancy problem.

• Temporal relevancy is easier to solve and MT workers provide almost perfect agreement with experts’ opinions.

• We successfully collected a gold standard dataset of temporal user intention.

• We found a temporal inconsistency in the shared resource ranging from <1% to 25% according to the dataset.


Reading the Correct History? Modeling Temporal Intention in Resource Sharing

Technology

Transcript of Reading the Correct History? Modeling Temporal Intention in Resource Sharing