Zen & the art of data mining

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Old Dominion University Department of Computer Science Hany SalahEldeen Hany SalahEldeen Khalil [email protected] 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 1

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

Transcript of Zen & the art of data mining

  • Old Dominion University Department of Computer Science Hany SalahEldeen Hany SalahEldeen Khalil [email protected] 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 1
  • Before we start.. here is a lil bit about me Hany SalahEldeen 2
  • 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 Hany SalahEldeen 3
  • Research & Technical Experience Microsoft Research Cairo Google GmBH Zurich Microsoft Inc. Mountain View National University of Singapore Hany SalahEldeen 4
  • 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? 5
  • 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 6
  • 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 7
  • Beside the perks of travelling, our research has been popular Hany SalahEldeen 8
  • MIT Technology Review Hany SalahEldeen 9
  • MIT Technology Review Hany SalahEldeen 10
  • MIT Technology Review Hany SalahEldeen 11
  • Mashable Hany SalahEldeen 12
  • Popular Mechanics Hany SalahEldeen 13
  • BBC Hany SalahEldeen 14
  • The Virginian Pilot Hany SalahEldeen 15
  • Our Researchs Popularity Hany SalahEldeen 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 16
  • My goal: Detect, model, and predict user intention in social media Hany SalahEldeen 17
  • Ok hold on, lets go back to the basics Hany SalahEldeen 18
  • 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 Hany SalahEldeen 19
  • 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* Hany SalahEldeen 20 *Toby Segaran, Programming Collective Intelligence, 2007
  • 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 Hany SalahEldeen 21
  • So why do we even care about all that? Hany SalahEldeen 22
  • Power to the People! Hany SalahEldeen 23
  • 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. Hany SalahEldeen 24
  • So what is Data Mining? Hany SalahEldeen 25
  • 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 Hany SalahEldeen 26
  • Back to my goal: Hany SalahEldeen Detecting, Modeling, & Predicting User Temporal Intention in Social Media 27
  • Lets breakdown the title first Hany SalahEldeen Detecting, Modeling, & Predicting User Temporal Intention in Social Media 28
  • Lets breakdown the title first Hany SalahEldeen Detecting, Modeling, & Predicting User Temporal Intention in Social Media 29
  • Scenario 1: Jenny reading Jeffs tweets Hany SalahEldeen 30
  • Michael Jackson Dies Hany SalahEldeen Snapshot on: June 25th 2009 http://web.archive.org/web/20090625232522/http://www.cnn.com/ 31
  • Jeff tweets about it Hany SalahEldeen Published on: June 25th 2009 https://twitter.com/mdnitehk/status/2333993907 32
  • Jeffs friend Jenny was on a vacation in Hawaii for a month Jenny is off the grid Hany SalahEldeen 33
  • When she came back she checked Jeffs tweets and was shocked! Jenny starts catching up a month later Hany SalahEldeen Read on: July26th 2009 https://twitter.com/mdnitehk/status/2333993907 34
  • She quickly clicked on the link in the tweet Jenny follows the link on July 26th Hany SalahEldeen http://web.archive.org/web/20090726234411/http://www.cnn.com/ CNN page on: July 26th 2009 35
  • 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! Hany SalahEldeen 36
  • Scenario 2: The Egyptian Revolution Hany SalahEldeen 37
  • The Egyptian Revolution Jan 2011 Hany SalahEldeen 38
  • Reading about it in Storify.com a year later in March 2012 Hany SalahEldeen http://storify.com/maq4sure/egypts-revolution 39
  • I noticed some shared images are missing Hany SalahEldeen http://storify.com/maq4sure/egypts-revolution 40
  • Some tweets are still intact Hany SalahEldeen https://twitter.com/miss_amy_qb/status/32477898581483521 41
  • and some lost their meaning with the disappearance of the images Hany SalahEldeen Missing ? https://twitter.com/aishes/status/32485352102952960 https://twitter.com/omar_chaaban/status/32203697597452289 42
  • The tweet remains but the shared image disappeared Hany SalahEldeen http://yfrog.com/h5923xrvbqqvgzj 43
  • 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! Hany SalahEldeen 44
  • back to the title Hany SalahEldeen Detecting, Modeling, & Predicting User Temporal Intention in Social Media 45
  • back to the title Hany SalahEldeen Detecting, Modeling, & Predicting User Temporal Intention in Social Media 46
  • 47 The Anatomy of a Tweet Hany SalahEldeen 47
  • 48 The Anatomy of a Tweet Authors username Other user mention Tweet Body Hash TagShortened URL to resource Publishing timestamp Social Post Shared Resource Interaction options Hany SalahEldeen 48
  • 49 3 URIs = 3 Chances to fail Hany SalahEldeen http://news.blogs.cnn.com/2012/04/26/norwegian s-sing-to-annoy-mass-killer/ https://twitter.com/KentEiler/status/19553574 9754527745 49
  • 50 t1 t4 t2 t3 t5 t7 t8 t9 tn t6 Explanation in MJs example 50
  • 51 If I click on a link in a tweet, which version should I get? ttweet or tclick ? Hany SalahEldeen 51
  • 52 Sometimes you want a previous version The Correct Temporal Intention CNN.com at the closest time to the tweet: 25th June 2009 ~ 7pm Hany SalahEldeen 52
  • 53 Sometimes you want the current version The Correct Temporal Intention In this case the current state of the press releases page Hany SalahEldeen 53
  • 54 Research Question Can we estimate the users intention at the time of posting and reading to predict and maintain temporal consistency? Hany SalahEldeen 54
  • 55 People rely on social media for most updated information Hany SalahEldeen 55
  • Hany SalahEldeen So if you are posting a tweet about your cat No one cares! 56
  • Hany SalahEldeen Regardless how cool your cat was! 57
  • All tweets are equal but some are more equal than the others Hany SalahEldeen 58
  • Preliminary Research Questions: 1. How long would these last? 2. And if lost, are they archived? 3. Is this what the author intended? Hany SalahEldeen 59
  • 60 Since tweets are considered the first draft of history the historical integrity of the tweets could be compromised. Hany SalahEldeen Historical Integrity 60
  • 61 The life cycle of a social post Hany SalahEldeen 61
  • 62 The life cycle of a social post tweets Hany SalahEldeen 62
  • 63 The life cycle of a social post tweets Links to Hany SalahEldeen 63
  • 64 The life cycle of a social post tweets What the reader receives Links to Same state the author intended Hany SalahEldeen 64
  • 65 The life cycle of a social post tweets What the reader receives Links to Same state the author intended Hany SalahEldeen The resource has disappeared 65
  • 66 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 Hany SalahEldeen 66
  • 67 Same state the author intended The Resources Possibilities a bigger problem since the reader might not know. What the reader receives The resource has disappeared The resource has changed Hany SalahEldeen 67
  • 68 We could lose the linked resource Hany SalahEldeen 68
  • 69 The attack on the embassy was in February 2013 Or the resource could change Hany SalahEldeen 69
  • 70 Why do we want to detect the Authors 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. Hany SalahEldeen 70
  • 71 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 71
  • 72 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 72
  • 73 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 Hany SalahEldeen 16%-79% Archived according to the source 73
  • 74 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 74
  • 75 The timeline of the resource Hany SalahEldeen 75 http://ws-dl.blogspot.com/2013/04/2013-04-19-carbon-dating-web.html
  • 76 Timestamps Accumulation Hany SalahEldeen 76
  • 77 Actual Vs. Estimated Dates Hany SalahEldeen Successfully estimated the creation date >75% of the resources >33% we estimated the exact date 77
  • 78 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 78
  • From Twitter, Websites, Books: The Egyptian revolution From Twitter Only: Stanfords SNAP dataset: Iranian elections H1N1 virus outbreak Michael Jacksons death Obamas Nobel Peace Prize Twitter API: The Syrian uprising Six Socially Significant Events Hany SalahEldeen 79
  • Resources Missing & Archived Hany SalahEldeen 80
  • Revisiting after a year Hany SalahEldeen 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 81
  • Measured Vs. Predicted Hany SalahEldeen 82
  • First Attempts to Shared Content Replacement Hany SalahEldeen 83 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
  • First Attempts to Shared Content Replacement Hany SalahEldeen We were able to extract another resource with >70% similarity to the missing resource in >40% of the cases 84
  • 85 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 85
  • 86 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 Hany SalahEldeen 86
  • 87 Resource is changed but relevant The resource changed But it is still relevant Intention: need the current version of the resource at any time Hany SalahEldeen 87
  • 88 Relevancy and Intention Mapping Current Hany SalahEldeen 88
  • 89 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 Hany SalahEldeen 89
  • 90 Past Relevancy and Intention Mapping Current Hany SalahEldeen 90
  • 91 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 Hany SalahEldeen 91
  • 92 Past Relevancy and Intention Mapping Current Past Hany SalahEldeen 92
  • 93 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 Hany SalahEldeen 93
  • 94 Past Relevancy and Intention Mapping Current Past Not Sure Hany SalahEldeen 94
  • 95 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 authors intention and collect a dataset 5. Model this intention 6. Create a time-based navigation tool to match the predicted intention Hany SalahEldeen 95
  • 96 Feature extraction For each tweet we perform: Link analysis Social Media Mining Archival Existence Sentiment Analysis Content Similarity Entity Identification Hany SalahEldeen 96
  • 97 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 resources URI Hany SalahEldeen 97
  • 98 2- Social Media Mining Twitter: Using Topsy.coms 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. Hany SalahEldeen 98
  • 99 2- Social Media Mining Facebook: Mined too for likes, shares, posts, and clicks related to each resource. Hany SalahEldeen 99
  • 100 3- Archival Existence Using Memento Time Maps we get: Total mementos available Different archives count. The closest archived version to the tweet time. Hany SalahEldeen 100
  • 101 4- Sentiment Analysis Using NLTK libraries of natural language text processing Extract the most prominent sentiment in the text Hany SalahEldeen 101
  • 102 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 Hany SalahEldeen 102
  • 103 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 Hany SalahEldeen 103
  • 104 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% Hany SalahEldeen 104
  • 105 Whats Next for Hany? Finish up my dissertation Defend. Get a research/Data scientist position Interests: L3S Research Center Germany Microsoft Research Hany SalahEldeen 105
  • 106 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 authors 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: [email protected] Office: 3102 Website: http://www.cs.odu.edu/~hany/ Twitter: @hanysalaheldeen 106