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Social Web 2014: Final Presentations (Part II)

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Final presentations by students in the Social Web Course at the VU University Amsterdam, 2014 (groups 16-23)

Final presentations by students in the Social Web Course at the VU University Amsterdam, 2014 (groups 16-23)

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  • 1. Group 16
  • 2. The Reincarnation App Filip Ilievski Sylvia van Schie Wouter Stuifmeel The Reincarnation App • A social web application to let you see who you were in previous lives • The Social Web • March 20th, 2014, VU University Amsterdam GROUP 16
  • 3. Introduction / Approach TARGET Users of Facebook GOAL Find the reincarnation chain of the user (all predecessors) METHOD We hook up the user's date of birth to a person who deceased around the same date. (margin up to three weeks) VALUE The app is unique for it links data from Facebook to DBpedia and shows info about deceased people It furthermore has a high entertainment value The Reincarnation App • A social web application to let you see who you were in previous lives • The Social Web • March 20th, 2014, VU University Amsterdam
  • 4. Data flow The Reincarnation App • A social web application to let you see who you were in previous lives • The Social Web • March 20th, 2014, VU University Amsterdam
  • 5. Data clustering The Reincarnation App • A social web application to let you see who you were in previous lives • The Social Web • March 20th, 2014, VU University Amsterdam Clustering is based on the 8 results we received when entering the initial birth date of 20 March 2007.
  • 6. User interface The Reincarnation App • A social web application to let you see who you were in previous lives • The Social Web • March 20th, 2014, VU University Amsterdam
  • 7. Group 19
  • 8. Social Web - Facebook Activity Checker Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Vrije Universiteit Amsterdam March 20th, 2014
  • 9. What? How? Who? What does it look like? 1 What? 2 How? 3 Who? 4 What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 10. What? How? Who? What does it look like? Aim of the application The overall goal is to explore when (ie: which days? what time?) users are the most active on Facebook. Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 11. What? How? Who? What does it look like? Data mining Pyhon script using Python SDK for Facebook; Getting friends statuses and likes; Categorize them, sum up all likes and statuses within a category; For the last week, sum up friends activities for each day. Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 12. What? How? Who? What does it look like? Individual work Thomas: Python code, documentation; Timo: Charts, design of app, data integration and documentation; Kristoffer: Charts, design of app, data integration and documentation. Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 13. What? How? Who? What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 14. What? How? Who? What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 15. What? How? Who? What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 16. What? How? Who? What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 17. What? How? Who? What does it look like? Thomas Berger, Kristoffer Lie Braathen, Timo Nijhof Social Web - Facebook Activity Checker
  • 18. Group 21
  • 19. Concertify Group 21 Bart Eijk, Theano Kotisi, Timur Carpeev
  • 20. Structured data Visualizations
  • 21. Feature I: Clustering genres blues-rock > blues & rock viking metal > metal synthpop > electronic & pop Use pre-determined general tags blues, classical, country, dance, electronic, folk, hip-hop, indie, jazz, latin, metal, musicals, pop, reggae, rnb, rock
  • 22. Feature II: Clustering venues Classification: most occurring word (genre) Bag of Words model based on clustered tags {'blues':  0.0,   'classical':  0.0,   'country':  25.476190476190474,   'dance':  7.1428571428571423,   'electronic':  7.1428571428571423,   'folk':  25.476190476190474,   'hip-­‐hop':  0.0,   'indie':  2.8571428571428572   ...  
  • 23. Feature III: Filtering & Visualizing of venues | JSON data | venue name | latitude, longitude | genre classification | genre vector
  • 24. |  Limitations {  Cold Start Problem/Popularity bias, ad hoc annotation behavior, weak labeling {  Non-agreement on taxonomies {  Ill-defined genre labels {  Scalability of genre taxonomies |  Scope {  From taxonomies to folksonomies?
  • 25. |  Evaluation {  User Qualitative Evaluation of the Application |  Future Work / Developments {  Hierarchical Classification {  Alternative Autotagging Models {  Combining data sources: hybrid context - content systems for music classification & recommendation
  • 26. Group 22
  • 27. Exploiting Twitter data for the automatic annotation of Dutch Public TV Programming Group 22 Guido van Bruggen, Jochem Havermans, Shailin Mohan & Frank Schurgers The Social Web 2014, VU Amsterdam | Final Presentation Social Web App
  • 28. Goal: Helping the Netherlands Institute of Sound and Vision to automatically annotate TV shows Target user group: Clients of Sound and Vision
  • 29. Advantages: ● Use of existing data; ● inexpensive; ● instantaneous annotation; ● easy to implement; ● automatic detection of hot topics; ● less need for tedious human annotation. much advantage wow such cheap very automatic amaze so data
  • 30. Words per minute annotation De Wereld Draait Door, 18- 03-2014. Minute: 18:17.
  • 31. Pauw en Witteman, 18-03-2014. Viewers: 778.000. Total tweets: 2231.
  • 32. Additions ● Words per minute annotation ● fragmentation of episodes; ● access to tweet content of all tweets; ● normalizing tweet count with viewer count;
  • 33. Group 23
  • 34. WhatsAround “Be everywhere, anywhere!” Youssef Azriouil, Sara Chambel Pinheiro, Ana Rodrigues & Evangelia Marinaki
  • 35. "Footprint - Where I've Been" is a designed map app for adding notes and marking places “Flickr” capture, create, and share photos “Instagram” way to capture and share the world's moments “Worldcam” easy way of finding photos from a specific venue
  • 36. WhatsAround FAST GLOBAL FUN Way to share your life and travels with friends, family and community Easiest way of finding out what's happening right now, anywhere and everywhere
  • 37. WhatsAround Get user location Serve up photos Serve up place info Send coordinates to API (DataSets)
  • 38. WhatsAround Customized search Different perspectives on a place Be part of history by documenting Chronological sequence - Understand the evolution of the place
  • 39. Check your location in real time No hashtags Friends Vs Community Clustering analysis
  • 40. Photographers Travellers Active users of social web apps
  • 41. SARA – Location Assessment EVI – Instagram and Flickr API info retrieval ANA – WikiLocation API connection YOUSSEF – Mobile experience
  • 42. Group 25
  • 43. Group 25 Jesse Groen, Alessio Muis, Ragaselvi Ratnasingam
  • 44. Our application Features of the application Data Analysis Individual features
  • 45. Where is it used for? Connect common hashtags to users Fun! Enrich tweets
  • 46. Clickable (Domain) Recommendation User Dependent
  • 47. Twitter data: Hashtags Times used Followers & following
  • 48. Cluster analysis Trends analysis
  • 49. Jesse: Common hashtags among friends Alessio: User dependent - Your own content Selvi: Recommendation among your connections
  • 50. AlessioSelviJesse @Group25 Thank you very much! #SW2014 #findyourhashtag #finalassignment 20 Mar via web Favorite Retweet Reply
  • 51. Group 26
  • 52. GROUP 26-APP INTRODUCTION • Goal: Find top popular places among my friends • Function: • 1. Filter different type of places • 2. Find people from different countries like to go where? • 3. Analyze our data
  • 53. INDIVIDUAL WORK • Annan Cheng: 1. Retrieve friends data from Facebook; 2. Extract where friends have been from data; 3. Get friends nationality through Google Geocoding API • Jiahui Chen: Design the demo, realize the map, filter and sequencing functions through Google map API, d3 and JavaScript • Ziyan Zong: Realize analyze data function by using different visualization methods like histogram and pie chart, etc. Find out meaningful and useful facts behind those data.
  • 54. Group 27
  • 55. Group 27 Final assignment presentation
  • 56. Data ● Twitter streaming API ● VeryRelated API
  • 57. Approach ● Search for query ○ Select most popular tweets ○ Find most relevant terms ○ Look for synonyms ● Select relevant terms ○ Refine the query ○ Update the results
  • 58. Mockup
  • 59. Added value ● Interactive ○ Does suggestions ● Intuitive ○ Clicking instead of typing ● Interesting ○ 1 page overview
  • 60. Group 29
  • 61. Predicting attendance and outcome of local elections - A web application Group 29: Remco Draijer Renee Vaessen Lily Martinez Ugaz
  • 62. Description & Goal • A web application ▫ Local elections on March 19th in the Netherlands • Predicting outcome and attendance ▫ Based on number of tweets • Functions as an informative app • Investigates correlation between tweets and: ▫ Outcome (number of seats in municipal council) ▫ Attendance (turnout)
  • 63. Approach • Mining Twitter for location-based tweets ▫ Amsterdam, Den Haag, Rotterdam and Utrecht • Nine parties selected ▫ All occur in four cities • Extract tweets with party-name in text, user- name or user-description ▫ Preprocess data for duplicates and party- generated tweets
  • 64. Data • Twitter streaming API ▫ Fetch tweets during 24 hours • Interactive visualizations with d3.js • Results: correlation between % of seats in council and % of tweets about parties ▫ For Amsterdam, Den Haag, Rotterdam, Utrecht • Distribution of tweets over mining period
  • 65. Visualization • http://www.remcodraijer.nl/socialweb/ass4/
  • 66. Group 30
  • 67. News Timeline The Social Web 2014 - final assignment Group 30
  • 68. Motivation & Purpose • information • easy, visual way of getting up to speed • easier than reading a newspaper or an RSS reader • like a more visual, web-based, Flipboard
  • 69. Data Sources • media agencies around the world (list from Wikipedia) • Twitter • Bing image search • Sentiment140
  • 70. Data Flow • get the trending stories from the news agencies • find tweets about the stories • perform sentiment analysis on the tweets • get images (and videos) related to the story • export via JSON API
  • 71. Data Flow • get the trending stories from the news agencies • find tweets about the stories • perform sentiment analysis on the tweets • get images (and videos) related to the story • export via JSON API filtered by region and/or tag
  • 72. Display • Web app • Mobile friendly • Responsive gallery • See a prototype at http://www.mihneadb.net/news_timeline/
  • 73. Demo
  • 74. Demo
  • 75. Group 31
  • 76. The Social Web 2014 – Final assignment – G31: Jeroen Wever JWR970, Jeffrey Bruijntjes JBS257, Marije ten Brink MBK262 APPSPIRE! What are his/her true aspirations? An entertaining way to discover dreams and desires of influential people… or your friends!
  • 77. The Social Web 2014 – Final assignment – G31: Jeroen Wever JWR970, Jeffrey Bruijntjes JBS257, Marije ten Brink MBK262 Goal a. Discover aspirations of a person of your interest b. Compare your aspirations with others c. Visualize aspirations in tag clouds or photo clouds d. Find people who have specific aspirations e. Follow trends in our shared aspirations Related work: Twitter Account Showdown
  • 78. The Social Web 2014 – Final assignment – G31: Jeroen Wever JWR970, Jeffrey Bruijntjes JBS257, Marije ten Brink MBK262 Approach: discover 1. Retreive all tweets of user with Greptweet.com 2. Clean up tweets, remove punctuation, stop words, links 3. Use stemmer: matches verbs and nouns with same stem 4. Use word list with aspirational verbs to extract tweets with aspirations 5. Count occurrences of meaningful word Other possible data sources: FB, blogs, Pinterest, Google+, ...
  • 79. The Social Web 2014 – Final assignment – G31: Jeroen Wever JWR970, Jeffrey Bruijntjes JBS257, Marije ten Brink MBK262 Approach: compare Determine similarity between 2 users, based on amount of matching words: correlation
  • 80. The Social Web 2014 – Final assignment – G31: Jeroen Wever JWR970, Jeffrey Bruijntjes JBS257, Marije ten Brink MBK262 Approach: visualize Tag cloud Image cloud Timeline
  • 81. Group 32
  • 82. Final Assignment: Group 32 Francois Lelievre, Marek Janiszewski, Yahia El-Sherbini, David Marshall-Nagy
  • 83. Main Concept ■ An easy to use application to manage your SoundCloud collection and enhance the social experience with music
  • 84. Group Effort ■ Concept and Development: Marek and Francois ■ Concept and Testing: Yahia and David
  • 85. Technical Background ■ Based on the SoundCloud API ■ In-browser interface
  • 86. Music Collection Overview ■ Check all my likes in one place ■ Check all the tracks of a followed artist in one place
  • 87. Collection Management ■ Search for new songs ■ Sort out and favorite your songs ■ Follow artists
  • 88. Song Recommendation ■ Based on the selected genre and on the likes of followed people ■ Based on the recent likes of the followed artists ■ Visualized search results
  • 89. Thank You for Your Attention! Questions?