Social Web 2014: Final Presentations (Part I)

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Final presentations by students in the Social Web Course at the VU University Amsterdam, 2014 (groups 1-15)

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  • Group 15 screencast: https://www.youtube.com/watch?v=j-hKJPVcv2M
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Social Web 2014: Final Presentations (Part I)

  1. 1. Group 1
  2. 2. Features a project by tsw I group01 hardie I pinzi I piscopo Concept Target users
  3. 3. What > social profiles > user posts > user played music Data set 1 Facebook user statuses and posts Data set 2 Last.fm listened tracks
  4. 4. How > sentiment analysis > filtering > cross-correlation Sentiment analysis Colours encode user’s mood Listening prefs Tracks played are shown for each time slot Playlist generation Playlist generated according to moods
  5. 5. Evaluation process > user study Preliminary studies User profiling Information needs Low-fi prototypes Hi-fi prototype User evaluation On a working prototype ● Design evaluation ● Information gains, user relevance ● Functionality evaluation
  6. 6. Conclusions > critical aspects > future work Moods detection Minimum amount of data needed to reliably extract emotional patterns Single sign on At present, signing in each of the two SNSs is needed Moods detection Datasets could be further expanded and more elements analysed to detect users’ moods Single sign on Authentication through OpenId or similar services should be implemented
  7. 7. Organisation > individual work Graham Hardie Programming, data collection and data visualization Viola Pinzi Theoretical analysis, visual design and data analysis Alessandro Piscopo Theoretical analysis, visual design and data visualisation
  8. 8. Group 2
  9. 9. The Social Thermometer The Social Web - VU University Amsterdam Group 2: Adnan Ramlawi, Sindre Berntsen, Yaron Yitzhak
  10. 10. Introduction ● Weather issues: ○ Too hot, too cold, too wet, et cetera ○ Does the weather affect people’s mood? ● Is there a correlation between: ○ Weather ○ Twitter sentiment
  11. 11. The application: ● Data used: ○ Tweets ○ Weather data (temperature, precipitation, cloudiness) ● Analysis: ○ Classification of tweets ○ Filtering ● Virtualization: ○ Average sentiment of tweets vs. weather elements (per day) ○ ChartJS, Bootstrap
  12. 12. Code: ● How does the application work: ○ Long, Lat retrieval via Google Maps API ○ Weather data - World Weather Online (JSON). ○ Tweets - Twitter API (filtered by long,lat,lang,date) ■ Tweets re-formatted (JSON) ■ Tweets sent to Sentiment140 API ● Returned data is displayed in graphs using a ChartJS script.
  13. 13. Progress - What we have so far...
  14. 14. Acknowledgements: All: brainstorming, report Yaron: data retrieval Sindre: data processing Adnan: data visualisation Adnan, Yaron: presentations
  15. 15. Group 3
  16. 16. Sleep@Broad Begoña Álvarez de la Cruz Aristeidis Routsis Giorgos Lilikakis
  17. 17. Introduction & Context o Willingness to travel around the world • Expensive • Time to plan the trip (finding accommodation) o Alternatives • Couch surfing (accommodate to a stranger’s house) o Our application: • Leverage the hospitality of your friends
  18. 18. Goals o Reduce the financial cost of exploration o Motivate the traveler to explore new places feeling safer
  19. 19. Approach & Method o Extract data from user’s Facebook account • User’s friends • User’s friends name • User’s friends photo • User’s friends current location • Personal friends lists o Visualization • Google Maps API • Map • Markers o Provide travel details • Google flights • Skyscanner API
  20. 20. Our application : Sleep@Broad Welcome page Login
  21. 21. Our application : Sleep@Broad Friends’ location
  22. 22. Our application : Sleep@Broad Friend List
  23. 23. Our application : Sleep@Broad Friends in a specific location
  24. 24. Questions ?
  25. 25. Group 4
  26. 26. @ Twitter username: ENTER Group 4: Hassan Ali Annemarie Collijn Julia Salomons Hashtags Research tool
  27. 27. Twitter Followers World Map
  28. 28. Twitter Followers Locations Map
  29. 29. Hashtag Word Cloud Interactive word cloud based on hashtags Link to tweets with the clicked hashtag (#whereihandstand)
  30. 30. Work Division Hassan Ali Writing of Report Annemarie Collijn Development of App Julia Salomons Development of app
  31. 31. Group 5
  32. 32. Travel Together
  33. 33. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5 Help user to find people with similar routes to their workplace • Allows car pooling which saves fuel, reduces carbon dioxide emission and helps to reduce traffic jams • More social to ride with somebody else or use the car in case of bad weather Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Purpose
  34. 34. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Motivation
  35. 35. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Motivation
  36. 36. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Approach Magic + Travel Together Control Center Building a community + reuse of existing data
  37. 37. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Approach Magic + Travel Together Control Center Building a community + reuse of existing data  Friendlist  Working and living place  Opening hours  Realtime updates
  38. 38. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Approach Magic + Travel Together Control Center Building a community + reuse of existing data  Working place  Opening hours
  39. 39. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Approach Magic + Travel Together Control Center Building a community + reuse of existing data  Realtime Updates
  40. 40. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Approach Magic + Travel Together Control Center Building a community + reuse of existing data  Working and living place  Workinghours
  41. 41. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Screenshots Search- and Displayoptions Resultsection Option to share on Facebook and Twitter X-Ray Mode
  42. 42. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Screenshots Searchradius Related Messages
  43. 43. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Screenshots X-Ray Mode for easily finding matching routes
  44. 44. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Screenshots Ability to contact friends
  45. 45. Achraf Belmokadem Bernd Themann Sheldon Pijpers Group 5Evaluation  Burdon to join cummunity decreased due to prefilled information and access via Facebook account  Higher value for the user because even not registered users are participating  „missuse“ of information  NLP techniques are really weak and have a low accuracy
  46. 46. Thanks!
  47. 47. Group 6
  48. 48. Proofread Pal Group 6: Bob de Graaff, Justin Post and Melvin Roest
  49. 49. What is Proofread Pal? The simplest and quickest way to have your documents proofread!
  50. 50. How does it work?
  51. 51. User Expertise
  52. 52. Matching algorithm ● Similar domain knowledge ● Similar personality profile ● Similar “Proofread Pal” ranking A match!
  53. 53. Let’s take a look
  54. 54. What’s next? ● Queue times based on ranking! ● Text mining for better document classification! ● Weighted evaluation! ● Dolphins!
  55. 55. Thanks for listening! Are there any questions not regarding dolphins?
  56. 56. Group 7
  57. 57. #Social Web 2014 #Group 7 #Benjamin Timmermans #Rens van Honschooten #Harriëtte Smook
  58. 58. #motivation #useful  An easy way to find free things via Twitter  You don’t need to search for Twitter accounts about free things  You don’t need to have a Twitter account at all! #unique  There are several Twitter accounts that tweet about freebies  Gratweet collects all new tweets about freebies for you.  Unique in The Netherlands
  59. 59. #data #what  Dutch tweets that contain the keyword ‘gratis’  Geographic coordinates of the tweets  Alternative: social web data from other resources such as Facebook #pre-processing: filtering  Explicit tweets  Identical (re)tweets  Stopwords, meaningless words, personal pronounces  Timestamps, URLs
  60. 60. #approach #algorithms Assign specific weights to words surrounding the keyword ‘gratis’ #backend Cache tweets using Twitter API and Tweet.JS #frontend Visualizations made with D3.JS, Jquery, CSS, HTML
  61. 61. #screencast
  62. 62. Group 8
  63. 63. #analyzing Twitter’s Trending Topics The Social Web, 2014 Group 8: Ans de Nijs, Matthijs Rijken, Lia Sterkenburg
  64. 64. Why this solution? Our goal: Inform people on specific topics and how they developed over time. •  People may not know what trending – or certain other – topics are about on Twitter. Our solution: Visualization of trending topics as word clouds combined with insight on the explosion of tweets over time with sentiment analysis if the tweets are about good or bad news.
  65. 65. Analysis of existing tools •  Twistori (sentiment keyword search) à •  We feel fine (feeling analysis) à •  I-logue (trending topic word cloud)
  66. 66. Data •  Twitter Tweets (100s - 1000s) •  Text •  Timestamps •  Extract keywords
  67. 67. Approach 1.  Use Twitter API •  GET search/tweets (Matthijs) 2.  Use Python packages •  Textblob (sentiment analysis - Ans) •  Visualize sentiments of tweets over time in a cloud •  Pytagcloud (word cloud visualization - Lia) •  Extract tags based on word frequencies •  Important words are displayed larger
  68. 68. Smart part •  Filter out ‘meaningless’ words (e.g. ‘of’, ‘that’) and process the ones that really matter •  Provide a condensed view of a trending topic in a word cloud. •  Sentiment over time: shows changing opinions
  69. 69. Group 9
  70. 70. Odd “like” out Group 9 Lennert Gijsen, Mustafa Küçüksantürk & Ömer Ergül
  71. 71. Our application ● Odd one out game using “likes” from Facebook. ● Retrieve small list of likes for a selection of Facebook friend. ● Random pages(potential likes) are added to each list. ● Player has to pick the odd one(s) out.
  72. 72. Our application ● Type: - Entertainment - Raise awareness to other possible likes. - Give insight to what friends like in an interactive and fun way. ● Scoping: - Only usable with a Facebook account. - Facebook users who’s friends have enough likes.
  73. 73. Demo
  74. 74. Demo
  75. 75. Demo
  76. 76. Demo
  77. 77. Demo
  78. 78. Evaluation / Improvements ● Measurables: - Amount of users / games played per day - Variations in users per day - Users’ scores ● Future work: - Clustering for better matching of “likes” ○ Creates more variety in difficulty - Add scores ○ Percentage correct on daily basis ○ Leaderboards, shared between friends ○ Makes users come back
  79. 79. Individual work - Explore possibilities Omer, Mustafa - Retrieving and analysing Facebook data Lennert, Omer - Programming Lennert, Mustafa - Testing Everyone
  80. 80. Questions ?
  81. 81. Group 10
  82. 82. Rcmdr/UTV Timothy Dieduksman, Guangxue Cao, Adi Kalkan
  83. 83. Rcmdr/UTV, Group 10 IMake Problem: ●  Irrelevant recommendations ○  Annoyed viewers ●  Goal: ○  Provide users relevant recommendation
  84. 84. Data & Analysis SCORE
  85. 85. Demonstration
  86. 86. Group 11
  87. 87. CARSIDEROR: Car Perception  Public opinions on car brands  Twitter data: pre-assigned domain-specific #hashtags  Retrieve tweets  Sentiment analysis  Distribute results - Geographically For (potential) buyers & car manufacturers G11
  88. 88. CARSIDEROR: Car Perception For (potential) buyers & car manufacturers G11
  89. 89. CARSIDEROR: Car Perception For (potential) buyers & car manufacturers G11
  90. 90. CARSIDEROR: Car Perception  Feature 1: Positive/negative/neutral classification (tweets) For (potential) buyers & car manufacturers G11 By Andreas Karadimas
  91. 91. CARSIDEROR: Car Perception For (potential) buyers & car manufacturers G11
  92. 92. CARSIDEROR: Car Perception  Feature 2: Location-based analysis For (potential) buyers & car manufacturers G11 By Luxi Jiang
  93. 93. CARSIDEROR: Car Perception For (potential) buyers & car manufacturers G11
  94. 94. CARSIDEROR: Car Perception  Feature 3: Positive/negative/neutral proportion analysis For (potential) buyers & car manufacturers G11 By Micky Chen
  95. 95. CARSIDEROR: Car Perception For (potential) buyers & car manufacturers G11
  96. 96. Group 13
  97. 97. PoPlaces Group 13: Thom Boekel, Rianne Nieland, Maiko Saan popular places among your friends
  98. 98. Goal & Added value Group 13: Thom Boekel, Rianne Nieland, Maiko Saan Goal: Helps you to find places to go to based on popular places among your friends. Added value: Information of friends might be more interesting to you than reviews available on the internet.
  99. 99. Data Group 13: Thom Boekel, Rianne Nieland, Maiko Saan Data source: Facebook locations of friends Wikipedia location information, future work Size of data: Information of all your friends, in our case: 140 friends (1819 locations) and 215 friends (2517 locations) Type of data: JSON files containing friends and locations (latitudes and longitudes)
  100. 100. Approach Data collection Gather friend locations from Facebook Process Categorize data on year Filter out locations without latitude and longitude Visualization Heatmap with markers Heatmap → number of friends Markers → all locations Group 13: Thom Boekel, Rianne Nieland, Maiko Saan
  101. 101. Visualization (1/2) Group 13: Thom Boekel, Rianne Nieland, Maiko Saan Visualization type: Google heatmap with location markers Visualization of places: Locations marked with markers Popularity of locations indicated with colors and radius
  102. 102. Visualization (2/2) Group 13: Thom Boekel, Rianne Nieland, Maiko Saan Options: Filter locations by year Heatmap options (e.g. radius) Infobox with: ● information about the location provided by Wikipedia ● friend visits per year
  103. 103. Critical reflection Pro’s: ● Filter on year ● Indication of popularity of a location (heatmap) ● Able to perform pattern analysis, e.g. Ziggodome (number of visits increases every year) Con’s: ● Only locations your friends have checked in or were tagged ● Cannot see the names of your friends ● Only information for locations available on Wikipedia
  104. 104. Group 14
  105. 105. Predicting the local elections with Twitterdata GROUP 14 Mabel Lips Marco Schreurs Wouter van den Hoven
  106. 106. Data & Approach • Our data • Collection of tweets of political parties and prominent politicians • Size of data: ~15.000 • Approach • Sentiment analysis • Normalisation
  107. 107. Purpose of WebApp • Predict the outcome of the local elections • People of Amsterdam interested in politics • Unique: • Using realtime Twitter data • Normalisation
  108. 108. Algorithms • Sentiment analysis • Pattern: python package with functionality for sentiment analysis • SentiWordNet: Dutch sentiment lexicon (De Smedt and Daelemans, 2012) Source image: http://jmlr.org/papers/volume13/desmedt12a/desmedt12a.pdf
  109. 109. Individual work • Wouter: Twitterdata retrieval • Marco: Sentiment analysis of Twitter data • Mabel: Algorithm sentiment analysis and normalization process
  110. 110. Group 15
  111. 111. Twitter Recommendation App Group 15 - Niels, Dick & Sarah March 2014
  112. 112. Goal Discovering interesting Tweets, subjects and users.
  113. 113. System Overview
  114. 114. General Features • Memory-based collaborative filtering. • Naive Bayes classifier to train on user’s timeline. • Linear discriminant analysis: interesting vs. uninteresting. • Continuous loop: retrieve Tweets and let user rate.
  115. 115. Semantic Markup ● Allows for machine understanding ● schema.org/{CreativeWork, Person} ● Suggestion: schema.org/MicroBlogPost
  116. 116. Feature Sarah ● Discovering and extracting recurring terms (i. e. common subjects) ● Categorization and visualization of interesting and uninteresting Tweets
  117. 117. Feature Niels Recommending Tweets ● Part of the larger system ● Basis for more features
  118. 118. Questions or Feedback

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