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

2. Group 1

3. Features
a project by
tsw I group01
hardie I pinzi I piscopo
Concept
Target
users

4. What
> social profiles
> user posts
> user played music
Data set 1
Facebook user
statuses and posts
Data set 2
Last.fm listened
tracks

5. 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

6. 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

7. 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

8. 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

9. Group 2

10. The Social Thermometer
The Social Web - VU University Amsterdam
Group 2: Adnan Ramlawi, Sindre Berntsen, Yaron Yitzhak

11. 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

12. 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

13. 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.

14. Progress - What we have so far...

15. Acknowledgements:
All: brainstorming, report
Yaron: data retrieval
Sindre: data processing
Adnan: data visualisation
Adnan, Yaron: presentations

16. Group 3

17. Sleep@Broad
Begoña Álvarez de la Cruz
Aristeidis Routsis
Giorgos Lilikakis

18. 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

19. Goals
o Reduce the financial cost of exploration
o Motivate the traveler to explore new places
feeling safer

20. 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

21. Our application : Sleep@Broad
Welcome page
Login

22. Our application : Sleep@Broad
Friends’ location

23. Our application : Sleep@Broad
Friend List

24. Our application : Sleep@Broad
Friends in a specific location

25. Questions ?

26. Group 4

27. @
Twitter username:
ENTER
Group 4:
Hassan Ali
Annemarie Collijn
Julia Salomons
Hashtags Research tool

28. Twitter Followers World Map

29. Twitter Followers Locations Map

30. Hashtag Word Cloud
Interactive word cloud based on
hashtags
Link to tweets with the clicked
hashtag (#whereihandstand)

31. Work Division
Hassan Ali Writing of Report
Annemarie Collijn Development of App
Julia Salomons Development of app

32. Group 5

33. Travel Together

34. 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

35. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Motivation

36. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Motivation

37. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Approach
Magic
+
Travel Together Control Center
Building a community + reuse of existing data

38. 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

39. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Approach
Magic
+
Travel Together Control Center
Building a community + reuse of existing data
 Working place
 Opening hours

40. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Approach
Magic
+
Travel Together Control Center
Building a community + reuse of existing data
 Realtime Updates

41. 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

42. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Screenshots
Search-
and
Displayoptions
Resultsection
Option to share
on Facebook
and Twitter
X-Ray Mode

43. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Screenshots
Searchradius
Related Messages

44. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Screenshots
X-Ray Mode for easily finding matching routes

45. Achraf Belmokadem
Bernd Themann
Sheldon Pijpers
Group 5Screenshots
Ability to contact friends

46. 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

47. Thanks!

48. Group 6

49. Proofread Pal
Group 6: Bob de Graaff, Justin Post and Melvin Roest

50. What is Proofread Pal?
The simplest and quickest
way to have your
documents proofread!

51. How does it work?

52. User Expertise

53. Matching algorithm
● Similar domain knowledge
● Similar personality profile
● Similar “Proofread Pal” ranking
A match!

54. Let’s take a look

55. What’s next?
● Queue times based on ranking!
● Text mining for better document
classification!
● Weighted evaluation!
● Dolphins!

56. Thanks for listening!
Are there any questions
not regarding dolphins?

57. Group 7

58. #Social Web 2014
#Group 7
#Benjamin Timmermans
#Rens van Honschooten
#Harriëtte Smook

59. #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

60. #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

61. #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

62. #screencast

63. Group 8

64. #analyzing
Twitter’s Trending Topics
The Social Web, 2014
Group 8: Ans de Nijs, Matthijs Rijken, Lia Sterkenburg

65. 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.

66. Analysis of existing tools
• Twistori (sentiment keyword search) à
• We feel fine (feeling analysis) à
• I-logue (trending topic word cloud)

67. Data
• Twitter Tweets (100s - 1000s)
• Text
• Timestamps
• Extract keywords

68. 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

69. 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

70. Group 9

71. Odd “like” out
Group 9
Lennert Gijsen, Mustafa Küçüksantürk & Ömer Ergül

72. 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.

73. 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.

74. Demo

75. Demo

76. Demo

77. Demo

78. Demo

79. 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

80. Individual work
- Explore possibilities
Omer, Mustafa
- Retrieving and analysing Facebook data
Lennert, Omer
- Programming
Lennert, Mustafa
- Testing
Everyone

81. Questions ?

82. Group 10

83. Rcmdr/UTV
Timothy Dieduksman, Guangxue Cao, Adi Kalkan

84. Rcmdr/UTV, Group 10
IMake Problem:
● Irrelevant
recommendations
○ Annoyed viewers
● Goal:
○ Provide users
relevant
recommendation

86. Data & Analysis
SCORE

87. Demonstration

88. Group 11

89. 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

90. CARSIDEROR: Car Perception
For (potential) buyers & car manufacturers
G11

91. CARSIDEROR: Car Perception
For (potential) buyers & car manufacturers
G11

92. CARSIDEROR: Car Perception
 Feature 1:
Positive/negative/neutral
classification (tweets)
For (potential) buyers & car manufacturers
G11
By Andreas Karadimas

93. CARSIDEROR: Car Perception
For (potential) buyers & car manufacturers
G11

94. CARSIDEROR: Car Perception
 Feature 2:
Location-based analysis
For (potential) buyers & car manufacturers
G11
By Luxi Jiang

95. CARSIDEROR: Car Perception
For (potential) buyers & car manufacturers
G11

96. CARSIDEROR: Car Perception
 Feature 3:
Positive/negative/neutral
proportion analysis
For (potential) buyers & car manufacturers
G11
By Micky Chen

97. CARSIDEROR: Car Perception
For (potential) buyers & car manufacturers
G11

98. Group 13

99. PoPlaces
Group 13:
Thom Boekel, Rianne Nieland, Maiko Saan
popular places among your friends

100. 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.

101. 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)

102. 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

103. 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

104. 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

105. 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

106. Group 14

107. Predicting the local elections
with Twitterdata
GROUP 14
Mabel Lips
Marco Schreurs
Wouter van den Hoven

108. Data & Approach
• Our data
• Collection of tweets of political parties and prominent politicians
• Size of data: ~15.000
• Approach
• Sentiment analysis
• Normalisation

109. Purpose of WebApp
• Predict the outcome of the local elections
• People of Amsterdam interested in politics
• Unique:
• Using realtime Twitter data
• Normalisation

110. 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

111. Individual work
• Wouter: Twitterdata retrieval
• Marco: Sentiment analysis of Twitter data
• Mabel: Algorithm sentiment analysis and normalization process

112. Group 15

113. Twitter Recommendation App
Group 15 - Niels, Dick & Sarah
March 2014

114. Goal
Discovering interesting
Tweets, subjects and users.

115. System Overview

118. 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.

119. Semantic Markup
● Allows for machine understanding
● schema.org/{CreativeWork, Person}
● Suggestion: schema.org/MicroBlogPost

120. Feature Sarah
● Discovering and extracting recurring terms (i.
e. common subjects)
● Categorization and visualization of interesting
and uninteresting Tweets

121. Feature Niels
Recommending Tweets
● Part of the larger system
● Basis for more features

122. Questions or Feedback