Social Web @VU - Student Presentations on Housing, Events & Tourism Apps

Social Web @VU
2015
Final Student Presentations
Lecturer: Lora Aroyo
The Network Institute
VU University Amsterdam

Student housing in Amsterdam
Group 1
Menno Nelis Rutger van Gennep Melanie van der Velde

The idea
- Finding a home in a new city can be difficult for
students
- Provide an overview of the largest student houses and
their surroundings
- Preferably even filter on certain types of venues such as
restaurants, bars or fitness centers

The idea
Red circles = existing
student housing
Blue circles = student
housing in development

Used data sources
- Amsterdam Open Data - Dataset that provides
DUWO/de Key available housing for students
- Foursquare - Data on public venues
- Facebook - Likes of the venues

Feature 1
- Amsterdam Open Data
- Dataset with stastic information about the student
houses
- Name, address, coordinates
- Imported into MySQL database

Feature 2
- Information extracted from Foursquare
- Search for venues based on coordinates from
Amsterdam Open Data dataset
- Result is exported into a JSON file
- JSON is displayed on website
via Javascript or PHP

Feature 3
- Retrieve extra information from Facebook pages of the
student houses
- Likes
- Other information possible

The GoTwi Grab Bag
Josephine Jessen, Roxane Lubbers Arjan Vis
Group 2

Structure
The GoTwi Grab Bag
Workflow of Application
Some screenshots
Analyse
Future
Individual
QA

The GoTwi Grab Bag
Goal → Get surprised by new interesting topics and subject!
Method → Jump into a black box, with just little swing
Data Sources and services used next to our code:
→ Twitter(API): tweets
→ Google+(API)
→ Topia Term extractor
→ NYtimes API
→ DBpedia
→ Wikipedia
→ ‘Let me Google that for you’ server

Workflow of the Application
- Query a subject of your interest
- Retrieve 20 Popular Tweets (if less than 6 popular tweets, go to 20 recent tweets) + 20 Google+
Posts
→ One list is formed out of the Tweets and Google+ Posts
- Choose random one Tweet or G+Post
→ If Twitter, the timeline of the users account will be retrieved
→ If Google, the activity of user will be retrieved
- Randomly choose one Tweet/activity
→ mine subjects, take one randomly → result: one new ‘inspiring’ subject
→ subject links to DBpedia, wikipedia and 10 articles from the New York Times

Analyse
Cluster
◆ K means cluster
◆ Popular tweets
◆ Google+ posts
◆ Topics
Link
◆ Find similarities between tweets, posts, and subjects
Gain insight into interests of a Twitter and/or Google+ user

Individual: Josephine
➔ Rationale: Surprise yourself with a new inspiring topic
➔ Motivation: Let go of your structured search, through the
uncertainty you will invite new interesting topics out of
your scope
➔ Evaluation: How ‘new’ are the subjects to the user
➔ Scoping: Is the new subject really new? Design of
application
➔ Future work: Create a sort of loop, so the application
can see if the user is already familiar with the randomly
chosen topic (and if so, take other topic etc.)

Individual: Roxane
➔ Rationale: Gain new insights with the topics
you like
➔ Motivation: Meet new people with same
interests
➔ Scoping: More user friendly
➔ Evaluation: How many users?
➔ Future work: Do clusters change over time?

Individual: Arjan
➔ Selecting random tweet / activity
➔ Limitations: Number of tweets / activities that
can be retrieved
➔ Evaluation: Usage of the app
➔ Future work: Visualisation tagging

Giada Binelli
Rio Essed
Aydan Gasimova
Group 3
VU Amsterdam: Social Web 2015

Mobile Application
Browse through potential roommates’ profiles, chat and schedule an appointment
Compatible roommates matched based on Social Web data:
Facebook Spotify In-app survey
Likes
Friends
User Profile info
Attended Events
Saved tracks
Personal Playlist
Genres
Personal preferences
Tolerance
Basic Information
Innovative feature: Using data on Music taste to determine compatibility
Added value:
➔ Makes search for a roommate a fun and social activity
➔ Better system for screening roommates, increases probability of finding the right person

Data retrieval
Rationale: Wide range of data already available
Motivation: Enhance usability
Scoping: Retrieve user related data (basic info, events,
pictures, likes etc) and friendlist
Evaluation: Check for similarity in data of attributes that
co-occur in the different sources

Analysing Processing
● Frequency analysis of genres listened to by users rank = matching
the highest ranks
● Identify overlaps in tracks listened by users score and rank matches
● Encode user preference/tolerance weighed based on assigned
importance
= Calculate a satisfaction score of each match
= Calculate the Match percentage using geometric mean
● Filter match suggestions by clustering user-data on gender, age, and
location

Matching algorithm
Rationale: “Similarity Theory” in Psychology;
Matching based on (Music) taste
Target users: Younger demographic;
Roommate seekers likely to use Spotify
Survey Data = (Preference vs. Tolerance)*Importance
Limitations: “Type 1 vs. Type 2 errors”;
Not using a parameter of relevance VS, using an
irrelevant one
Evaluating success: Collect and analyse data on actual
matches as opposed to Match percentages
Future work: Use association rules;
Look at more than overlaps in taste but also at
similarities (would require a similarity score)

Learning Optimizing
● Learning as an optimization problem
● Backpropagation algorithm
● Update weights
● Minimize loss function
➔ Improved matches

Group 4: Galen, Isabelle Vigo
19 March 2015
S

Problem
Smartphones have become a daily
requisite, but battery life is not
keeping up with our demand.
Crowdsource the locations of the
numerous yet hard to find power
sockets in public places.
Solution

S
S
S
S
S
S
S
S 2 sockets.
Inside Starbucks.
Upon entering on
your right.
Next to a table.
In the corner.
Close to the floor.

Features Functionalities
o Crowdsourcing sockets
o Google (Indoor) Maps
o Socket-community
o Link to social networks
o Gamification:
Point-system, rankings unlockables
Screenshot of actual app

Research: Similar apps
TomTom Waze PulsePoint Healthmap

Research: Questionnaire (1)
o Heterogeneous group
o n = 100
o ♂/♀ = 51/49
o Mean age = 31

Research: Questionnaire (2)
Functionality appreciation:
+ + Google Maps
+ + Location finder
+ Search
- Profile
- Signup
Motivators for (non-)contributors
to contribute to crowdsourcing:
 Point system
 Social benefits
 Curiosity
 Conscience

Planned data analyses
• Summarization: visualize and create report on data
• For the developer as well as the user (in-app)
• Clustering: group and structure the data
• Detect if users that make use of the app also contribute to the community
• Association rule learning: show relationships between variables
• Track users’ frequent locations and suggest favorite sockets or near sockets

Event Explorer
TSW 2015, Group 6

Problem-statement
● Hard to get overview about local events
● Lack of good recommendation for events
● Facebook often used to search for events
● Not possible to search for events on a map

Event Cluster 1
Event Cluster 2
Event Cluster 3
Application
Lexical
Similarity

Features
Facebook-Login
● Rationale: Personalization
● Motivation: Find interesting Events
● Scoping: Analyze previous Event attendances
● Limitation:
○ No additional Profile information
○ Change of user taste
○ Not the full picture
● Evaluation: Self-Evaluation, Analysis on past Events
● Future work: Evaluate facebook likes

Features
Find Local Facebook Events
● Rationale: Search/Recommendation
● Motivation: Missing local search
● Scoping:
○ Events which are present on external sources
○ Public Events
○ Lexical Similarity Search + Geographic filtering
● Evaluation: Compare Map with manual FB-crawling
● Future Work:
○ Expand the amount of external sources
○ Search for events by (facebook) place’s

Features
Map View for Events
● Rationale: Visualization
● Motivation: Replace Lists
● Scoping:
○ Show events for one day/week
○ Show limited number of events
○ Show only one event at one location
● Evaluation: Usability Performance
● Future Work:
○ Display more Event Information
○ Make timeframe configurable
○ Advanced Clustering

Technology
http://eventexplorer.openshift.com/
http://graph.facebook.com/
http://api.meetup.com/open_eventshttp://api.eventful.
com/json/events/search
https://www.eventbriteapi.
com/v3/events/search/
http://ws.audioscrobbler.com/2.0/?
method=geo.getevents

GuideMeNow
Your Social Tourist Guide
Ali Harrak: Front-end
Yassin el Aajati: Business Case
Abdelilah Mounir: Back-end
Team 8

PROBLEM STATEMENT
Destinations compete for tourists in a very competitive environment (Kevin
K.F Wong, 2001)
It is observed that major tourist problems are deviation in the
arrangements made for their stay, visit, transport and unexpected
expenses. (Chockalingam Ganesh, 2010).

TOURIST INDUSTRY IS VITAL
one in 11 jobs globally 9 % of the world’s economy
World Travel Tourism Council (WTTC), 2012

Data Analysis
● Cluster Analysis
● Classification Analysis

FEATURES
Collect ideas for your trips and getaways
● Nearby recommendations at-the-spot
● Discover the experiences from the social community

REFERENCES
Ap, J., Wong, K. K. (2001). Case study on tour guiding: Professionalism, issues and problems. Tourism Management, 22(5),
551-563.
Ganesh, A. A., Chockalingam, M. (2010). Problems encountered by tourists. Business and Economic Horizons, (03), 68-72.

Routist
Group 9
Bas Sijtsma, Hayo Bart Stefan Paap
March 19th
, 2015

✓ Idea
○ Visualizing movements between points of interested
throughout Amsterdam
○ Based on Flickr and Foursquare data
✓ Purpose
○ Describe movements of people throughout the city
○ Display interaction of venues with one another
✓ Target Group
○ Businesses (e.g. marketing)
○ Tourist (e.g. provide insight, discover hot spots,
assist trip planning)
Concept

Visualisation
✓ Venue exploration
✓ Movement exploration
✓ Filtering
○ On time period
○ On tourist nationality

Data
✓ Foursquare
○ Points of Interest: ~ 800 venues
✓ Flickr
○ Photos containing geo-tags: ~82.000
○ Movements between points of interest
➢ Issue
○ Flickr users ‘location’ field (country of origin) is
unstructured
○ Solution: Geocode Location Lookup API

Data Analysis
✓ Clustering
○ Soft assignment of photos to venues
○ Aggregation of individual movements into
aggregated movements
✓ Patterns
○ Identification of movements of individual tourists
between venues

Group Effort
✓ Bas
○ Back-end development; data processing
✓ Hayo Stefan
○ Front-end development; visualizations in d3

TripReco!
Martin Altmann
Sebastian Hoffmann
Hsu-Young Ho

Agenda!
•  Introduc*on

•  Model
of
TripReco

•  Demo

•  Discussion

– Limita*on

– Future
feature

About TripReco!
•  TripReco
aims
to
help
users
to
ﬁnd
the

popular
places

•  Using
the
friends´
photos
which
shared
on

thier
Facebook
and
Instagram

•  TripReco
displays
the
overview
in
the
whole

map
(which
Facebook

Instagram
do
not
do)

§  Find
the
paFerns
of
diﬀerent
genders

Model!
Data
processing

Data
analyzing

TOP
10
places

Data
Visualizing
Data
gathering

•  Facebook
API

✓  Friends

✓  Gender

✓  Photos

✓  ID

✓  Created_*me

✓  Name_tags

✓  Place

✓  Source

✓  Link

•  Instagram
API

✓  Rela*onships

✓  Follows

✓  Media

•  Geo
informa*on

•  genderize.io

✓  Gender
of
Instagrams’

followers

•  Top
popular
places
ranking
by
friends

•  Categories
of
gender

•  Map
the
geotagged
photoz

Model!
•  Facebook
API

✓  Friends

✓  Gender

✓  Photos

✓  ID

✓  Created_*me

✓  Name_tags

✓  Place

✓  Source

✓  Link

•  Instagram
API

✓  Rela*onships

✓  Follows

✓  Media

Model!
•  Geo
informaAon

•  genderize.io

✓  Gender
of
Instagrams’

followers

Model!
•  Top
popular
places
ranking

by
friends

•  Map
the
geotagged
photos

on
the
map

•  Categories
of
gender

Limitation!
•  Facebook
API

✓ The
endpoints
of

friends´
photos
only
allowed
the
photos

which
friends
were
tagged
in.

✓ Retrieved
the
photos
from
the
albums
do
not
provide
the

informa*on
of
la*tude

longitude.

•  Instagram
API

✓ The
endpoints
of
follower
do
not
include
the
informa*on

of
media

✓ The
endpoints
of
media
only
can
retrieve
the
public
user

ID
even
though
it
it
is
follower

•  Gender

ü The
informa*on
of
instagram’s
ﬁrst
name
does
not
match

genderize.io

Work!
•  Mar*n
Altmann

–  Data
analysis

–  Web
applica*on

•  Sebas*an
Hoﬀmann

–  Data
collec*on

•  Hsu-‐Young
Ho

–  Data
collec*on

–  Slides

The Social Museum
Johan Assink
Marc Jacobs
Nicky van Oorschot
Team 11

Problem
- Museum Guestbook
- Overview of museums in NL
- Combination of both problems result in:
The Social Museum

Solution
● Museums overview sort by county.
● Additional information for each museum.
● Tweets as guestbook notes.
● Slider that shows popularity over time.
● Museum recommendations.
● Analyse popularity and social Phenomena.

Datasets
The Social Museum
DBpedia
WikipediaTwitter
Yelp
Google

Demo time!
https://www.youtube.com/watch?v=BuKDCQXyooIfeature=youtu.be

Acknowledgments
Johan Marc Nicky
Timeline Tweet Crawler Counties
Switching leaflet maps Twitter integration Museum Description
(DBPedia)
Coffee provider Yelp Integration Museum Recommendation
DBPedia integration

Social Web
Group 12
Baraa Alnawakil
Amir Hossein Zadeh
Ioannis Markopoulos

Goals
- Help users find real-time events based on location of their preferences
- Provide information about events to help users decide on which
events to go to
Real-time events finder app

● Facebook API
○ Event Name
○ Description
○ End time
○ Place
○ attendees
● Twitter API
○ tweets about the event
Data Used

● Ranking Algorithm based on tweet texts analysis.
○ categorise tweet words in 5 groups ( very good, good , normal, bad, very bad).
○ assign weight measure for each group.
○ calculate the frequency of words in each group.
○ calculate the rank based on frequency and weight.
● Classification based on location of attendees
○ classify locations based on the cities in which the attendees currently live.
○ find number of attendees per city and calculate percentage.
Type of data analysis

The
Social
Playlist

Group
14

Eric,
Peter,
Lara

Paul

Group 14
The Social Playlist
Eric, Peter, Lara Paul
+

The
Social
Playlist

Group
14

Eric,
Peter,
Lara

Paul

The Social Playlist

The Social Playlist
Party
9me!

Genre
Age
Artist
Tracks

Event:

SocWeb2015

Party
9me!
Home

Age

Unknown
1

Till
16
0

16
9ll
18
0

18
9ll
25
1

25
9ll
30
1

30
9ll
40
0

40
and
older
1

The Social Playlist

Party
9me!

Genre
Age
Artist
Tracks

The Social Playlist

Party
9me!
Home

Genre

Pop
10

House
8

Electro
House
6

Edm
3

Permanent
Wave
2

R

B
2

Neo
Soul
2

Dutch
House
2

New
Wave
1

The Social Playlist

Party
9me!
Home

Ar9st

Bakermat
4

Mar9n
Garrix
4

ATer
House
FlicFlac
3

Serif
Chase
3

John
Legend
3

Nicky
Romero
1

One
Direc9on
1

Jus9n
Bieber
1

The Social Playlist

Party
9me!
Home

Event

Ar9sts
in
this
selec9on

Include
top

Number
of
tracks
per
ar9st
(1
–
10)

25

20

2

The Social Playlist

The
Social
Playlist

Group
14

Eric,
Peter,
Lara

Paul

The Social Playlist
Group 14
Features:
•  Select age group
•  Select genre
•  Select artists
•  Select tracks and create playlist

Trendng
Gambling on the current trends on Twitter
Florian Golemo Marjeta Markovic Kevin Wezeman
19-03-2015

Idea
Twitter Trends + Stock Market + Game
(think binary option trading)

10 twitter trends
60s to place a bet
42$

10 twitter trends
60s to place a bet
then cashout
next round

Idea - Incentive (Moneh moneh)
● now: just virtual money
● future: bitcoins?

Datasources
● Twitter:
○ REST: Top 10 worldwide trends every 5min
○ Streaming: all tweets for those 10 hashtags
● What The Trend:
○ Description for trends

The Interaction
● Every minute 1 bet
● Up/down
● Higher payout for higher risk
● Tweet link for each hashtag

The Implementation
● Server-side:
○ NodeJS
○ Socket.io
○ Twitter OAuth
● Client-side:
○ AngularJS

Extra Ideas
● add map, chose regional trends

Who does what
● The game: Florian Marjeta
● The report: Marjeta Kevin

Team 16
Erik Lubbers
Christian Heymans
Juan Manuel Bedregal
FAN FAVORITE
ACTORS
The Social Web - 2015

IDEA
• Present relevant general and social information related to a specific actor‟s and
his/her movies
• Genre
• Year
• Rating
• Likes
• People Talking
about them

TARGET USER
• Anyone interested in viewing the social
information related to the movies of a specific
actor
• Anyone interested in viewing a movie from a
specific actor.

USE
• Navigate through the movies of a specific
actor, explore movies with similar:
rating/point in time/likes/people talking
about
• The information and visualizations provided
by the application, can be used to help the
user choose a movie from an actor

DATA SOURCES
• ranker.com API
• (top 250 voted actors)
• dbpedia.org API
• (actor information and movie list)
• facebook API
• (likes and people talking about it)
• omdb API
• (IMDB rating and genre of the movies)

HOW IT WORKS 1/2
1. Retrieve the actor from our Database and list it on the applications searchbox,
ranker.com
2. Search for the actor in dbpedia and retrieve:
• photo
• summary
• movie list
3. Query Facebook
• query movies = Facebook return list of pages
• Clean and Filter Data: search for „movie‟ category, query Facebook for more
information, retrieve actor list and compare
• get likes and people talking about the movies

HOW IT WORKS 2/2
4. Query OMDB
• query movies = IMDB return list of movies
• Clean and Filter Data: search list of movies for actor list and compare
• get first genre and rating
5. Create a new dataset with the summarized information:
• Movie rating X Years X Genre
• Genre X Rating X Number of movies
• Movies X Likes X People talking X Rating X Genre
6. Draw the visualizations

LIMITATIONS
• Inconsistency of the data
• Different spelling of Movies
• Incomplete information
• Missing: category, cast info, additional
information
• This affected the overall reliability of the
results

INDIVIDUAL WORK
• CHRISTIAN
• Overall Design
• Styling of the application
• Integration and coding of the Facebook and OMDB api‟s
• ERIK
• Overall Design
• Finding and integrating the ranker actor list
• Integration and coding of the DBPedia querying
• Tuning the complete project to work as a whole
• JUAN
• Overall Design
• Prepare the summarized data for the Visualizations
• Design and Code the Visualizations

CONCLUSIONS
• It is feasible to gather social Information from different sources, and analyze in
order to create a bigger understanding of a particular subject.
• Important to define first the “problem to solve” and then search for the Data
Sources.
• Difficulty to Mine information from Social API‟s
• Inconsistency and Incompleteness of Information

THANK YOU
QUESTIONS ?
visit us @:
find-it.nl/ffactors

#Tweebay
Group 17: Wojciech Sidor, Janusz Kukla, Elinesofie Dolhain

Added value
•  Connect buyers / sellers on Twitter
•  Opportunity to compare via eBay
•  Search by ratio
2

Opportunities
•  Number of tweets with geolocation
▫  # Tweebay
▫  Other ways to find location on tweets
•  Streaming instead of search API
•  Search by date / period of time
•  SPAM filter
4

Individual slide (1) Wojciech
•  Free eBay
•  Geodata – not only a threat
•  Twitter is omnipotent
5

Individual slide (2) Elinesofie
•  Connect users via geolocation (real-time update)
for selling / buying products
•  Easy to sell home-made products
•  #Tweebay as evaluation
•  Improvements on user evaluation
6

Individual slide (3) Janusz
•  Pre-attentive human analysis of the data
•  Harder to draw any conclusions from the raw
data
•  The possibility to add more data in order to help
users to get in touch makes a new type of society
– Internet society
7

Meet
In
Middle
Alsjeblieft
Social Web 2015
Anthony Nwosisi
Aron de Vries
Roberto Floris
Group 18
19 March 2015

Application Introduction
• Humans are social beings
• Socializing involves meeting
• Meeting could be very hectic
• Friends, family or colleagues might be reluctant to travel
• MIMA proposes a solution – the Dutch Solution
• MIMA is based on shared distance between two points
• The application is using the Google Maps Javascript API

Application works with:
• Full address
• A postcode
• City
• Country
• Objects

• Two locations: Amsterdam and Alkmaar
• App calculates distance using a straight line (Purple
in the Diagram)
• The App also depicts the road landmark between
the two points (the blue line)
• The middle point is the point with the bouncing
object.
• Makes a circle at the middle with a radius of 1
kilometer
http://garagejackbakker.nl/sw

Read A Movie
A Book Movie Recommendation Application
Image from : http://www.fanpop.com/clubs/reading/images/27819134/title/read-book-photo Group 19

Idea
• Recommend books based on the movies and books that users and their Facebook
friends like + Goodreads ratings
• Recommend movies based on the movies and books that users and their
Fecebook friends like + Goodreads ratings

Motivation
• Movies are “fast” (we can see more films than read books):
o “Cold start”
o Broader exploration - easier to identify new fields of interest
• Books are “deep”
o Time investment is greater and people choose carefully what to read
o There are good films without stars and famous directors, there are hardly any good films
without a good story
• Naturally connected
Many movies are based on books and many screenwriters write books

Resources
• Facebook API
Likes about movies and books
• Goodreads API, Listopia
Book ratings
• imdbapi
Genre and screenwriter properties

Under Construction Issues
• Privacy - not possible to harvest friends
likes without Facebook’s explicit
permission
• Incompatibility - Goodreads API works only
with XML format (not even DOM)
• Listopia - blocked
• Only one way queries allowed genre =
author

Demo
https://www.youtube.com/watch?v=SgLTt2V2kcgfeature=youtu.be

Who did what
• Sander – development
• Aneta – concept and research
• Sergio – XML to JSON parser development

1
Company Proprietary and Confidential Copyright Info Goes Here Just Like
This
Hay fever application
Group 20
Sietse Huisman
David Lopez Mejia
Gert‐Jan de Graaf
Vrije Universiteit – the social web

2
This
• Extract tweets related to hay fever
• Convert number of tweets to heat map of the Netherlands
Issues
• 1% of tweets has geo location available
• Number of tweets is scarce
Initial idea

3
This
Improved idea - HooikoortsBot
• Flow
– Extract hay fever tweets with key word extraction
– Use sentiment analysis
• Positive
• Negative
– Tweet back to the person “Solution / Preparation”
– Data visualization

4
This
Data analysis – key word extraction
• Alchemyapi

5
This
Data analysis - Sentiment Analysis
Positive sentiment
Negative sentiment

6
This
Presenting the HooikoortsBot!

7
This
Example

8
This
Visualization
Sentiment of tweets
Positive Neutral Negative
0
2
4
6
8
10
12
14
runny nose itchy eyes dry throat clogged ears groggyness
Symptoms described by users with
sentiment analysis
Positive Neurtal Negative

9
This
Future work
• Heat map
• Connect twitter users with similar
symptoms

Linked movie location
Group 21:
Fleur, Elmar Arnold

Planning
● Introduction - the initial idea
● Our application
○ The idea
○ Screenshots
● Acknowledgements
● Conclusion
● Questions

Introduction - the initial idea
● Movies from the Facebook API
○ Description Actor from DBpedia
● Maybe add:
○ Recommendations based on genre + director
○ Film location from Linkedmdb plotted on Google
maps with Sgvizler

Our application - the idea
● Lookup actor information
○ DBpedia + Sgvizler on HTML/PHP
● Film information from Assignment 3 is used
● Login with facebook

Acknowledgements
Arnold: Presentation, Application, Documentation
Elmar: Documentation, Application
Fleur: Presentation, Documentation

Conclusion
● Linkedmdb is buggy and the right
information was difficult to retrieve.
● We are not developers, so building an
application was hard.

MovieVis
Visualization of opinions and
sentiment of movie reviews
Group 22:
Adriatik Bedjeti
Boris de Groot
Edgar Weidema

MovieVis: Why?!
Motivation
● Movie ratings are not
enough
● Movie reviews are long
and countless
● Movie reviews contains
spoilers
Goal
● Analyse sentiment in
movie reviews
● Analyse movies reviews
for subjective terms that
describe, but do not give
away, the movie
● Get user opinions and
compare them to public
data

What data
Crawl user reviews from IMDb and Metacritic

Sentiment Analysis
● Analyse sentiment in user
reviews
● Categorize sentiment
● Visualize the results to
the user

Gather User Opinions
● Data-preprocessing:
clearing the data
● Get the most used
descriptive terms used
● Visualize the results to
the user Can you guess this 2015 movie?

Individual Work
● Adriatik: Project idea, visual design, theoretical
background
● Boris: Sentiment analysis User opinion programming
● Edgar: IMDB Crawling, MetaCritic Crawling, Data
preprocessing

Duration of
trending topics
Group 23
Felicia Hotie
Mark van der Laan
Marc Went

Introduction
Aim: Gain insight in topic trending between different social media
platforms and a historic social media source
Find answers to quenstions like:
Are trending topics of shorter relevance in comparison to 20 years ago?

Clustered by social media source
● The Digital City (1995)
● Reddit
● Twitter

Approach
● How it works:
- Retrieve posts from the 3 sources with a search query
- Sort by date
- Count frequency of posts per date
- Visualise in graph
● Clustered by topic (query) and social media source

Interface
● Mine data using the Reddit API and Python
● Mine data from DDS using a self created parser
● Mine data from Twitter, first prototype is manual labor
● Interface created with HTML5
● Graph plotted with JavaScript

Evaluation / improvements
● Limitations:
- Reddit API allows only 100 search results
● Future work:
- Add more data to compare. E.g. compare word frequencies per topic and
source
- Add more social media sources

The app
! Its a forum that shows info of a TV show, where
people can give their opinions about it
! To see a discussion you must have an opinion
toward the particular TV show
! People get more points when they make more
posts, and when they become more influencial

Data
! Create an RDF store using Tomcat
! Storing all the stuctured used inthe application for
future reference
! Data sources
! DBpedia
! Information about TV show for reference
! Twitter
! Positive and negative tweets related to the show

Analysis
! Co-occurrence of words on posts
! Influences Patterns
! Track users attitude towards the different shows
! Track general opinion and discussion intensity
! Track general opinion on Twitter periodically
! *Possible in another social network

Visualization
! Visualize influence related to others
! Array which shows position towards each TV show by
the user --- compare it with general position array,
and twitter array
! Array with frequent words ---- compared with general
frequent

! Track appearance of new words
Visualization

Group 26 Recommendation App
Emmanouil Pavlidakis
eps780
Jaideep Khandelwal
jkl650
Andreas Manios
ams620
Course: Social Web 2015
Group : 26
1

What is our app?
 It is a Facebook Application.
 Its purposeto provide personalized recommendations for movies basedon the similarity score.
 Differences betweenour applicationand other movie recommendation sites.
 The rating for each movie is unique for a particular user and that ratingwill be based on his preferences and his personal profile.
 The recommended movies will be a selection of movies based on the similarity score of two or more users.
 The user can register and login with his Facebook account.
 At the registration the app asks for permissions and it imports some personalinformation and the movies he has liked.
 Afterthe registration the useris asked to select genre/s .
 Basedon the genrehe/she has selectedand the movies from FB, a list of movies is displayed to him. This list contains :
1. Movies that the user likes in his FB account.
2. The most popular and highly rated movies that belong to the genres that he/she has selected.
 Finally, the user is asked to rate this list of movies.
2

What is the similarity score?
 From theseinformation (movies ratings) a personalprofile has been createdfor eachuser.
 By the use of it the App is able to calculate the similarity score between users.
 After the registration and the creation of the Personal profile, the user is always able to rate new movies that he/she sees in his timeline.
 As a result the similarity score becomes more accurate.
 The similarity score is a percentagethat displays, in which degreethe user that has logged-in, has similar or identical preferences in movies with
other users.
 It is calculated based on the movies that users has rated.
 And it will be different for each user or for a pair of users .
 E.g. if user_a has similarity score 80% with user_b, user_b may have 60% similarity score with user_a.
 In the case that the similarityscore of the logged-in useris 0 (worst case), the system recommends to that userthe most popularmovies based
on the selection of genre.
 As a result the user has more involvement with the system.
3

Social Aspect
 The logged-in user :
1. Sees a list of the users based on high similarity score.
 He/she is able to follow those users, see their profile and create one wayrelationship.
 Also, the logged-in user is able to see the movies that these users has ratted.
2. Following users.
 The logged-in useris able to follow other users.
 As a result the user:
 Can get notificationsif theseusersrate a new movie.
 Can sendthem messages.
 Can ask them for recommendations.
3. Get Recommendations about new movies that other users has rated.
 These recommendations will be basedon the users that the logged-in userhas high
similarity score.
4

Screen Casts of the App 1/2 6
Login and Registration by FB account
The userafter the registration selects genres
The user is asked to rate movies based on the genres that he has selected

7Screen Casts of the App 2/2
After the completion of the movie profile the user is able to get recommendations for movies.
See users with high
similarity score
Follow them and
view their ratings Get recommendations
based on the similarity
score
Change the rating
See the users that
he follows
Contact users that
he follows

Individual Work
 Andreas Manios
 Front End
 Emmanouil Pavlidakis
 The theoretical part of the report and the creation of the graphs.
 Jaideep Khandelwal
 BackEnd
 Links
 Backend code : https://github.com/jdk2588/socialweb
 Application Link : http://dessad.altervista.org/yars/main.html
8

COMPARING USAGE OF TAGS PER
COUNTRY IN A SPECIFIC TIME PERIOD
Group 27: Annelore Franke, Daniel Gallo, Lars Rouvoet and Reza Mahmood Khalesi
The Social Web 2015

COMPARING USAGE OF TAGS PER COUNTRY IN A SPECIFIC TIME PERIOD
#happy
worldwide
period of time
1) Location Clustering:
Hashtag behaviour by
country
2) Hashtag behavior
worldwide over time analysis
#happy
#sad
The Netherlands
#galaxy
#iphone
#Bavaria
#Heineken
3) Comparisons between
hashtags by country
period of time

Web InterfaceUser
Twitter API
Web Framework
APPLICATION ARCHITECTURE
… and other sources

DEMO SCREENCAST NO.1
Hashtag behaviour by country:
Web Interface

Hashtag behaviour by country:
Results of #happy visualized
by country

Comparison of 2 hashtags worldwide:
Web Interface

Comparison of 2 hashtags worldwide:
Results #bavaria and #heineken worldwide
Bar chart Line chart

Comparison of 2 hashtags in a specific country:
Web Interface

Comparison of 2 hashtags in a specific country:
Results of ‘happy’ and ‘sad’ in The Netherlands
Bar chart Line chart

THANK YOU FOR LISTENING
ARE THERE ANY QUESTIONS?
Group 27: Annelore Franke, Daniel Gallo, Lars Rouvoet and Reza Mahmood Khalesi
The Social Web 2015

Social Web @VU - Student Presentations on Housing, Events & Tourism Apps

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