Social Web 2014: Final Presentations (Part I)

Group 1

Features

a project by

tsw I group01

hardie I pinzi I piscopo

Concept

Target users

What> social profiles> user posts > user played music

Data set 1Facebook user

statuses and posts

Data set 2Last.fm listened

tracks

How> sentiment analysis> filtering> cross-correlation

Sentiment analysisColours encode

user’s mood

Listening prefsTracks played are shown

for each time slot

Playlist generationPlaylist generated

according to moods

Evaluation process> user study

Preliminary studiesUser profiling

Information needs

Low-fi prototypes

Hi-fi prototype

User evaluationOn a working prototype● Design evaluation● Information gains,

user relevance● Functionality

evaluation

Conclusions> critical aspects> future work

Moods detectionMinimum amount of data needed to reliably extract emotional patterns

Single sign onAt present, signing in each of the two

SNSs is needed

Moods detectionDatasets could be further expanded

and more elements analysed to detect users’ moods

Single sign onAuthentication through OpenId or

similar services should be implemented

Organisation> individual work

Graham HardieProgramming, data collection and data visualization

Viola PinziTheoretical analysis, visual design and data analysis

Alessandro PiscopoTheoretical analysis, visual design and data visualisation

Group 2

The Social ThermometerThe Social Web - VU University AmsterdamGroup 2: Adnan Ramlawi, Sindre Berntsen, Yaron Yitzhak

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

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

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.

Progress - What we have so far...

Acknowledgements:

All: brainstorming, reportYaron: data retrievalSindre: data processingAdnan: data visualisationAdnan, Yaron: presentations

Group 3

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

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

Goals

o Reduce the financial cost of exploration

o Motivate the traveler to explore new places

feeling safer

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

Our application : Sleep@Broad

Welcome page

Login


Friends’ location


Friend List


Friends in a specific location

Questions ?

Group 4

@ Twitter username:

ENTER

Group 4: Hassan Ali Annemarie Collijn Julia Salomons

Hashtags Research tool

Twitter Followers World Map

Twitter Followers Locations Map

Hashtag Word Cloud

Interactive word cloud based on hashtags

Link to tweets with the clicked hashtag (#whereihandstand)

Work Division

Hassan Ali Writing of Report Annemarie Collijn Development of App Julia Salomons Development of app

Group 5

Travel Together

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

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Motivation

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Approach

Magic

+

Travel Together Control Center

Building a community + reuse of existing data

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Approach

Magic

+



Friendlist

Working and living place

Opening hours

Realtime updates

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Approach

Magic

+



Working place

Opening hours

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Approach

Magic

+



Realtime Updates

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Approach

Magic

+



Working and living place

Workinghours

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Screenshots

Search-and

Displayoptions

Resultsection

Option to shareon Facebook

and Twitter

X-Ray Mode

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5Screenshots

Searchradius

Related Messages

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5ScreenshotsX-Ray Mode for easily finding matching routes

Achraf Belmokadem

Bernd Themann

Sheldon Pijpers

Group 5ScreenshotsAbility to contact friends

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

Thanks!

Group 6

Proofread PalGroup 6: Bob de Graaff, Justin Post and Melvin Roest

What is Proofread Pal?

The simplest and quickest way to have your documents proofread!

How does it work?

User Expertise

Matching algorithm● Similar domain knowledge● Similar personality profile● Similar “Proofread Pal” ranking

A match!

Let’s take a look

http://www.youtube.com/watch?v=6vXMwFtz7vI

What’s next?● Queue times based on ranking!● Text mining for better document

classification!● Weighted evaluation!

● Dolphins!

Thanks for listening!

Are there any questionsnot regarding dolphins?

Group 7

#Social Web 2014

#Group 7

#Benjamin Timmermans

#Rens van Honschooten

#Harriëtte Smook

#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

#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

#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

#screencast

Group 8

#analyzing Twitter’s Trending Topics

The Social Web, 2014

Group 8: Ans de Nijs, Matthijs Rijken, Lia Sterkenburg

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.

Analysis of existing tools

•  Twistori (sentiment keyword search) à

•  We feel fine (feeling analysis) à

•  I-logue (trending topic word cloud)

Data

•  Twitter Tweets (100s - 1000s) •  Text

•  Timestamps

•  Extract keywords

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

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

Group 9

Odd “like” out

Group 9

Lennert Gijsen, Mustafa Küçüksantürk & Ömer Ergül

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.

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.

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

Individual work- Explore possibilitiesOmer, Mustafa

- Retrieving and analysing Facebook dataLennert, Omer

- ProgrammingLennert, Mustafa

- TestingEveryone

Questions ?

Group 10

Rcmdr/UTV Timothy Dieduksman, Guangxue Cao, Adi Kalkan

Rcmdr/UTV, Group 10

IMake Problem: ●  Irrelevant

recommendations ○  Annoyed viewers

●  Goal: ○  Provide users

relevant recommendation

Data & Analysis

SCORE

Demonstration

Group 11

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

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

G11


Feature 1: Positive/negative/neutral classification (tweets)


G11 By Andreas Karadimas


G11


Feature 2: Location-based analysis


G11 By Luxi Jiang


G11


Feature 3: Positive/negative/neutral proportion analysis


G11 By Micky Chen


G11

Group 13

PoPlacesGroup 13:Thom Boekel, Rianne Nieland, Maiko Saan

popular places among your friends

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.

Data


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)

Approach

Data collectionGather friend

locations from Facebook

ProcessCategorize data on year

Filter out locations without latitude and

longitude

VisualizationHeatmap with markers Heatmap → number of

friends Markers → all locations


Visualization (1/2)


Visualization type:Google heatmap with location markers

Visualization of places: Locations marked with markers

Popularity of locations indicated with colors andradius

Visualization (2/2)


Options:

Filter locations by year

Heatmap options (e.g. radius)

Infobox with:

● information about the location provided by Wikipedia

● friend visits per year

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

Group 14

Predicting the local elections

with Twitterdata

GROUP 14

Mabel Lips

Marco Schreurs

Wouter van den Hoven

Data & Approach

• Our data

• Collection of tweets of political parties and prominent politicians

• Size of data: ~15.000

• Approach

• Sentiment analysis

• Normalisation

Purpose of WebApp

• Predict the outcome of the local elections

• People of Amsterdam interested in politics

• Unique:

• Using realtime Twitter data

• Normalisation

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

Individual work

• Wouter: Twitterdata retrieval

• Marco: Sentiment analysis of Twitter data

• Mabel: Algorithm sentiment analysis and normalization process

Group 15

Twitter Recommendation App

Group 15 - Niels, Dick & SarahMarch 2014

Goal

Discovering interesting Tweets, subjects and users.

System Overview

http://www.youtube.com/watch?v=j-hKJPVcv2M

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.

Semantic Markup

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

Feature Sarah

● Discovering and extracting recurring terms (i.e. common subjects)

● Categorization and visualization of interesting and uninteresting Tweets

Feature Niels

Recommending Tweets

● Part of the larger system● Basis for more features

Questions or Feedback

Social Web 2014: Final Presentations (Part I)

Technology

Transcript of Social Web 2014: Final Presentations (Part I)