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Introduction
Footnotes
Acknowledgements
Sreejata Chatterjee ([email protected])
Faculty of Computer Science, Dalhousie University, Halifax, Canada
[1] Mashable Social Media: http://mashable.com/2011/09/08/twitter-has-100-million-active
[2] Social Media Lab: http://socialmedialab.ca/?p=1952
[3] Wired.com: http://www.wired.com/wiredscience/2010/10/twitter-crystal-ball
[4] Radian6: Social Media Monitoring and Engagement, Social CRM
There are huge amounts of real-time social media data
being created every moment. For example, ~230 million
tweets are posted daily by Twitter’s 200 million users [1].
If harnessed, it can provide a great wealth of insight into
what people are thinking about and what they like or
dislike. For instance, Twitter data has already proven to
be useful in a number of different contexts: monitoring
elections [2] to predicting stock market trends [3] to
conducting brand monitoring and PR campaigns [4].
However, social media data tend to be noisy and
ephemeral. Furthermore, social media companies often
limit the amount of data one can access automatically at
any point of time, making this rich source of transient
data difficult to collect.
This work focuses on designing and developing
automated methods and a web-based infrastructure that
can help other researchers and developers to collect
and process raw social media data by:
(1) Creating a Data Collector and Repository Tool
for collecting and storing public Twitter data for a
specified group of online users in an effective and
efficient manner,
(2) Connecting open APIs via Web Services which
process Twitter to add value and richness to the
Twitter data in our database, such as geo-coding or
assigning “influence” scores to Tweeters,
(3) Creating an NLP (Natural Language Processing)
Module that can conduct sentiment analysis on
social media data,
(4) Providing a robust API that other developers can
use to create and test innovative web applications
with the data collected.
I would like to thank Dr. Anatoliy Gruzd, Director of the Social Media Lab, for
supervising this research. Additionally, I would like to thank Philip Mai,
Research Manager at the Social Media Lab for his valuable feedback.
System Architecture for Handling Social Media Data
getAllTweet - Return all the tweets by all the users
getUserTweets - Returns tweets posted by a specified user
getTimedUserTweets - Returns tweets within a time interval
getUserProfilePicUrl - Returns user’s profile picture
getUserDetails - Returns detailed user information
getUserTimeLineInfo - Returns basic user information
API calls are made via HTTP requests (see below).
The output is formatted in JSON (JavaScript Object
Notation).
1) Gets all tweets that have been posted between Feb 14 -
April 14, 2012, by all of the users who follow “asist2011” and
“asist_org”:
http://URL_BASE/tweetApiCalls.php?call=getAllTweets&
seedUserList=asist2011,asist_org&startTime=2012-02-
14&endTime=2012-04-14
2) Returns details about dalprof’s profile such as profile info,
followers, friends, Klout score (influence score), geocoded
location – for easy and universal location identification
http://URL_BASE/tweetApiCalls.php?call=getUserDetails
&user=dalprof
GRAND Projects:
• DINS - Digital Infrastructures: Access and
Use in the Network Society
• NAVEL - Network Assessment and
Validation for Effective Leadership
Netlytic – a system for
automated discovery, analysis
and visualization of information
about online communities, being
developed by Dr. Gruzd at the
Dalhousie University Social
Media Lab.
Example 2: Tag Cloud of Top 30 Topics derived from
Positive (left) and Negative (right) Tweets about #OccupyWallStreet
Example 1: A Visual Representation of the Sentiment Analysis
made possible by the new NLP Module now available in Netlytic
As a proof of concept, the new NLP Module, based on the
Natural Language ToolKit (NLTK), has been added to an existing
web tool called Netlytic, giving it the ability to provide sentiment
analysis.
Sentiment Analysis of >70K Tweets
about #OccupyWallStreet
Conclusion: Overall, tweets about
the Occupy Wall Street movement
were more positive than negative.
Case Studies #2: Netlytic.org
Sample API Calls
Research Objectives
Case Studies #1: AcademiaMap.com
AcademiaMap-Dashboard App
AcademiaMap-GeoVisualizer App
AcademiaMap helps scholars to filter
the “noise” from their Twitter streams
using various "influence" metrics and
provides them with an easy way to
identify trending topics and interesting
voices to follow on Twitter.
(Lead developer: Melissa Anez)
A Geo-based Visualization system
that displays communication
connections between scholarly users
of Twitter from across the globe.
(Lead developer: Jamiur Rahman)
AcademiaMap - Twitter App
The API developed as part of this project is currently being
used in a few different applications for a system called
AcademiaMap, an Online Influence Assessment App
designed for scholars.
A Twitter app that automatically posts
tweets about trending topics and re-
posts tweets that are popular within a
group of scholarly Twitter users.
(Lead developer: Sreejata Chatterjee)