Social Media & Public Opinion - Project Overview

HOME

TEAM

PROJECT OVERVIEW

FINDINGS

PROJECT MANAGEMENT

DOCUMENTATION

Proposal

Final

Introduction and Project Background

In the past decade, we have witnessed the rapid proliferation of social media worldwide. Since Twitter launched in 2006, the social networking microblogging service has grown rapidly to become the second largest social network after Facebook. Twitter now boasts 284 million monthly active users and they send out 500 million tweets per day as of December 2014. It has become a real-time information network generated by people around the world that let users share their thoughts about various topics in short updates or tweets in 140 characters of text or less. Twitter is growing the fastest in Asia Pacific and Singaporeans are one of the most active social media consumers in the world, with the world’s second highest social penetration rate in Singapore at 5%, more than double the global average of 26%. This represents a great source of data that we can analyse and derive valuable insights from.

However, harnessing big data is challenging as data lacks structure and context. Computers cannot deal with implicit information as well as humans do. This project aims at qualifying and quantifying the trends in human emotions expressed by Twitter users over a period of time via sentiment analysis, which is the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. In other words, it determines whether a tweet is positive, negative or neutral. This proves vital during the 2014 Israel – Gaza Conflict, with many taking to Twitter for real-time news and updates on the crisis. Majority found Twitter to be a powerful means of expressing their activism against Israel’s brutal campaign in the region. A deep sentiment analysis of social network data, such as Twitter, could lead to very interesting insights of global public opinion. In this conflict, it could help in engaging more people to help balancing the world’s public opinion, both during the fighting and after the cease fire. For our project, it is expected that the results are presented as a web-based visualisation that summarizes the trend of the happiness level over time and allows the inspection of factors associated with the happiness level at a certain point in time. In addition, it should provide the end-users with various drill-down features to choose from while interacting with the end system.

Motivation & Project Scope

Being able to identify what make people happy is arguably one of the most important parts of socio-economic development. Increasingly, many public-opinion polls and government agencies have asked citizens the questions related to happiness and wellbeing in their surveys. In the Gallup poll in 2012, Singapore was ranked the most emotionless society and the least positive country in the world. What makes Singaporeans (un)happy? The goal of this project is to apply the use of social media to measure happiness as a less expensive (in terms of time and resources) method to traditional surveys. The motivation behind this project is to be able to visually represent the data that Living Analytics Research Centre (LARC) has collected from Twitter. The data-set provided comprises of social media data in form of tweets published by Singapore-based Twitter users over several months. The key scope to the project would be to create a dashboard that would distinctively represent the Twitter data to us. The Twitter data will consists of information that is provided Twitter, and on top of that, additional predicted user attributes. We will come up with their granular analysis of change in mood trends, periods of significance (may be weekends or any weekday) and other noteworthy actionable insights coming out of analysis done. The dashboard allows users to quickly view and understand what the data is telling them without delving into the data itself. The focus is therefore to create a replicable and scalable dashboard that can accommodate the large amount of data collected by the LARC team.

High-Level Requirements

The system will include the following:

• A timeline based on the tweets provided
• The timeline will display the level of happiness as well as the volume of tweets.
• Each point on the timeline will provide additional information like the overall happiness scores, the level of sentiments for each specific category etc.
• Linked graphical representations based on the time line
• Graphs to represent the aggregated user attributes (gender, age groups etc.)
• Comparison between 2 different user defined time periods
• Optional toggling of volume of tweets with sentiment timeline graph

Work Scope

• Data Collection – Collect Twitter data to be analysed from LARC
• Data Preparation – Clean and transform the data into a readable CSV for upload
• Application Calculations and Filtering – Perform calculations and filters on the data in the app
• Dashboard Construction – Build the application’s dashboard and populate with data
• Dashboard Calibration – Finalize and verify the accuracy of dashboard visualizations
• Stress Testing and Refinement – Test software performance whether it meets the minimum requirements of the clients and * perform any optimizations to meet these.
• Literature Study – Understand sentiment and text analysis in social media
• Software Learning – Learn how to use and integrate various D3.js / Hicharts libraries, and the dictionary word search provided by the client.

Methodology

The key aim of this project is to allow the user to be able to explore and analyse the happiness level of the targeted subjects based on a given set of tweets. Tweets are a string of text made up of 140 characters. Tweets may contain shorten URLs, tags (@xyz) or trending topics (#xyz) The interactive visual model prototype should allow the user to be able to see the past tweets based upon certain significant events and derive a conclusion from the results shown. To be able to do this, we will propose the following methodology. Tweet data will be provided to us from the user via uploading a csv file containing the tweets in the JSON format.

First, we will first display an overview of the tweets that we are looking at. Tweets will be aggregated into intervals based upon the span of tweets’ duration as given in the file upload. Each tweet will have a ‘happiness’ score tagged to it. “Happiness” score is derived from the study at Hedometer.org. Out of the 10,100 words that have a score tagged to it, some of them may not be applicable to words on Twitter. (Please refer to the study to find out how the score is derived). Words that are not applicable will not be used to calculate the score of the tweet and will be considered as a stop/neutral word on the application.

To visualise the words that are mentioned in these tweets, we will use a dynamically generated word cloud. A word cloud is useful in showing the users which are the words that are commonly mentioned in the tweets. The more a particular word is mentioned, the bigger it will appear on the word cloud. Stop/neutral words will be removed to ensure that only relevant words show up on the tag cloud. One thing to note is that the source of the text is from Twitter, which means that depending on the users, these tweets may contain localized words which may be hard to filter out. The list of stop words that we will be using to filter will be based upon this list.

Secondly, there is a list of predicted user attributes that is provided by the client. Each line contains attributes of one user in JSON format. The information is shown below:

• id: refers to twitter id
• gender
• ethnicity
• religion
• age_group
• marital_status
• sleep
• emotions
• topics

This predicted user attributes will be displayed in the 2nd segment where the application allows users to have a quick glance of the demographics of the users.

Third, we will also display the score of the words mentioned based upon the happiness level. This will allow the user to quickly identify the words that are attributing to the negativity or positivity of the set of tweets.

The entire application will entirely be browser based and some of the benefits of doing so include:

• Client does not need to download any software to run the application
• It clean and fast as most of the people who own a computer would probably have a browser installed by default
• It is highly scalable. Work is done on the front-end rather than on the server which may be choked when handling too many requests.

HTML5 and CSS3 will be used primarily for the display. Javascript will be used for the manipulation of the document objects front-end. Some of the open source plugins that we will be using includes:

• Highchart.js – a visualisation plugin to create charts quickly.
• Jquery – a cross-platform JavaScript library designed to simplify the client-side scripting of HTML
• Openshift – Online free server for live deployment
• Moment.js – date manipulation plugin

Deliverables

• Project Proposal
• Mid-term presentation
• Mid-term report
• Final presentation
• Final report
• Project poster
• A web-based platform hosted on OpenShift.

Limitations & Assumptions

Limitations	Assumptions
Insufficient predicted information on the users (location, age etc.)	Data given by LARC is sufficiently accurate for the user
Fake Twitter users	LARC will determine whether or not the users are real or not
Ambiguity of the emotions	Emotions given by the dictionary (as instructed by LARC) is conclusive for the Tweets that is provided
Dictionary words limited to the ones instructed by LARC	A comprehensive study has been done to come up with the dictionary

ROI analysis

As part of LARC’s initiative to study the well-being of Singaporeans, this dashboard will be used as springboard to visually represent Singaporeans on the Twitter space and identify the general sentiments of twitter users based on a given time period. This may provide one of the useful information about people's subjective well-being which helps realise the visions of the smart nation initiative by the Singapore government to understand the well-being of Singaporeans. This project may be a standalone or a series of projects done by LARC.

Future extension

• Scalable larger sets of data without hindering on time and performance
• Able to accommodate real-time data to provide instantaneous analytics on-the-go

Acknowledgement & credit

• Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM (2011) Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 6(12)
• Companion website: http://hedonometer.org/
• Schwartz HA, Eichstaedt J, Kern M, Dziurzynski L, Agrawal M, Park G, Lakshmikanth S, Jha S, Seligman M, Ungar L. (2013) Characterizing Geographic Variation in Well-Being Using Tweets. ICWSM, 2013
• Helliwell J, Layard R, Sachs J (2013) World Happiness Report 2013. United Nations Sustainable Development Solutions Network.
• Bollen J, Mao H, Zeng X (2010) Twitter mood predict the stock market. Journal of Computational Science 2(1)
• Happy Planet Index

References