Deriving insights from national happiness indices

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Title: Deriving insights from national happiness indices
Authors: Brew, Anthony
Greene, Derek
Archambault, Daniel
Cunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/3243
Date: 11-Dec-2011
Abstract: In online social media, individuals produce vast amounts of content which in effect "instruments" the world around us. Users on sites such as Twitter are publicly broadcasting status updates that provide an indication of their mood at a given moment in time, often accompanied by geolocation information. A number of strategies exist to aggregate such content to produce sentiment scores in order to build a "happiness index". In this paper, we describe such a system based on Twitter that maintains a happiness index for nine US cities. The main contribution of this paper is a companion system called SentireCrowds that allows us to identify the underlying causes behind shifts in sentiment. This ability to analyse the components of the sentiment signal highlights a number of problems. It shows that sentiment scoring on social media data without considering context is difficult. More importantly, it highlights cases where sentiment scoring methods are susceptible to unexpected shifts due to noise and trending memes.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2011 IEEE
Keywords: Visualisation;Data mining;Social network analysis;Twitter
Subject LCSH: Visualization
Data mining
Online social networks--Data processing
Happiness--Testing
Twitter
DOI: 10.1109/ICDMW.2011.61
Language: en
Status of Item: Peer reviewed
Is part of: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW) [proceedings]
Conference Details: Paper presented at the IEEE International Conference on Data Mining series (ICDM'11), December 11th to 14th, 2011, Vancouver, Canada
Appears in Collections:Computer Science Research Collection
Clique Research Collection
CASL Research Collection

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