NDLI: Quantifying mood, content and dynamics of health forums

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Quantifying mood, content and dynamics of health forums

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Quantifying mood, content and dynamics of health forums

Content Provider	ACM Digital Library
Author	Curran, James Rivera, Ivan Warren, Jim
Abstract	In this paper we examined content, mood and general dynamics of health forum discussions concerning vaccinations, genetically modified organisms (GMO) and a gluten-free diet and explored the ability to extract sentiment from social media. Using data from the social media website Reddit.com, we applied text mining techniques together with machine learning algorithms to derive insights. We used metadata from the source, text features, Latent Dirichlet Allocation (LDA) topic model outputs and manually annotated disposition labels that separate comments into affirmative or negative groups together with Gradient Boosted Models (GBM) to devise a set of disposition models inferring commentators' sentiment towards each topic and expand our understanding of relevant arguments. Manual annotation resulted in moderate interrater agreement of an average 0.48 Fleiss-Kappa. Despite that, the disposition models for each topic were able to achieve a balanced successful prediction rates of between 68% and 74% providing a considerably better than chance assessment of a commentator's disposition towards each topic. We observed changes in disposition over time and found areas of disagreement between the supporters and opponents of each topic. Despite the limitations associated with manual annotations, we obtained a wider view on the issues concerning the topics of interest than those offered by previous research.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450340427
DOI	10.1145/2843043.2843379
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Reddit Classification Machine learning Gmo Natural language processing Opinion mining Vaccinations Gluten
Content Type	Text
Resource Type	Article

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