Data-mining twitter and the autism spectrum disorder: A Pilot study

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

[1]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[2]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[3]  G. Eysenbach,et al.  Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak , 2010, PloS one.

[4]  Jeong Yeob Han,et al.  Cancer Talk on Twitter: Community Structure and Information Sources in Breast and Prostate Cancer Social Networks , 2014, Journal of health communication.

[5]  Brendan T. O'Connor,et al.  Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters , 2013, NAACL.

[6]  A. Wakefield,et al.  Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. , 1998, Lancet.

[7]  A. Sabrá,et al.  Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children , 1998, The Lancet.

[8]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[9]  Xiaozhong Liu,et al.  Mirroring the real world in social media: twitter, geolocation, and sentiment analysis , 2013, UnstructureNLP@CIKM.

[10]  Adam D. I. Kramer,et al.  Autism online: a comparison of word usage in bloggers with and without autism spectrum disorders , 2009, CHI.

[11]  Michael D. Barnes,et al.  Tracking suicide risk factors through Twitter in the US. , 2014, Crisis.

[12]  Johan Bollen,et al.  Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena , 2009, ICWSM.

[13]  Christophe G. Giraud-Carrier,et al.  Identifying Health-Related Topics on Twitter - An Exploration of Tobacco-Related Tweets as a Test Topic , 2011, SBP.

[14]  Benyuan Liu,et al.  Predicting Flu Trends using Twitter data , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[15]  Paul G. King,et al.  A prospective double-blind, randomized clinical trial of levocarnitine to treat autism spectrum disorders , 2011, Medical science monitor : international medical journal of experimental and clinical research.

[16]  Albert Bifet,et al.  Sentiment Knowledge Discovery in Twitter Streaming Data , 2010, Discovery Science.

[17]  Christopher M. Danforth,et al.  The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place , 2013, PloS one.

[18]  Tiejun Zhao,et al.  Target-dependent Twitter Sentiment Classification , 2011, ACL.

[19]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[20]  B. L. Beattie,et al.  Aging 2.0: Health Information about Dementia on Twitter , 2013, PloS one.

[21]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[22]  Claire Cardie,et al.  Early Stage Influenza Detection from Twitter , 2013, ArXiv.

[23]  E. Larson,et al.  Dissemination of health information through social networks: twitter and antibiotics. , 2010, American journal of infection control.

[24]  Guandong Xu 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, August 17-20, 2014 , 2014 .

[25]  Robert Power,et al.  An Evidence Based Earthquake Detector using Twitter , 2013, LPCI@IJCNLP.

[26]  Leysia Palen,et al.  Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency , 2011, ICWSM.

[27]  E. Fombonne Epidemiology of Pervasive Developmental Disorders , 2009, Pediatric Research.

[28]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.