Quantifying Regional Differences in the Length of Twitter Messages

The increasing usage of social media for conversations, together with the availability of its data to researchers, provides an opportunity to study human conversations on a large scale. Twitter, which allows its users to post messages of up to a limit of 140 characters, is one such social media. Previous studies of utterances in books, movies and Twitter have shown that most of these utterances, when transcribed, are much shorter than 140 characters. Furthermore, the median length of Twitter messages was found to vary across US states. Here, we investigate whether the length of Twitter messages varies across different regions in the UK. We find that the median message length, depending on grouping, can differ by up to 2 characters.

[1]  Alessandro Vespignani,et al.  The Twitter of Babel: Mapping World Languages through Microblogging Platforms , 2012, PloS one.

[2]  Christian M. Alis,et al.  Spatio-Temporal Variation of Conversational Utterances on Twitter , 2013, PloS one.

[3]  Xincheng Xie,et al.  Quantum phase transitions and coherent tunneling in a bilayer of ultracold atoms with dipole interactions , 2012 .

[4]  Tobias Faust Geographies Of England The North South Divide Material And Imagined , 2016 .

[5]  Christopher M. Danforth,et al.  Happiness and the Patterns of Life: A Study of Geolocated Tweets , 2013, Scientific Reports.

[6]  Joachim Mathiesen,et al.  Modular networks of word correlations on Twitter , 2011, Scientific Reports.

[7]  S. González The North/South divide in Italy and England: Discursive construction of regional inequality , 2011 .

[8]  Matjaz Perc,et al.  Inheritance patterns in citation networks reveal scientific memes , 2014, ArXiv.

[9]  Nick Chater,et al.  Using big data to predict collective behavior in the real world 1 , 2014, Behavioral and Brain Sciences.

[10]  Qi Wang,et al.  Quantifying Human Mobility Perturbation and Resilience in Hurricane Sandy , 2014, PloS one.

[11]  Philip Treleaven,et al.  Quantifying the Digital Traces of Hurricane Sandy on Flickr , 2013, Scientific Reports.

[12]  Björn-Olav Dozo,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010 .

[13]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[14]  M. De Domenico,et al.  The Anatomy of a Scientific Rumor , 2013, Scientific Reports.

[15]  G. Miller Sociology. Social scientists wade into the tweet stream. , 2011, Science.

[16]  Dirk Helbing,et al.  Saving Human Lives: What Complexity Science and Information Systems can Contribute , 2014, Journal of statistical physics.

[17]  P. Earle,et al.  OMG Earthquake! Can Twitter Improve Earthquake Response? , 2009 .

[18]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[19]  Guido Caldarelli,et al.  A Multi-Level Geographical Study of Italian Political Elections from Twitter Data , 2014, PloS one.

[20]  Xin Lu,et al.  Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami , 2014, Scientific Reports.

[21]  Brendan T. O'Connor,et al.  A Latent Variable Model for Geographic Lexical Variation , 2010, EMNLP.

[22]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[23]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[24]  J. R. Scotti,et al.  Available From , 1973 .

[25]  Kristina Lerman,et al.  The Simple Rules of Social Contagion , 2013, Scientific Reports.

[26]  H. Stanley,et al.  Quantifying Trading Behavior in Financial Markets Using Google Trends , 2013, Scientific Reports.

[27]  Tobias Preis,et al.  Adaptive nowcasting of influenza outbreaks using Google searches , 2014, Royal Society Open Science.

[28]  Christian M. Alis,et al.  Adaptation of fictional and online conversations to communication media , 2012, ArXiv.

[29]  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.

[30]  Sebastian Funk,et al.  Word usage mirrors community structure in the online social network Twitter , 2013, EPJ Data Science.

[31]  I. Buchan,et al.  Trends in mortality from 1965 to 2008 across the English north-south divide: comparative observational study , 2011, BMJ : British Medical Journal.

[32]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[33]  E. Algonquin Road QUARTERLY REPORT PURSUANT TO SECTION 13 OR 15(d) OF THE SECURITIES EXCHANGE ACT OF 1934 , 2014 .

[34]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[35]  Matjaz Perc,et al.  Self-organization of progress across the century of physics , 2013, Scientific Reports.

[36]  P. Murphy,et al.  EARNINGS, UNEMPLOYMENT AND BRITAIN'S NORTH-SOUTH DIVIDE: REAL OR IMAGINARY?† , 1995 .

[37]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[38]  John Knight,et al.  Oxford Bulletin of Economics and Statistics , 2006 .

[39]  Christopher M. Danforth,et al.  Positivity of the English Language , 2011, PloS one.

[40]  Katie Wales,et al.  North and South: An English linguistic divide? , 2000, English Today.

[41]  H. Eugene Stanley,et al.  Quantifying Wikipedia Usage Patterns Before Stock Market Moves , 2013, Scientific Reports.

[42]  H. Eugene Stanley,et al.  Quantifying the Advantage of Looking Forward , 2012, Scientific Reports.

[43]  Jeffrey T. Hancock,et al.  Experimental evidence of massive-scale emotional contagion through social networks , 2014, Proceedings of the National Academy of Sciences.

[44]  Kazuyuki Aihara,et al.  Quantifying Collective Attention from Tweet Stream , 2013, PloS one.

[45]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[46]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[47]  Guido Caldarelli,et al.  Web Search Queries Can Predict Stock Market Volumes , 2011, PloS one.