Computational Paremiology: Charting the temporal, ecological dynamics of proverb use in books, news articles, and tweets

Ethan Davis, ∗ Christopher M. Danforth, 2, † Wolfgang Mieder, ‡ and Peter Sheridan Dodds 4, § Computational Story Lab, Vermont Complex Systems Center, MassMutual Center of Excellence for Complex Systems and Data Science, Vermont Advanced Computing Core, University of Vermont, Burlington, VT 05401. Department of Mathematics & Statistics, University of Vermont, Burlington, VT 05401. Department of German & Russian, University of Vermont, Burlington, VT 05401. Department of Computer Science, University of Vermont, Burlington, VT 05401. (Dated: July 13, 2021)

[1]  S. Naranan,et al.  Quantitative Linguistics and Complex System Studies , 1996, J. Quant. Linguistics.

[2]  Christopher M. Danforth,et al.  Is language evolution grinding to a halt? The scaling of lexical turbulence in English fiction suggests it is not , 2015, J. Comput. Sci..

[3]  David Bamman,et al.  The Transformation of Gender in English-Language Fiction , 2018 .

[4]  Christopher M. Danforth,et al.  Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution , 2015, PloS one.

[5]  Ricard V. Solé,et al.  Least effort and the origins of scaling in human language , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Erez Lieberman Aiden,et al.  Quantitative Analysis of Culture Using Millions of Digitized Books , 2010, Science.

[7]  Neal R. Norrick,et al.  How Proverbs Mean: Semantic Studies in English Proverbs , 1985 .

[8]  B. J. Whiting.  Modern Proverbs and Proverbial Sayings , 1989 .

[9]  Robert J. Smith,et al.  A mathematical model of Bieber Fever : The most infectious disease of our time ? , 2012 .

[10]  Lahomtoires d'Electronique.  AN INFORMATIONAL THEORY OF THE STATISTICAL STRUCTURE OF LANGUAGE 36 , 2010 .

[11]  Judith A. Hall,et al.  When Love Is Blind , 1976 .

[12]  Colin M. Van Oort,et al.  Simon's fundamental rich-get-richer model entails a dominant first-mover advantage. , 2016, Physical review. E.

[13]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[14]  G. Lakoff,et al.  Metaphors We Live by , 1982 .

[15]  David A. Allred.  "Proverbs Speak Louder Than Words": Folk Wisdom in Art, Culture, Folklore, History, Literature, and Mass Media , 2010 .

[16]  Val Hamilton.  The Dictionary of Modern Proverbs , 2013 .

[17]  Paul Swuste,et al.  Safety metaphors and theories, a review of the occupational safety literature of the US, UK and The Netherlands, till the first part of the 20th century , 2010 .

[18]  G. Cocho,et al.  Universality of Rank-Ordering Distributions in the Arts and Sciences , 2009, PloS one.

[19]  James Obelkevich.  Proverbs and Social History , 2015 .

[20]  Ricard V. Solé,et al.  Two Regimes in the Frequency of Words and the Origins of Complex Lexicons: Zipf’s Law Revisited* , 2001, J. Quant. Linguistics.

[21]  A. Prahlad.  Proverbs Are the Best Policy: Folk Wisdom and American Politics , 2009 .

[22]  Thayer Alshaabi,et al.  Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter , 2020, Science Advances.

[23]  Wolfgang Mieder,et al.  A Dictionary of American Proverbs , 1992 .

[24]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[25]  Timothy R. Tangherlini.  Big Folklore: A Special Issue on Computational Folkloristics , 2016 .

[26]  James Abello,et al.  Computational folkloristics , 2012, Commun. ACM.

[27]  Christopher M. Danforth,et al.  The emotional arcs of stories are dominated by six basic shapes , 2016, EPJ Data Science.

[28]  R. Moon.  Fixed Expressions and Idioms in English: A Corpus-Based Approach , 1998 .

[29]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[30]  Afsaneh Fazly,et al.  Unsupervised Type and Token Identification of Idiomatic Expressions , 2009, CL.

[31]  Timothy Baldwin,et al.  Multiword Expressions: A Pain in the Neck for NLP , 2002, CICLing.

[32]  James P. Bagrow,et al.  Zipf’s law holds for phrases, not words , 2014, Scientific Reports.

[33]  Heather A. Haas.  Proverb Familiarity in the United States: Cross-Regional Comparisons of the Paremiological Minimum , 2008 .

[34]  Ekaterina Shutova,et al.  Models of Metaphor in NLP , 2010, ACL.

[35]  Wolfgang Mieder,et al.  Proverbs Are Never Out of Season: Popular Wisdom in the Modern Age , 1993 .

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

[37]  Daniella Andersson.  Understanding Figurative Proverbs: A Model Based on Conceptual Blending , 2013 .

[38]  K. Lau,et al.  “ Cheaters Never Prosper ” and Other Lies Adults Tell Kids Proverbs and the Culture Wars over Character , 2022 .

[39]  B. Babcock,et al.  The Literary Use of Proverbs , 1977 .

[40]  R. Browne.  Proverbs: A Handbook , 2005 .

[41]  Gözde Özbal,et al.  Learning to Identify Metaphors from a Corpus of Proverbs , 2016, EMNLP.

[42]  P. S. Dodds,et al.  Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy , 2020, ArXiv.

[43]  Gözde Özbal,et al.  PROMETHEUS: A Corpus of Proverbs Annotated with Metaphors , 2016, LREC.

[44]  Yuen Ren Chao,et al.  Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology , 1950 .

[45]  David Cram,et al.  The Linguistic Status of the Proverb , 1983 .