Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19

Among the critical challenges around the COVID-19 pandemic is dealing with the potentially detrimental effects on people's mental health. Designing appropriate interventions and identifying the concerns of those most at risk requires methods that can extract worries, concerns and emotional responses from text data. We examine gender differences and the effect of document length on worries about the ongoing COVID-19 situation. Our findings suggest that i) short texts do not offer as adequate insights into psychological processes as longer texts. We further find ii) marked gender differences in topics concerning emotional responses. Women worried more about their loved ones and severe health concerns while men were more occupied with effects on the economy and society. This paper adds to the understanding of general gender differences in language found elsewhere, and shows that the current unique circumstances likely amplified these effects. We close this paper with a call for more high-quality datasets due to the limitations of Tweet-sized data.

[1]  John S. Brownstein,et al.  Inferences about spatiotemporal variation in dengue virus transmission are sensitive to assumptions about human mobility: a case study using geolocated tweets from Lahore, Pakistan , 2018, EPJ Data Science.

[2]  Ryan L. Boyd,et al.  The Development and Psychometric Properties of LIWC2015 , 2015 .

[3]  Maximilian Mozes,et al.  Measuring Emotions in the COVID-19 Real World Worry Dataset , 2020, NLPCOVID19.

[4]  Gerardo Chowell,et al.  A Twitter Dataset of 150+ million tweets related to COVID-19 for open research , 2020 .

[5]  Andrew McCallum,et al.  Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.

[6]  Carla J. Groom,et al.  Gender Differences in Language Use: An Analysis of 14,000 Text Samples , 2008 .

[7]  Huan Liu,et al.  Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.

[8]  J. Kruschke Bayesian estimation supersedes the t test. , 2013, Journal of experimental psychology. General.

[9]  Gorka Navarrete,et al.  Bayesian Hypothesis Testing: An Alternative to Null Hypothesis Significance Testing (NHST) in Psychology and Social Sciences , 2017 .

[10]  John D. Lafferty,et al.  A correlated topic model of Science , 2007, 0708.3601.

[11]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[12]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[13]  E. Wagenmakers,et al.  Bayesian hypothesis testing for psychologists: A tutorial on the Savage–Dickey method , 2010, Cognitive Psychology.

[14]  K. Bowers,et al.  Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data , 2018 .

[15]  Kristina Lerman,et al.  COVID-19: The First Public Coronavirus Twitter Dataset , 2020, ArXiv.

[16]  Daniël Lakens,et al.  Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs , 2013, Front. Psychol..

[17]  Torrin M. Liddell,et al.  The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective , 2016, Psychonomic bulletin & review.

[18]  Margaret E. Roberts,et al.  stm: An R Package for Structural Topic Models , 2019, Journal of Statistical Software.

[19]  Fred Morstatter,et al.  Tampering with Twitter’s Sample API , 2018, EPJ Data Science.