skip to main content
10.1145/2661829.2662090acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

What a Nasty Day: Exploring Mood-Weather Relationship from Twitter

Published: 03 November 2014 Publication History

Abstract

While it has long been believed in psychology that weather somehow influences human's mood, the debates have been going on for decades about how they are correlated. In this paper, we try to study this long-lasting topic by harnessing a new source of data compared from traditional psychological researches: Twitter. We analyze 2 years' twitter data collected by twitter API which amounts to 10% of all postings and try to reveal the correlations between multiple dimensional structure of human mood with meteorological effects. Some of our findings confirm existing hypotheses, while others contradict them. We are hopeful that our approach, along with the new data source, can shed on the long-going debates on weather-mood correlation.

References

[1]
C. A. Anderson. Heat and violence. Current Directions in Psychological Science, 10(1):33--38, 2001.
[2]
R. A. Baron and V. M. Ransberger. Ambient temperature and the occurrence of collective violence: the" long, hot summer" revisited. Journal of Personality and Social Psychology, 36(4):351, 1978.
[3]
E. Benson, A. Haghighi, and R. Barzilay. Event discovery in social media feeds. In ACL, 2011.
[4]
S. Bergsma, D. Lin, and R. Goebel. Web-scale n-gram models for lexical disambiguation. In IJCAI, 2009.
[5]
J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2011.
[6]
S. Chung and S. Liu. Predicting stock market fluctuations from twitter. 2011.
[7]
L. A. Clark and D. Watson. Mood and the mundane: relations between daily life events and self-reported mood. Journal of personality and social psychology, 1988.
[8]
J. E. Clougherty, C. A. Rossi, J. Lawrence, M. S. Long, E. A. Diaz, R. H. Lim, B. McEwen, P. Koutrakis, and J. J. Godleski. Chronic social stress and susceptibility to concentrated ambient fine particles in rats. Environmental health perspectives, 118(6):769, 2010.
[9]
M. R. Cunningham. Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 1979.
[10]
J. J. Denissen, L. Butalid, L. Penke, and M. A. Van Aken. The effects of weather on daily mood: a multilevel approach. Emotion, 8(5):662, 2008.
[11]
E. Digon and H. B. Bock. Suicides and climatology. Archives of Environmental Health: An International Journal, 12(3):279--286, 1966.
[12]
P. S. Dodds and C. M. Danforth. Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies, 11(4):441--456, 2010.
[13]
P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A. Bliss, and C. M. Danforth. Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PloS one, 6(12):e26752, 2011.
[14]
E. Gilbert and K. Karahalios. Widespread worry and the stock market. In ICWSM, 2010.
[15]
K. M. Goldstein. Weather, mood, and internal-external control. Perceptual and Motor skills, 1972.
[16]
M. G. Harmatz, A. D. Well, C. E. Overtree, K. Y. Kawamura, M. Rosal, and I. S. Ockene. Seasonal variation of depression and other moods: a longitudinal approach. Journal of Biological Rhythms, 15(4):344--350, 2000.
[17]
T. Hastie and R. Tibshirani. Generalized additive models.
[18]
D. J. Hopkins and G. King. A method of automated nonparametric content analysis for social science. American Journal of Political Science, 2010.
[19]
E. Howarth and M. S. Hoffman. A multidimensional approach to the relationship between mood and weather. British Journal of Psychology, 1984.
[20]
D. Kahle and H. Wickham. ggmap: A package for spatial visualization with Google Maps and OpenStreetMap, 2013. R package version 2.3.
[21]
A. D. Kramer. An unobtrusive behavioral model of gross national happiness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 287--290. ACM, 2010.
[22]
D. F. Kripke. Light treatment for nonseasonal depression: speed, efficacy, and combined treatment. Journal of affective disorders, 1998.
[23]
G. Lambert, C. Reid, D. Kaye, G. Jennings, and M. Esler. Effect of sunlight and season on serotonin turnover in the brain. The Lancet, 2002.
[24]
V. Lampos, T. De Bie, and N. Cristianini. Flu detector-tracking epidemics on twitter. In Machine Learning and Knowledge Discovery in Databases, pages 599--602. Springer, 2010.
[25]
A. O. Larsson and H. Moe. Studying political microblogging: Twitter users in the 2010 swedish election campaign. New Media & Society, 14(5):729--747, 2012.
[26]
S. Leppämäki, T. Partonen, and J. Lönnqvist. Bright-light exposure combined with physical exercise elevates mood. Journal of affective disorders, 2002.
[27]
J. Li and C. Cardie. Early stage influenza detection from twitter. arXiv preprint arXiv:1309.7340, 2013.
[28]
R. Lindsay. Predicting polls with lexicon. Available at: languagewrong. tumblr. com/post/55722687/predicting-polls-with-lexicon, 2008.
[29]
Y. Liu, X. Huang, A. An, and X. Yu. Arsa: a sentiment-aware model for predicting sales performance using blogs. In SIGIR, 2007.
[30]
G. Mishne and M. de Rijke. Capturing global mood levels using blog posts. In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, 2006.
[31]
L. Mitchell, M. R. Frank, K. D. Harris, P. S. Dodds, and C. M. Danforth. The geography of happiness: Connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS one, 8(5), 2013.
[32]
J. C. Norcross, E. Guadagnoli, and J. O. Prochaska. Factor structure of the profile of mood states (poms): two partial replications. Journal of Clinical Psychology, 1984.
[33]
V. Nowlis and H. H. Nowlis. The description and analysis of mood. Annals of the New York Academy of Sciences, 1956.
[34]
B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 2010.
[35]
O. Owoputi, B. OConnor, C. Dyer, K. Gimpel, N. Schneider, and N. A. Smith. Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of NAACL-HLT, pages 380--390, 2013.
[36]
A. Pak and P. Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. In LREC, 2010.
[37]
W. G. Parrott and J. Sabini. Mood and memory under natural conditions: Evidence for mood incongruent recall. Journal of Personality and Social Psychology, 59(2):321, 1990.
[38]
M. Persinger. Lag responses in mood reports to changes in the weather matrix. International Journal of Biometeorology, 1975.
[39]
V. Pollock, D. W. Cho, D. Reker, and J. Volavka. Profile of mood states: the factors and their physiological correlates. The Journal of nervous and mental disease, 1979.
[40]
T. Rao and S. Srivastava. Tweetsmart: Hedging in markets through twitter. In Emerging Applications of Information Technology (EAIT), 2012 Third International Conference on, pages 193--196. IEEE, 2012.
[41]
T. Rao and S. Srivastava. Using twitter sentiments and search volumes index to predict oil, gold, forex and markets indices. 2012.
[42]
E. Riloff and J. Wiebe. Learning extraction patterns for subjective expressions. In EMNLP, 2003.
[43]
E. Riloff, J. Wiebe, and T. Wilson. Learning subjective nouns using extraction pattern bootstrapping. In NAACL, 2003.
[44]
B. Rind. Effect of beliefs about weather conditions on tipping. Journal of Applied Social Psychology, 26(2):137--147, 1996.
[45]
A. Ritter, O. Etzioni, S. Clark, et al. Open domain event extraction from twitter. In SIGKDD, 2012.
[46]
J. L. Sanders and M. S. Brizzolara. Relationships between weather and mood. The Journal of General Psychology, 107(1):155--156, 1982.
[47]
N. Schwarz and F. Strack. Evaluating ones life: A judgment model of subjective well-being. Subjective well-being: An interdisciplinary perspective, 1991.
[48]
S. Shacham. A shortened version of the profile of mood states. Journal of personality assessment, 1983.
[49]
R. Stain-Malmgren, B. F. Kjellman, and A. Åberg-Wistedt. Platelet serotonergic functions and light therapy in seasonal affective disorder. Psychiatry research, 1998.
[50]
G. Tom, M. F. Poole, J. Galla, and J. Berrier. The influence of negative air ions on human performance and mood. Human Factors: The Journal of the Human Factors and Ergonomics Society, 23(5):633--636, 1981.
[51]
A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10:178--185, 2010.
[52]
N. Wang, M. Kosinski, D. Stillwell, and J. Rust. Can well-being be measured using facebook status updates? validation of facebooks gross national happiness index. Social Indicators Research, pages 1--9, 2012.
[53]
D. Watson. Mood and temperament. Guilford Press, 2000.
[54]
T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In EMNLP, 2005.
[55]
S. N. Wood. Modelling and smoothing parameter estimation with multiple quadratsic penalties. Journal of the Royal Statistical Society (B), 62(2):413--428, 2000.
[56]
S. N. Wood. Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467):673--686, 2004.
[57]
S. N. Wood. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73(1):3--36, 2011.
[58]
X. Zhang, H. Fuehres, and P. A. Gloor. Predicting stock market indicators through twitter i hope it is not as bad as i fear. Procedia-Social and Behavioral Sciences, 2011.

Cited By

View all
  • (2024)MAWI Rec: Leveraging Severe Weather Data in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688157(850-854)Online publication date: 8-Oct-2024
  • (2024)Public responses to heatwaves in Chinese cities: A social media-based geospatial modelling approachInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.104205134(104205)Online publication date: Nov-2024
  • (2023)Humidity and air temperature predict post count on Twitter in 10 countries: Weather changes & LIWC psychological categoriesEkonomika preduzeca10.5937/EKOPRE2303213B71:3-4(213-229)Online publication date: 2023
  • Show More Cited By

Index Terms

  1. What a Nasty Day: Exploring Mood-Weather Relationship from Twitter

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
    November 2014
    2152 pages
    ISBN:9781450325981
    DOI:10.1145/2661829
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 November 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. mood
    2. twitter
    3. weather

    Qualifiers

    • Research-article

    Conference

    CIKM '14
    Sponsor:

    Acceptance Rates

    CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

    Upcoming Conference

    CIKM '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)38
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 14 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)MAWI Rec: Leveraging Severe Weather Data in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688157(850-854)Online publication date: 8-Oct-2024
    • (2024)Public responses to heatwaves in Chinese cities: A social media-based geospatial modelling approachInternational Journal of Applied Earth Observation and Geoinformation10.1016/j.jag.2024.104205134(104205)Online publication date: Nov-2024
    • (2023)Humidity and air temperature predict post count on Twitter in 10 countries: Weather changes & LIWC psychological categoriesEkonomika preduzeca10.5937/EKOPRE2303213B71:3-4(213-229)Online publication date: 2023
    • (2023)VeatherReflect: Employing Weather as Qualitative Representation of Stress Data in Virtual RealityProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3596125(446-458)Online publication date: 10-Jul-2023
    • (2022)Ruh Halimizi Etkileyen Hava Durumu Parametreleri Borsa Verilerini De Etkiler Mi?Do Weather Parameters That Affect Our Mood Also Affect Stock Market Data?19 Mayıs Sosyal Bilimler Dergisi10.52835/19maysbd.11612103:3(334-340)Online publication date: 30-Sep-2022
    • (2021)Exploring Weather Data to Predict Activity Attendance in Event-based Social NetworkACM Transactions on the Web10.1145/344013415:2(1-25)Online publication date: 22-Apr-2021
    • (2021)A social Beaufort scale to detect high winds using language in social media postsScientific Reports10.1038/s41598-021-82808-x11:1Online publication date: 11-Feb-2021
    • (2020)In Cold Weather We Bark, But in Hot Weather We Bite: Patterns in Social Media Anger, Aggressive Behavior, and TemperatureEnvironment and Behavior10.1177/001391652093745553:7(787-805)Online publication date: 26-Jun-2020
    • (2020)Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networksTransactions in GIS10.1111/tgis.1263024:3(704-718)Online publication date: 14-May-2020
    • (2020)Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and HealthIEEE Transactions on Affective Computing10.1109/TAFFC.2017.278483211:2(200-213)Online publication date: 1-Apr-2020
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media