Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures

By Scott A. Golder, Michael W. Macy. 2011

1,101 citations recorded by the source corpus.

Subfields: Applied Psychology, Social Psychology

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Citations in the corpus

Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “1 in [8]) and online activity ( cf Fig.”
Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls (2014, methodology): “…and allow us to observe social phenomena at a previously unthinkable level, such as the mood oscillations of millions of people in 84 countries (Golder et al., 2011), or in cases where there is arguably no other feasible method Copyright © 2014, Association for the Advancement of Artificial…”
A smartphone app reveals erratic diurnal eating patterns in humans that can be modulated for health benefits (2015, background): “Because at the population level human digital activity reaches the trough between 2 and 4 a.m. (Golder and Macy, 2011), none of the subjects were self-reported shift-workers, and the reporting trough was close to 4 a.m. (Figures 2A and 2B), we considered 4 a.m. as the onset of ''metabolic day''…”
Cross-Cultural Comparison of Nonverbal Cues in Emoticons on Twitter: Evidence from Big Data Analysis (2014, methodology): “In order to classify users into countries, we utilized the geo-location information on user profiles in a similar manner to other studies (Golder & Macy, 2011; Kulshrestha, Kooti, Nikravesh, & Gummadi, 2012).”
Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter (2011, background): “The period 5–6 am marks ‘biological midnight’ when, for example, body temperature is typically lowest (see also [37]).”
Extracting Diurnal Patterns of Real World Activity from Social Media (2013, background): “Later, Golder and Macy (2011) drew a connection between sentiment on Twitter posts to cycles of sleep and seasonality.”
Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides (2015, background): “Note that LIWC has been extensively validated to perform well on Internet language [7, 18].”
Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products (2013, background): “Finally, temporal patterns, such as those in problem drinking [13] and seasonal mood variation [14], have also been demonstrated using Twitter surveillance.”
Tweeting From Left to Right (2015, background): “…characteristics of social-media users have been calculated only for small, self-selected samples (Conover et al., 2011; Kosinski, Stillwell, & Graepel, 2013) or have focused on variables that are of little direct relevance to the study of ideological polarization (Golder & Macy, 2011).”
Understanding Short-term Changes in Online Activity Sessions (2017, background): “Later, Golder and Macy [5] drew a connection between sentiment on Twitter posts to cycles of sleep and seasonality.”
Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, neutral): “Such examples can be found in many of the descriptive studies using big data (Golder and Macy 2011).”
Editorial Expression of Concern and Correction (2014, methodology): “Positivity and negativity were evaluated separately given evidence that they are not simply opposite ends of the same spectrum (8, 10).”
Protecting privacy when mining and sharing user data (2012, background): “Examples of such studies include: measurement of moods and happiness depending on season, geographic location, and relationship status [66,72], analysis of the structure and size of human groups and reach of social connections [15,104], study of disease epidemics [63], forecasts of economic indicato”
Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013, background): “Through media such as Facebook and Twitter, used regularly by more than 1/7 of the world’s population [4], variation in mood has been tracked diurnally and across seasons [5], used to predict the stock market [6], and leveraged to estimate happiness across time [7,8].”