Tracking Fluctuations in Psychological States Using Social Media Language: A Case Study of Weekly Emotion
Corresponding Author
Johannes C. Eichstaedt
Stanford University, USA
Correspondence to: Johannes C. Eichstaedt, Stanford University, Stanford, CA, USA and Aaron C. Weidman, University of Michigan, Ann Arbor, MI, USA.
E-mail: johannes.stanford@gmail.com; aaron.c.weidman@gmail.com
Search for more papers by this authorCorresponding Author
Aaron C. Weidman
University of Michigan, USA
Correspondence to: Johannes C. Eichstaedt, Stanford University, Stanford, CA, USA and Aaron C. Weidman, University of Michigan, Ann Arbor, MI, USA.
E-mail: johannes.stanford@gmail.com; aaron.c.weidman@gmail.com
Search for more papers by this authorCorresponding Author
Johannes C. Eichstaedt
Stanford University, USA
Correspondence to: Johannes C. Eichstaedt, Stanford University, Stanford, CA, USA and Aaron C. Weidman, University of Michigan, Ann Arbor, MI, USA.
E-mail: johannes.stanford@gmail.com; aaron.c.weidman@gmail.com
Search for more papers by this authorCorresponding Author
Aaron C. Weidman
University of Michigan, USA
Correspondence to: Johannes C. Eichstaedt, Stanford University, Stanford, CA, USA and Aaron C. Weidman, University of Michigan, Ann Arbor, MI, USA.
E-mail: johannes.stanford@gmail.com; aaron.c.weidman@gmail.com
Search for more papers by this authorAbstract
Personality psychologists are increasingly documenting dynamic, within-person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality-relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text-based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human-annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self-reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model-predicted emotion and self-reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology
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Supporting Information
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PER2261-supp-0001-Online Supplement.docxWord 2007 document , 935.8 KB |
Table S1: Model fit statistics for relation between valence and arousal in calibration sample Table S2: Model fit statistics for relation between valence and arousal in validation sample Figure S1: Histogram of words per Facebook status in the calibration sample. Figure S2: Valence as a function of arousal in the calibration sample. Figure S3: Temporal distribution of weeks included in the validation sample. Figure S4: Stability of valence and arousal mean and standard deviations as a function of different thresholds. Figure S5: Histogram of Facebook statuses per user included in the validation sample. Figure S6: Histogram of weeks per user included in the validation sample. Figure S7. User-level time series in validation data set. Table S3: Relations between demographics, personality and weekly valence and arousal with no age or gender controls. Table S4: Descriptive statistics for primary variables in validation sample. Table S5. Average autocorrelations for lags of 1 to 7 weeks for valence and arousal in validation data set. Table S6. Associations of lag 1 autocorrelation coefficients across users in validation data set. |
per2261-sup-0001-Open_Practices_Disclosure_Form.pdfPDF document, 654.8 KB |
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Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Citing Literature
Special Issue:Behavioral personality science in the age of big data
September/October 2020
Pages 845-858