Advertisement

Quantification of Social Media Influence on Behavior Using Psychophysiological Profiles

  • Christian RichardEmail author
  • Marissa McConnell
  • Jared Poole
  • Abigail Fink
  • Gregory Rupp
  • Marija Stevanovic-Karic
  • Amir Meghdadi
  • Chris Berka
Conference paper
  • 5 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)

Abstract

The emergence of social media platforms at the start of this century has led to a transformation in how people consume and share information about the world around them. Since their inception it’s been made clear that this new medium offers opportunities for both promise and peril, a fact that highlights the importance of research into human interactions with and through these social media platforms. The primary goal of this study was to investigate the conditions under which exposure to emotionally charged or controversial social media content can affect users’ opinions. Multiple biological signals including EEG, pupillometry, and heart rate were measured concurrently with participant’s behavioral responses on the social media platform to derive objective measures of emotional valence and arousal. Here we present results of validation testing and preliminary findings from this multi-year project.

Keywords

Social media simulation EEG Frontal alpha asymmetry Pupillometry Affective neuroscience 

Notes

Acknowledgments

The work presented herein was supported by DARPA Contract No. FA865019C68899. The views expressed are those of the author and do not reflect the official policy or position of the DoD or the U.S. Government.

References

  1. 1.
    Falk, E.B., Morelli, S.A., Welborn, B.L., Dambacher, K., Lieberman, M.D.: Creating buzz: the neural correlates of effective message propagation. Psychol. Sci. 24, 1234–1242 (2013)CrossRefGoogle Scholar
  2. 2.
    Milkman, K.L., Berger, J.: The science of sharing and the sharing of science. Proc. Natl. Acad. Sci. U.S.A. 111(Suppl 4), 13642–13649 (2014)CrossRefGoogle Scholar
  3. 3.
    Lieberman, M.D.: Social cognitive neuroscience: a review of core processes. Annu. Rev. Psychol. 58, 259–289 (2007)CrossRefGoogle Scholar
  4. 4.
    Scholz, C., Baek, E.C., O’Donnell, M.B., Kim, H.S., Cappella, J.N., Falk, E.B.: A neural model of valuation and information virality. Proc. Natl. Acad. Sci. U.S.A. 114, 2881–2886 (2017)CrossRefGoogle Scholar
  5. 5.
    Ramsøy, T.Z., Skov, M., Macoveanu, J., Siebner, H.R., Fosgaard, T.R.: Empathy as a neuropsychological heuristic in social decision-making. Soc. Neurosci. 10, 179–191 (2015)CrossRefGoogle Scholar
  6. 6.
    Cartocci, G., Modica, E., Rossi, D., Inguscio, B.M.S., Aricò, P., Martinez Levy, A.C., Mancini, M., Cherubino, P., Babiloni, F.: Antismoking campaigns’ perception and gender differences: a comparison among EEG indices. Comput. Intell. Neurosci. 7348795 (2019)Google Scholar
  7. 7.
    Modica, E., Cartocci, G., Rossi, D., Martinez Levy, A.C., Cherubino, P., Maglione, A.G., Di Flumeri, G., Mancini, M., Montanari, M., Perrotta, D., Di Feo, P., Vozzi, A., Ronca, V., Aricò, P., Babiloni, F.: Neurophysiological Responses to Different Product Experiences. Comput. Intell. Neurosci. 2018, 9616301 (2018)Google Scholar
  8. 8.
    Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., Lumicao, M.N., Zivkovic, V.T., Popovic, M.V., Olmstead, R.: Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset. Int. J. Hum. Comput. Interact. 17, 151–170 (2004)CrossRefGoogle Scholar
  9. 9.
    Berka, C., Levendowski, D.J., Lumicao, M.N., Yau, A., Davis, G., Zivkovic, V.T., Olmstead, R.E., Tremoulet, P.D., Craven, P.L.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78, B231–B244 (2007)Google Scholar
  10. 10.
    Correa, K.A., Stone, B.T., Stikic, M., Johnson, R.R., Berka, C.: Characterizing donation behavior from psychophysiological indices of narrative experience. Front Neurosci. 9, 301 (2015)CrossRefGoogle Scholar
  11. 11.
    Coan, J.A., Allen, J.J.B., McKnight, P.E.: A capability model of individual differences in frontal EEG asymmetry. Biol. Psychol. 72, 198–207 (2006)CrossRefGoogle Scholar
  12. 12.
    Steiner, A.R.W., Coan, J.A.: Prefrontal asymmetry predicts affect, but not beliefs about affect. Biol. Psychol. 88, 65–71 (2011)CrossRefGoogle Scholar
  13. 13.
    Briesemeister, B.B., Tamm, S., Heine, A., Jacobs, A.M.: Approach the good, withdraw from the bad—a review on frontal alpha asymmetry measures in applied psychological research. Psychology 04, 261 (2013)CrossRefGoogle Scholar
  14. 14.
    Reimer, J., McGinley, M.J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D.A., Tolias, A.S.: Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7 (2016)Google Scholar
  15. 15.
    Safaai, H., Neves, R., Eschenko, O., Logothetis, N.K., Panzeri, S.: Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing. PNAS (2015)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Christian Richard
    • 1
    Email author
  • Marissa McConnell
    • 1
  • Jared Poole
    • 1
  • Abigail Fink
    • 1
  • Gregory Rupp
    • 1
  • Marija Stevanovic-Karic
    • 1
  • Amir Meghdadi
    • 1
  • Chris Berka
    • 1
  1. 1.Advanced Brain MonitoringCarlsbadUSA

Personalised recommendations