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The Big-2/ROSe Model of Online Personality

Towards a Lightweight Set of Markers for Characterizing the Behavior of Social Platform Denizens

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Abstract

The Big-5/OCEAN personality traits model, one of the central approaches to psychometrics, has been shown to have many applications over a variety of disciplines. In particular, correlations have been studied leading to effective characterization of people’s behavior, and the model has become notorious for its role in the Cambridge Analytica/Facebook scandal surrounding the 2016 US presidential elections. In this paper, we develop Big-2 (or ROSe, for Relationship to Others and to Self), a model via which the personality of users of online platforms can be studied using a lightweight set of markers focused on online behavior, avoiding the major data privacy pitfalls afflicting approaches based on more powerful models that characterize personal aspects of the human psyche. Evaluation of Big-2’s effectiveness is done in two parts: a quantitative evaluation on a specific prediction task and a qualitative one based on an analysis of the different ways in which the Big-2 traits can be derived from online behavior, proposing a general template to guide such efforts. Quantitative results show that our lightweight model can match or surpass the performance of Big-5 in a prediction task, while qualitative results show that it is feasible to implement the model based on the observation of basic online user behavior. Our main result is a general-purpose model that can be used to characterize the personality traits of users of online platforms in an ethical manner. Our proposed model provides a valuable tool to carry out effective and explainable analyses of online personality, avoiding the collection of unnecessary user data that would open the possibility for ethical violations.

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Notes

  1. This table partially reproduces the content of the table available at https://watson-developer-cloud.github.io/doc-tutorial-downloads/personality-insights/Personality-Insights-Facet-Characteristics.pdf

  2. For a more detailed discussion on the interpretation of numeric values, see https://cloud.ibm.com/docs/services/personality-insights?topic=personality-insights-numeric

  3. The code used for these experiments is available at: https://github.com/fabiorgallo/Big2-OCEAN-Experiment

  4. We thank V.S.Subrahmanian for sharing the dataset.

  5. https://www.ibm.com/watson/services/personality-insights/

  6. We thank Constanza Caorsi from IBM Argentina for facilitating an academic license for this service.

  7. https://github.com/JWHennessey/phpInsight

  8. This reduced example is for illustrative purposes only—the tool yields only a weak analysis over these 156 words.

References

  1. Furr RM. Psychometrics: An introduction. Sage Publications 2017.

  2. Allcott H, Gentzkow M. Social media and fake news in the 2016 election. National Bureau of Economic Research: Tech. rep; 2017.

    Book  Google Scholar 

  3. Del Vicario M, Zollo F, Caldarelli G, Scala A, Quattrociocchi W. Mapping social dynamics on Facebook: The Brexit debate. Social Networks. 2017;50:6–16.

    Article  Google Scholar 

  4. Digman JM. Personality structure: Emergence of the five-factor model. Annual Review of Psychology. 1990;41(1):417–40.

    Article  Google Scholar 

  5. Gallo FR, Simari GI, Martinez MV, Falappa MA. Predicting user reactions to Twitter feed content based on personality type and social cues. Future Generation Computer Systems. 2019.

  6. Hormes JM, Kearns B, Timko CA. Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction. 2014;109(12):2079–88.

    Article  Google Scholar 

  7. Abaeikoupaei N, Al Osman H. A multi-modal stacked ensemble model for bipolar disorder classification. IEEE Transactions on Affective Computing (Early Access). 2020.

  8. James TL, Lowry PB, Wallace L, Warkentin M. The effect of belongingness on obsessive-compulsive disorder in the use of online social networks. J Manag Info Sys. 2017;34(2):560–96.

    Article  Google Scholar 

  9. Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational Science. 2011;2(1):1–8.

    Article  Google Scholar 

  10. Asur S, Huberman BA. Predicting the future with social media. In: Proc. WI. IEEE Comp Soc. 2010;492–499.

  11. Galesic M, de Bruin WB, Dumas M, Kapteyn A, Darling J, Meijer E. Asking about social circles improves election predictions. Nature Human Behaviour. 2018;2(3):187.

    Article  Google Scholar 

  12. Sanders E, de Gier M, van den Bosch A. Using demographics in predicting election results with Twitter. In: Proc. SOCINFO. Springer. 2016;259–268

  13. Kušen E, Strembeck M. Politics, sentiments, and misinformation: an analysis of the Twitter discussion on the 2016 Austrian presidential elections. Onl Soc Netw Med. 2018;5:37–50.

    Google Scholar 

  14. Singh P, Dwivedi YK, Kahlon KS, Pathania A, Sawhney RS. Can Twitter analytics predict election outcome? an insight from 2017 punjab assembly elections. Government Information Quarterly. 2020;101444

  15. Zhang J, Otomo T, Li L, Nakajima S: Cyberbullying detection on Twitter using multiple textual features. In: 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). 2019;1–6.

  16. Balakrishnan V, Khan S, Fernandez T, Arabnia HR. Cyberbullying detection on Twitter using Big Five and Dark Triad features. Personality and Individual Differences. 2019;141:252–7.

    Article  Google Scholar 

  17. Phillips L, Dowling C, Shaffer K, Hodas NO, Volkova S. Using social media to predict the future: A systematic literature review. CoRR abs/1706.06134. 2017;1–55

  18. Businessballs: How to be self-aware (course): Personality theories and types. 2021. https://www.businessballs.com/self-awareness/personality-theories-and-types/. Accessed 04-Feb-2021

  19. Golbeck J, Robles C, Edmondson M, Turner K. Predicting personality from Twitter. In: Proc. PASSAT@SocialCom.. IEEE. 2011;149-156.

  20. Bai S, Hao B, Li A, Yuan S, Gao R, Zhu T. Predicting Big-Five personality traits of microblog users. In: Proc. WI. IEEE Comp Soc. 2013;501–508.

  21. Kaushal V, Patwardhan M. Emerging trends in personality identification using online social networks - a literature survey. ACM Trans Knowl Disc Data (TKDD). 2018;12(2):15.

    Google Scholar 

  22. Chen J, Haber EM, Kang R, Hsieh G, Mahmud J. Making use of derived personality: The case of social media ad targeting. In: Proc. ICWSM. 2015;51–60.

  23. Correa T, Hinsley AW, De Zuniga HG. Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior. 2010;26(2):247–53.

    Article  Google Scholar 

  24. IBM: IBM Cloud Docs / Personality Insights. https://console.bluemix.net/docs/services/personality-insights/models.html#models. Accessed: 14-May-2020

  25. Arnoux PH, Xu A, Boyette N, Mahmud J, Akkiraju R, Sinha V. 25 tweets to know you: A new model to predict personality with social media. In: Proc. ICWSM. 2017.

  26. Liu Z, Wang Y, Mahmud J, Akkiraju R, Schoudt J, Xu A, Donovan B. To buy or not to buy? Understanding the role of personality traits in predicting consumer behaviors. In: Proc. SOCINFO. Springer 2016;337–346

  27. De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. In: Proc. ICWSM. 2013;1–10.

  28. Kayiş AR, Satici SA, Yilmaz MF, Şimşek D, Ceyhan E, Bakioğlu F. Big Five-personality trait and internet addiction: A meta-analytic review. Comp Human Behav. 2016;63:35–40.

    Article  Google Scholar 

  29. Aidt T, Rauh C. The Big Five personality traits and partisanship in England. Electoral Studies. 2018;54:1–21.

    Article  Google Scholar 

  30. Seidman G. The Big 5 and relationship maintenance on Facebook. J Soc Pers Rel. 2019;36(6):1785–806.

    Article  Google Scholar 

  31. Sulaiman A, Jaafar NI, Tamjidyamcholo A. Influence of personality traits on Facebook engagement and their effects on socialization behavior and satisfaction with university life. Info Comm Soc. 2018;21(10):1506–21.

    Article  Google Scholar 

  32. Mehta Y, Fatehi S, Kazameini A, Stachl C, Cambria E, Eetemadi S. Bottom-up and top-down: Predicting personality with psycholinguistic and language model features. In: Proc. of ICDM. 2020.

  33. Stachl C, Au Q, Schoedel R, Gosling SD, Harari GM, Buschek D, Völkel ST, Schuwerk T, Oldemeier M, Ullmann T, et al. Predicting personality from patterns of behavior collected with smartphones. Proc Nat Acad Sci. 2020;117(30):17680–7.

    Article  Google Scholar 

  34. Stachl C, Pargent F, Hilbert S, Harari GM, Schoedel R, Vaid S, Gosling SD, Bühner M. Personality research and assessment in the era of machine learning. Euro J Pers. 2020;34(5):613–31.

    Article  Google Scholar 

  35. Li Y, Kazameini A, Mehta Y, Cambria E. Multitask learning for emotion and personality detection. arXiv preprint arXiv:2101.02346. 2021.

  36. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: An attention-based bidirectional cnn-rnn deep model for sentiment analysis. Future Generation Computer Systems. 2021;115:279–94. https://doi.org/10.1016/j.future.2020.08.005.

    Article  Google Scholar 

  37. Kumar A, Narapareddy VT, Srikanth VA, Malapati A, Neti LBM. Sarcasm detection using multi-head attention based bidirectional lstm. IEEE Access. 2020;8:6388–97.

    Article  Google Scholar 

  38. Akhtar MS, Ekbal A, Cambria E. How intense are you? predicting intensities of emotions and sentiments using stacked ensemble. IEEE Computational Intelligence Magazine. 2020;15(1):64–75. https://doi.org/10.1109/MCI.2019.2954667.

    Article  Google Scholar 

  39. Mehta Y, Majumder N, Gelbukh AF, Cambria E. Recent trends in deep learning based personality detection. Artif Intell Rev. 2020;53(4):2313–39.

    Article  Google Scholar 

  40. Gallo FR, Abad Santos N, Simari GI, Falappa MA. A desiderata for modeling and reasoning with social knowledge. In: Proc. CACIC. 2015

  41. Gallo FR, Abad Santos N, Simari GI, Martinez MV, Falappa MA. Belief dynamics in complex social networks. In: Proc. ASAI-JAIIO 45. 2016.

  42. Gallo FR, Simari GI, Martinez MV, Falappa MA, Abad Santos N. Reasoning about sentiment and knowledge diffusion in social networks. IEEE Internet Computing. 2017;21(6):8–17.

    Article  Google Scholar 

  43. Eysenck HJ. Dimensions of personality. London: Routledge & Kegan-Paul; 1947.

    Google Scholar 

  44. Digman JM. Higher-order factors of the Big Five. Journal of personality and social psychology. 1997;73(6):1246.

    Article  Google Scholar 

  45. Cambria E. Affective computing and sentiment analysis. IEEE Intell Syst. 2016;31(2):102–7.

    Article  Google Scholar 

  46. Hussain A, Cambria E. Semi-supervised learning for big social data analysis. Neurocomputing. 2018;275:1662–73.

    Article  Google Scholar 

  47. Recupero DR, Alam M, Buscaldi D, Grezka A, Tavazoee F. Frame-based detection of figurative language in tweets [application notes]. IEEE Comput Intell Mag. 2019;14(4):77–88.

    Article  Google Scholar 

  48. Ilmini K, Fernando T. Persons’ personality traits recognition using machine learning algorithms and image processing techniques. Adv Comp Sci. 2016;5:40–4.

    Google Scholar 

  49. Philip J, Shah D, Nayak S, Patel S, Devashrayee Y. Machine learning for personality analysis based on big five model. In: V.E. Balas, N. Sharma, A. Chakrabarti (eds.) Data Management, Analytics and Innovation. Springer Singapore. 2019;345–355.

  50. Miller T. Explanation in artificial intelligence: Insights from the social sciences. Artif Intell. 2019;267:1–38.

    Article  MathSciNet  Google Scholar 

  51. Nott G. Explainable artificial intelligence: Cracking open the black box of AI. Computer World 4. 2017.

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Acknowledgements

We are grateful to V.S. Subrahmanian for providing the Twitter dataset used in our empirical evaluation, and to Constanza Caorsi from IBM Argentina for facilitating access to an academic license for IBM Cloud Personality Insights.

Funding

This research was funded by Universidad Nacional del Sur (UNS) under grants PGI 24/N046 and PGI 24/ZN34, Secretaría de Investigación Científica y Tecnológica, Facultad de Ciencias Exactas y Naturales, UBA (RESCS-2020-345-E-UBA-REC) by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) under grant PIP 11220170100871CO, by Agencia Nacional de Promoción Científica y Promoción Tecnológica under grants PICT-2018-0475 (PRH-PIDRI-2014-0007) and PICT-2016-0215 (III-A-Raíces).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Universidad Nacional del Sur (UNS), Bahia Blanca, Argentina

    Gerardo I. Simari, Fabio R. Gallo & Marcelo A. Falappa

  2. Institute for Computer Science and Engineering (ICIC UNS–CONICET), Bahia Blanca, Argentina

    Gerardo I. Simari, Fabio R. Gallo & Marcelo A. Falappa

  3. Arizona State University, Tempe, Arizona, USA

    Gerardo I. Simari

  4. Department of Computer Science, Universidad de Buenos Aires (UBA), Ciudad Autonoma de Buenos Aires, Argentina

    Maria Vanina Martinez

  5. Institute for Computer Science Research (ICC UBA–CONICET), Ciudad Autonoma de Buenos Aires, Argentina

    Maria Vanina Martinez

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  1. Gerardo I. Simari
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Correspondence to Gerardo I. Simari.

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Simari, G.I., Martinez, M.V., Gallo, F.R. et al. The Big-2/ROSe Model of Online Personality. Cogn Comput 13, 1198–1214 (2021). https://doi.org/10.1007/s12559-021-09866-1

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