Sana Maidullah
PhD Student
Nowadays the big questions are: ‘who controls the internet (virtual platform)’; the big giant companies like Google and Facebook or the government; ‘how Google and Facebook show products of our interest’; ‘how do they know about our every preference, likes and dislikes’; ‘is it true that they monitor us or they regulate our behavior?
The answer is yes. They are monitoring us and can manipulate us because they know our pattern of behavior over the internet. We spend almost one-third of our day over the internet and never knew that we provide a lot of information about our preference on different online platforms just by the way we browse, or scroll, like the pages or photos of friends. The patterns of behavior of an individual show the critical features of his/her personality and thus we have created a window to peep in and capture our personality on the internet.
This topic became the point of everyday conversation after the Cambridge Analytica data breach came into the light; recently Facebook was also in the news for the similar case. This news brought the spotlight on the fact that many Behavioural scientists like (Katerina & Nicolaos, 2018; Li, Wan, & Wang, 2017; Quercia, Lambiotte, Stillwell, Kosinski, & Crowcroft, 2012; Oliveira, 2011) and many more, use internet user profile information to understand personality and predict behaviour (similar thing was allegedly used in campaigning and promotions in USA and Indian elections).
The online platform provides the opportunity to intimately acquaint with human personality and in turn higher precision in prediction. The efficiency is probably increasing because the data here is coming from the behavior itself and the patterns can be identified. Whereas, until last decade literature of personality analysis comprised only self-reported questionnaire which was very tedious, boring and takes lot of time and effort of the participant. The biggest drawback is that the questionnaire measures the responder’s perception of the concept, not the concept itself.
It’s safe to say that the ICT (Information communication technology) can change the way of studying and measuring personality. This trend has already been picked up by many researchers and marketers who have introduced tools and developed methods to understand the personality from their social media use, phone use, shopping website use, and mailing address. To cite few, Oliveira (2011) published an article on personality analyses from a mobile phone, Kosinski and Stillwell (2012) wrote on personality prediction from website choice. Similar articles on personality prediction by keyboard use (Katerina & Nicolaos, 2018), social networking site use (Wald, Khoshgoftaar, Napolitano, & Sumner, 2012; Wald et al., 2012) are also published.
In general, ICT has developed rapidly with time and has changed the way we live and work. It has transferred many real-life platforms to a virtual platform. It introduced the world of e-commerce, now our local uncle’s “Kirana Store” converted into the Big Basket, Flipkart, or Amazon, the uncle’s “Chasma Store” into Lenskart and Cool Winks, local phone recharge into Paytm etc. Many researchers report that these online sites have significantly reduced the physical burden and provide the convenience to do everything in just one click(Lee & Lee, 2004) but somewhere it increased the complexity and confusion in individual thinking, judgment and decision making also (Padampat, 2015).
Technology can be a fantastic tool for enhancing our understanding of human behavior and the prediction of personality is one such example. The better understanding of personality itself can increase the validity and generalizability of buyer personality (Bachrach, Kosinski, & Stillwell, 2012; (Bachrach et al., 2012) as well as can be beneficial for assistive technology, the recommendation system and adaptive environment (Ortigosa, Carro, & Quiroga, 2014). It can also be useful for e-commerce market for making personalized products (Ortigosa, Carro, & Quiroga, 2014) and services so that people can be served accordingly.
Bachrach, Y., Kosinski, M., & Stillwell, D. (2012). Personality and Patterns of Facebook Usage.
References:
Katerina, T., & Nicolaos, P. (2018). Title : Mouse Behavioral Patterns and Keystroke Dynamics in End-User Development : what can they tell us about users ’ behavioral attributes ? Authors : Computers in Human Behavior. Elsevier B.V. https://doi.org/10.1016/j.chb.2018.02.012
Lee, B., & Lee, W. (n.d.). The Effect of Information Overload on Consumer Choice Quality in an On-Line Environment, 21(March 2004), 159–183. https://doi.org/10.1002/mar.20000
Li, C., Wan, J., & Wang, B. (2017). Personality Prediction of Social Network Users. https://doi.org/10.1109/DCABES.2017.25
Oliveira, R. De. (2011). Towards a Psychographic User Model From Mobile Phone Usage, 2191–2196.
Ortigosa, A., Carro, R. M., & Quiroga, J. I. (2014). Journal of Computer and System Sciences Predicting user personality by mining social interactions in Facebook. Journal of Computer and System Sciences, 80(1), 57–71. https://doi.org/10.1016/j.jcss.2013.03.008
Padampat, S. (2015). Analysing Effects Of Information Overload On Decision Quality In An Online Environment ShubhamGoswami , Ph . D, IX(March).
Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., & Crowcroft, J. (2012). The Personality of Popular Facebook Users, 955–964.
Wald, R., Khoshgoftaar, T., Napolitano, A., & Sumner, C. (2012). Using Twitter Content to Predict Psychopathy, 394–401. https://doi.org/10.1109/ICMLA.2012.228