May 24, 2016
Using Big Data in Cyberpsychology
Thanks to the pervasive diffusion of social media and the increasing affordability of smartphone and wearable sensors, psychologists can gather and analyse massive quantities of data concerning people behaviours and moods in naturalistic situations.
The availability of “big data” presents psychologists with unprecedented professional and scientific opportunities, but also with new challenges. On the business side, for example, a growing number of tech-companies are hiring psychologists to help make sense of huge data sets collected online from their actual and prospective customers.
The job description of a “data psychologist” not only requires perfect mastery of advanced statistics, but also the ability to identify the kinds of behaviours that are most useful to track and analyse, in order to improve products and business strategies. Psychological research, too, may be revolutionized from emerging field of big data. Until recently, online research methods were mostly represented by web experiments and online survey studies.
Example of topic areas included cognitive psychology, social psychology, but also health psychology and forensing psychology (for an updated list of psychological experiments on the Internet see this useful resource by the Hanover College Psychology Department).
However, the emergence of advanced cloud-based data analytics has provided psychologists with powerful new ways of studying human behaviour using digital footprints. An interesting example is CrowdSignal, a crowdfunded mobile data collection campaign that aims at building the largest set of longitudinal mobile and sensor data recorded from smartphones and smartwatches available to the community. As reported in the project’s website, the final dataset will include geo-location, sensor, system and network logs, user interactions, social connections, communications as well as user-provided ground truth labels and survey feedback, collected from a demographically diverse pool of Android users across the United States.
A further interesting service that well exemplifies the scientific potential of social data analytics is the “Apply Magic Sauce PredictionAPI” developed by the Psychometrics Centre of the University of Cambridge. According to the Cambridge researchers, this algorithm allows predicting users’ personality traits based on Facebook interactions (i.e., Facebook Likes). To test the validity of the tool, the team compared the predictions generated by computer algorithms and the personality judgments made by human. The results, which were reported on Proceedings of the National Academy of Sciences (Youyou et al., 2015, PNAS, 112/4, pp. 1036–1040), showed that the computers’ judgments of people’s personalities based on their digital behaviors were more accurate than judgments made by their close others or acquaintances.
However, the emergence of “big data psychology” presents also big challenges. For example, it is the advantages of this approach for business and research should take into account the issues related to ethical, privacy and legal implications that are unavoidably linked to the collection of digital footprints. On the methodological side, it is also important to consider that quantity (of data) is not synonimous with quality (of data interpretation).
In order to create meaningful and accurate models from behavioural logs, one needs to consider the role played by contextual variables, as well as the possible data errors and spurious correlations introduced by high dimensionality.