Master Thesis accessible at
Predicting Personality Traits from Social Media Footprints : an enhanced Model
Mrutzek, Niklas Maximilian
Dokument 1.pdf (1.850 KB)
Keywords from authority file SWD (German):
Neue Medien , Nutzung , Prognose , Persönlichkeit
Free keywords (German):
Free keywords (English):
Social Media Footprint
Dewey Decimal Classification:
Sozialwissenschaften, Soziologie, Anthropologie
Year of creation:
Date of publication:
Abstract in English:
Evaluating another person´s personality is an essential part of human life. How an individual reacts to a certain trigger, let it be a statement, strongly depends on his personality. Therefore, knowledge about the personality of a conversational counterpart is crucial to predict how he or she will react to a question or an answer. Personality is commonly understood as ´patterns of thought, emotion, and behavior that are relatively consistent over time and across situations´ (Funder 2012). If personality is as aforementioned defined as stable ´over time and across situations´, then it has to be differentiated from the character, which might change as an actor plays a role. A large proportion of an individual´s outer behavior can be explained by the inner personality. The outer behavior as a result of the personality determines various socio-demographic attributes, like job satisfaction (Furnham et al. 2002), the success of romantic relationships (Noftle, Shaver 2006), job performance (Barrik, Mount 1991) or high income, conservative political attitudes, early life adjustment to challenges, and social relationships (Soldz, Vaillant 1999). Humans can infer another person´s personality pretty precise. A first impression like a short video in many cases is enough to asses a personality (Carney et al. 2007). However, personality assessment is not limited to the social-cognitive domain of human brains - machine learning models attempt to predict personalities as well, or even better than humans. The internet provides a vast amount of data regarding personal information about its users - to so-called digital footprint. Especially social networks offer personal data in a very condensed form, the social-media footprint. Social media networks, which are online platforms, where people create a profile of themselves and communicate with other users or artificial persons like newspaper, offer a wide range of personal data to the broad community, as well as the network and its developers. In the year 2014 49.7 % of the German internet participated in social media networks (Statistisches Bundesamt 3/16/2015) with an upward trend. Furthermore, social media networks, like Facebook, provide the possibility to ´like´ something, which means at first: the user starts to follow a certain page and therefore receives updates and messages from the page and secondly: that the user publicly declares that he or she likes the page, visible to other users. However, it has been shown that the profile of a social network user indeed reflects the individual user and his personality and not an ´idealized´ version of 5 themselves (Back et al. 2010). Hence, these profiles seem to be unbiased, or at least as biased as the personality tests themselves.
On the other side are the Facebook pages. A page in this case can be related to anything that a user started, let it be a political attitude, an artificial person, a company or a special kind of food. Any page can be created, and every user can give it a ´Like´. Facebook, as the biggest social media network as of today (Statista 2017) offers the possibility to collect data about a user´s Facebook likes, if the user agrees to the request. Due to the generic nature of Facebook likes and the relevance of personality assessment as a crucial part of social living, this paper focuses onto machine personality prediction based on Facebook likes. However, listening to music from a certain group in a web browser or reading a certain online newspaper can be easily translated into the Facebook like analogy and vice versa, which means that findings from this study are unlikely limited to the domain of Facebook likes.