Inferring Neuroticism of T witter Users by Utilizing their Following Interests (2020)

Abstract: Twitter is a medium where, when used adequately, users’ interests can be derived from what he follows. This characteristic can make it attractive for a source of personality derivation. We set out to test the hypothesis that, analogous to the Lexical hypothesis, which posits that word use should reveal personality, following behavior on social media should reveal personality aspects. We used a two-step approach, wherein the first stage, we selected accounts for whom it was possible to infer personality profiles to some extent using available literature on personality and interests. On these accounts, we trained a regression model and segmented the derived features using hierarchical cluster analysis. In the second stage, we obtained a small sample of users’ personalities via a questionnaire and tested whether the model from stage 1 correlated with the users from step 2. The the explained variance for the neurotic and neutral neuroticism groups indicated significant results (R2 = .131, p = .0205; R2 = .22, p = .0044). Confirming the hypothesis that following behavior should be correlated with one’s interests and that interests are correlated with the neuroticism personality dimension.

## introduction with the advent of massive data streams of personal online behavior, there has been a surge in interest in relating this behavior to classical constructs in psychology. in particular, the question of whether online behavior can be related to personality profiles obtained from psychometric personality instruments, often the big five @xcite , has led to a field of research that has been coined personality computing @xcite . studies in this field have found significant correlations between personality scores and lexical elements in written text, such as essays @xcite , blogs @xcite @xcite , self-presentations @xcite , emails @xcite , texting @xcite , and even the choice of email addresses @xcite . the idea that an author's personality is reflected in written or spoken text at all, is referred to as the lexical hypothesis @xcite , and has been confirmed in many studies. as personality involves and influences the interaction with one's environment, it should be expected to influence other facets of linguistic and non-linguistic expression. the rise of social media over the last decade resulted in this interaction being partly transposed to digital platforms. in fact, the current omnipresence of social media have compounded to the availability of records of individual expression and extends well beyond just the textual, and includes facebook "likes" @xcite , sharing behavior @xcite , website visiting behavior, youtube videos @xcite , linkedin profiles @xcite , instagram pictures @xcite , and tweets on twitter @xcite @xcite . at face value, facebook, twitter and other social media platforms seem quite similar. indeed lexical hypothesis-driven approaches have treated facebook status updates as more or less equivalent to twitter tweets. however, there is an essential distinction between the purposes of the two media. facebook is generally used as a place to keep in touch with friends and acquaintances. twitter, on the other hand, is a medium that is used for sharing and gaining information related to someone's interests. as a result, to use twitter adequately and benefit from its full set of features, an active twitter user is forced to follow his different interests on twitter. this characteristic of twitter can make it especially attractive as a source for personality derivation, as preferences of individuals can, to a significant extent, be explained by underlying personality traits @xcite . it has often been contended that the social network(s) that people find themselves embedded in is reflective of many personal characteristics @xcite . social media networks -reflected in the connections users can make to other users, e.g., by 'friending' on facebook or 'following' on twitter foot_0 -have been related to various personal characteristics such as gender, age, ethnicity, sexual orientation, religious and political views @xcite . although it has been observed that various generic network metrics that do not take into account the node characteristics of the connected nodes, including connectedness, node centrality measures, as well as the total number of friends / followers and the number of users followed, are correlated with personality @xcite , to our knowledge, no research on computational personality has investigated whether the specific set of accounts followed by a twitter user is associated with personality. in this study we aim to investigate if, and how profiles of accounts followed by a user are related to personality. we do this by deriving a predictive model from twitter following graph data related to a set of prior chosen interests, and validating this predictive model on a sample of twitter users from whom we obtained personality profiles using a standardized test. we focused particularly on neuroticism, as it has shown to have a reliable correlation to social media extracted features @xcite . the specific interest were chosen on the basis of available literature that relates the interest to personality, and on the requirement that this interest can be relatively easily inferred from twitter accounts using heuristic methods. we hypothesize that the following of (clusters of) nodes in the twitter graph (that we coin 'influencer accounts' below) are significantly correlated with personality scores on a standardized personality test. ## methods to build a predictive model that uses the links between nodes, a large sample of users is required as predictor variables within all the possible connections in the following graph data. while it is easy to obtain a large sample of twitter users and the information regarding their following behavior, it is not easy to obtain their personality profiles by having these users fill out a psychometric test. we therefore developed a two stage approach: in the first stage we select a large sample of twitter users for whom it was possible to infer personality profiles to some extend by heuristic means from their expressed interests and professions (we detail this below). on this data set, we trained a regression model. in the second stage, we obtained a small sample of twitter users who were willing to fill out a personality questionnaire (the neo-ffi) to provide us with a validation sample: we tested whether the predictions by model from stage 1 for the people in our stage 2 sample significantly correlated with personality profiles obtained from the questionnaire they filled out. ## discussion we set out to test the hypothesis that, analogous to the lexical hypothesis which posits that word use should reveal personality, following behavior on social media should reveal aspects of personality. this hypothesis was motivated by the fact that following behavior should be highly correlated with one's interests and occupation, and that interests and occupation are correlated with personality dimensions. in particular, we aimed to test this hypothesis with respect to the degree of neuroticism of an individual. to do so, we introduced a novel technique for constructing predictive independent variables: on the basis of these well established correlations between personality on the one hand, and interests and occupation on the other, we were able to gather a sufficiently large database of twitter users and their following behavior to extract clusters of influencer accounts that discriminate between twitter users that have a lower or higher propensity to score high on the neuroticism dimension of the big five. using these predictors we were then able to confirm the hypothesis by showing that these predictors were significantly related to neuroticism scores in a relatively small sample of 98 participants recruited to fill out the neo-ffi. on the basis of these results we can conclude that it is possible to derive estimates of neuroticism from following behavior. a significant consequence of these findings is that, in contrast to personality profiles extracted on the basis of lexical hypothesis which requires active engagement of social media platform users, our results are purely based on information that is passively conveyed by twitter users by their following behavior. hence, a neuroticism score can be obtained even from social media users who do not actively engage in information sharing on these platforms. one might object that the explained variance is rather low and consequently, very imprecise. this is certainly true for assessing specific individuals reliably-that is, our results do not really support the notion that it is possible, e.g., for employers to screen a specific applicant for a job. however, it is sufficient for targeting population segments; e.g., it allows for talent recruiters to target subgroups of the population that are more likely to have certain personality profiles, or for marketeers to target population segments, e.g., in attempts to sway an election.