Exploring the Prediction of Personality Traits from Drug Consumption Profiles

Bruce Ferwerda (Jönköping University, Sweden) and Marko Tkalcic (University of Primorska, Slovenia)

The number of people that have been in touch with drugs is continuously increasing. Excessive intake of drugs becomes problematic when it turns into disorderly behaviors, such as addictions. In order to treat these disorderly behaviors, treatment plans often adhere to a one-size-fits-all approach with fixed and standardized steps. However, for effective treatment of disorderly behaviors it has been acknowledged that personalized treatment programs are necessary. The personality of people has been argued to be a factor that plays an important role in setting up effective treatment plans. In this work we explored the predictability of people’s personality traits based on their drug consumption profile.

Data

To explore the predictability of personality traits from drug consumption profiles, we used the dataset of Fehrman et al (2017). The dataset was collected between March 2011 and March 2012 with a total of 1885 participants. Participants were asked to respond to 20 legal and illegal drug (see Table 1 for the drugs that were included in the dataset) consumption questions (“Never used”, “Used over a decade ago”, “Used in last decade”, “Used in last year”, “Used in last month”, “Used in last week”, and “Used in last day”) and additionally to fill in the NEO-FFI-R questionnaire, which measures the five personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) through 60-items (McCrae and Costa, 2004) (see Table 2 for a description of each of the five personality factors).

 

Results

We used the learner-based feature selection to select the best features (i.e. drugs) to train our model for predicting personality traits from the self-reported drug intake. All the 19 drugs that were used in the dataset showed to be relevant features for the prediction of each personality trait. Hence, no further selection of the features was needed.

The ZeroR majority classifier was used as the baseline model. Three different classifiers, chosen on the base of previous works on personality prediction, were used and compared against the baseline: M5’rules, random forest, and radial basis function (RBF) network (see Table 3 for the results). These classifiers are commonly used and have shown to perform well in personality prediction in previous works.

We trained our models with the aforementioned classifiers in Weka using 10-fold cross-validation with 10 iterations. For each classifier, we report the root-mean-squared error (RMSE) in Table 3 to indicate the difference between the predicted and observed values. The RMSE of each personality trait relates to a [1,5] score scale.

Conclusions

In this work we explored the prediction of personality traits from drug consumption profiles. By testing drug profiles based on the consumption of 19 legal and illegal abusable psychoactive drugs, we found that personality traits can be predicted from this self-reported data. For the prediction of all personality traits, we were able to outperform the baseline model by using three different classifiers: M5’rules, random forest, and RBF network. Out of the three tested classifiers, we found that the RBF network performs best across the classifiers that were used. This seems to be in line with findings of prior work that tested multiple classifiers as well.

Limitations and Future Work

Although we did not conduct any extensive feature engineering, we were already able to create predictive models that could outperform the baseline model. For future work we will try to improve the current models by creating better features. We see three different situations that need to be addressed: 1) The current dataset does not take into account the consumption frequency related to the drug characteristic (e.g., the consequences of excessive intake of such a drug). For example, caffeine consumption may occur way more frequent than the frequency of consumption of cocaine, 2) We did not look at different age groups (e.g., some drugs may be more “popular” than others), and 3) The nature of the drug consumption question may have introduced a confound for the participants. Participants were asked to provide responses to a 7-point consumption frequency measurement (“Never used”, “Used over a decade ago”, “Used in last decade”, “Used in last year”, “Used in last month”, “Used in last week”, and “Used in last day”). The recollection of some events may have been too far in the past for participants to recall them accurately and assign the correct time span to it. The aforementioned situations can partly be addressed through applying different coding schemes to the consumption frequency responses (e.g., combining scales) and/or giving weights by taking into account the drug characteristic. Furthermore, the current dataset only takes into account abusable psychoactive drugs. The inclusion of other kind of drugs could contribute to a richer drug consumption profile, and may further improve the personality prediction possibilities.

 

References

Elaine Fehrman, Awaz K Muhammad, Evgeny M Mirkes, Vincent Egan, and Alexander N Gorban. 2017. The Five Factor Model of personality and evaluation of drug consumption risk. In Data Science. Springer, 231–242.

Robert R McCrae and Paul T Costa Jr. 2004. A contemplated revision of the NEO Five-Factor Inventory. Personality and individual differences 36, 3 (2004),587–596.