A Survey of Automatic Personality Detection from Texts (2020)

Abstract: Personality profiling has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this survey, we show the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point out what has been gained and what lost during that trajectory, and show what can be realistic expectations in the field.

## introduction personality is a collection of different constructs such as thoughts, feelings, and values which underlie individual differences and predict human behavior @xcite . due to its complex and multifaceted structure, automatic detection of personality requires a holistic understanding of the construct, which is not an easy task even considering today's technological advancements. throughout the personality research history, attempts for personality modelling ranged from traditional psychology methods (e.g. questionnaires), via psycholinguistic approaches (e.g. counting specific word types in texts), to the recent purely natural language processing (nlp) approaches that attempt at detecting personality traits from large amounts of social media data. especially with recent technological advancements and big data, the research area has extended with the premise that digital footprints could capture not only indirect and natural characteristics but also psychological insights on deeper levels. however, the research has shown a number of problems raising from such approaches which we systematically discuss in this survey. in section 2, we introduce the two widely-used personality models, and discuss their similarities and dissimilarities in details. in section 3, we present use cases of personality detection in the fields of computational linguistics (cl), natural language processing (nlp), and artificial intelligence (ai). section 4 shows the trajectory of automatic personality detection, from the early pure psychology approaches to the latest nlp approaches on large social media datasets, pointing out strengths and weaknesses of each of the approaches. section 5 further discusses computational problems raising from using twitter data for the myers-briggs type indicator (mbti) personality modelling, while section 6 discusses ethical concerns regarding automatic personality detection in general. in section 7, we revisit the main conclusions of the presented survey. ## personality models the most widely and frequently used personality models are the big 5 model @xcite and the mbti model @xcite . both of them are long-established psychological models that have attracted attention from cl and nlp fields in hope to offer their wider usage in industry and everyday life. ## use of personality detection in nlp applications people are more engaged in interacting with others who have similar personality profiles as it requires less information processing and cognitive load @xcite . this idea has been used in designing dialog systems and personal assistants emphasising the need for them to be able to detect and mimic the personality style of a user @xcite @xcite , in order to behave similarly to humans that instinctively adapt to the other person's personality @xcite . it has been shown that adding personality traits to virtual agents leads to significantly better perceived emotional intelligence of such systems @xcite . however, conversational agents so far have mostly exploited methods for adapting to user's emotions instead of deeper personality traits, probably due to insufficiently good performances of automatic personality detection systems on short utterances. the psycholinguistic characteristics of the big 5 personality traits @xcite have been used for building personage, the first fully automatic natural language generation system that can generate output controlling for the personality trait @xcite @xcite . the style of the generated output is controlled by many parameters: verbosity, restatement, content polarity, polarisation, concessions, concession polarity, positive context first, claim complexity, self-reference, claim polarity, hedge variation, hedge repetition, tag question insertion, etc. @xcite . the system showed promising results for four out of five dimensions (all but conscientiousness), being only slightly below the performances of a hand-crafted rule-based system. apart from making dialog systems and personal assistants being perceived as more emotionally intelligent, automatic personality detection could be used to enhance various other areas of nlp. for example, inconsistencies in impressions of agreeableness traits across visual and acoustic mode are used as cues for deception detection by human judges @xcite , and therefore could be used to enhance systems for automatic deception detection. @xcite indicated that tutoring systems might be more effective if they were able to adapt to the learner's personality, and @xcite suggested that opinion mining might be more efficient if personality information was used @xcite . ## approaches to personality detection the first approaches to personality detection were manual, using personality questionnaires that were performed/analysed by trained psychologists. as they have shown a number of biases (section 5), and at the same time, computational methods were gaining popularity in linguistics and psycholinguistics, personality detection shifted towards automatic methods that used some basic computations over texts (section 4.2). soon after that, cl and nlp communities started showing more interest in the topic, proposing machine learning models that used a number of psycholinguistic features automatically extracted from texts (section 4.3). those approaches were still highly connected with psychology theory, as the features were designed based on psychology studies on personality assessment. at the same time, powered by the computational advances, they were able to explore some new features for this task. although many features showed strong correlations with the 'gold' personality labels obtained via questionnaires, the predictive power of the machine learning models that used those same features was quite low. with the expansion of social media, the focus of automatic personality detection shifted towards machine learning approaches that tried to predict personality from large numbers of twitter posts, facebook statuses, or users' behaviours on those social platforms (section 4.4). however, those approaches did not achieve much higher performances than previous psycholinguistic approaches which used much less data. ## mbti signals in twitter data we argue that twitter data simply do not make for a good dataset for the mbti personality detection, and even more, that purely textual data do not exhibit clear linguistic (either content-based or stylistic) signals for it. the sensing/intuitive (s-n), and judging/perceiving (j-p) dimensions depend on behavioral signals rather than linguistic. the s-n typology is the result of a preference for information processing, which in fact is the product of cognitive processing and requires cognitive methodologies to understand fully (e.g. abstract reasoning test). for example, the sentence this one contains the raw data, but it isn't available to the public, although it mentions data, does not provide sufficient information to determine if the user is interested in the mentioned data in a numerical and sensing way, or in an interpretative and intuitive way. the j-p dimension includes different sets of behavioral information about a person, such as planning and organization skills. short twitter posts usually do not include information about planning and organization. to test our hypothesis that twitter data does not contain sufficient information for mbti personality detection, we conducted the following annotation experiment. we hired two people, one coming from psychology background and the other from computational linguistic background, both with annotation experience and thorough knowledge of the mbti model. we randomly selected 96 user data from the mbti-twitter dataset @xcite , controlling for having equal number (six) of users from each of the 16 categories, and controlling for gender (three male and three female in each category), as we wanted to have a representative sample with equal number of user-data for each polar in each dimension. the dataset contained 50 concatenated tweets for each user/instance. 1 we asked the annotators to assign to each user (after reading all 50 tweets), for each of the mbti dimensions one of the following four labels: either of the two polars (e.g. e or i for the e-i dimension, or s or n for the s-n dimension, etc.), unsure (in case that they saw many signals from both polars and are not confident to make a binary decision), or not enough signal (in the case that they did not find any signal for any of the two polars). the annotators were instructed to have enough breaks to avoid the fatigue effect. we found that, as expected, j-p and s-n were the two dimensions for which the annotators did not find any signal in many instances ( although to be taken only as preliminary due to the small number of annotators and instances involved, these results confirm the raised doubts in possibility to use twitter posts as training datasets for automatic detection of mbti personalities. they show that large amounts of twitter posts do not seem to contain any signals about mbti dimensions, and that often twitter posts by the same user show a mixture of signals, thus making twitter data very noisy and unreliable for the tasks. furthermore, the annotators had low agreements with 'gold labels', as well as among themselves, even on the instances for which both annotators expressed their confidence. these results raise question if the mbti constructs from traditional questionnaire-based assessments show expected linguistic patterns on textual data. ## ethical concerns in automatic personality detection apart from concerns about whether social media data make for good training data for building reliable personality models, the approaches based on social media data carry some ethical concerns given that the data comes from social media profiles, a construct that is considered as highly private. the biggest issue is related to misuse of social media data without the knowledge or consent of the users. especially after the infamous cambridge analytica study, the issue has started gaining more attention and awareness, and public's opinion on digital targeting and modelling has deteriorated drastically @xcite . alongside the consent problem, algorithmic bias could be another issue in this context, making certain groups susceptible to negative effects of the psychological targeting. various research has suggested that detecting private characteristics of users can lead to biases @xcite , and put certain groups in unfair situations. for instance, an algorithm that detects a neurotic user might recommend jobs that do not include any human interaction by ignoring the user's educational and occupational background only because data indicates that neurotic people do not engage in social interactions as much. the use of personality detection via psychographic targeting in advertising and marketing also brings ethical concerns. it enables companies to easily target their consumer segments on large scales and increase their sales and conversion rates. however, psychographic targeting can be particularly harmful for vulnerable groups who engage with risky behaviors, such as targeting addicted people with online gambling advertising @xcite . at the same time, when handled properly, the same targeting approach could make consumers more satisfied by directing them to spend their money only on products which are compatible with their personality, and avoiding unnecessary and impulse purchasing behaviors @xcite . hence, as any research, personality detection can be used for good and for bad. ## conclusions we presented the trajectory of automatic personality detection, pinpointing the main strengths and weaknesses of each of the approaches along the way. psychology research seems to use more adequate datasets for personality detection, but does not evaluate its models with the same rigour as the nlp/cl approaches. the nlp/cl approaches, in contrast, seem to not pay much attention to the psychology theories that set grounds for the personality modelling and thus set unrealistic expectations that certain personality signals can be found in any kind of textual data. this is particularly important for the mbti modelling since it covers a theoretical framework that does not originate from (linguistic) data.