Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers

Exploring the Association Between the "Big Five" Personality Traits and Fatal Opioid Overdose: County-Level Empirical Analysis

BACKGROUND

Opioid-related deaths constitute a problem of pandemic proportions in the United States, with no clear solution in sight. Although addressing addiction-the heart of this problem-ought to remain a priority for health practitioners, examining the community-level psychological factors with a known impact on health behaviors may provide valuable insights for attenuating this health crisis by curbing risky behaviors before they evolve into addiction.

OBJECTIVE

The goal of this study is twofold: to demonstrate the relationship between community-level psychological traits and fatal opioid overdose both theoretically and empirically, and to provide a blueprint for using social media data to glean these psychological factors in a real-time, reliable, and scalable manner.

METHODS

We collected annual panel data from Twitter for 2891 counties in the United States between 2014-2016 and used a novel data mining technique to obtain average county-level "Big Five" psychological trait scores. We then performed interval regression, using a control function to alleviate omitted variable bias, to empirically test the relationship between county-level psychological traits and the prevalence of fatal opioid overdoses in each county.

RESULTS

After controlling for a wide range of community-level biopsychosocial factors related to health outcomes, we found that three of the operationalizations of the five psychological traits examined at the community level in the study were significantly associated with fatal opioid overdoses: extraversion (β=.308, P<.001), neuroticism (β=.248, P<.001), and conscientiousness (β=.229, P<.001).

CONCLUSIONS

Analyzing the psychological characteristics of a community can be a valuable tool in the local, state, and national fight against the opioid pandemic. Health providers and community health organizations can benefit from this research by evaluating the psychological profile of the communities they serve and assessing the projected risk of fatal opioid overdose based on the relationships our study predict when making decisions for the allocation of overdose-reversal medication and other vital resources.

Does It Matter What I Say? Using Language to Examine Reactions to Ostracism as It Occurs

Most of our knowledge related to how social exclusion affects those who ostracize and those who are being ostracized is based on questionnaires administered after the ostracism situation is over. In this research, we strived to further our understanding of the internal dynamics of an ostracism situation. We therefore examined individuals' language-specifically, function words-as a behavior indicative of psychological processes and emergent states that can be unobtrusively recorded right in the situation. In online chats, 128 participants talked about a personal topic in groups of three. In the experimental group (n = 79), two conversation partners ignored every contribution by the third. We found that, compared to the control group, these targets of ostracism used language indicative of a self-focus and worsened mood, but not of social focus or positivity, although positivity was related to a writer's likeability. Sources of ostracism used language suggesting that they were distancing themselves from the situation, and they further engaged in victim derogation. We discuss how our results highlight the severity and potential self-sustainability of ostracism.

Surrogates and Artificial Intelligence: Why AI Trumps Family

The increasing accuracy of algorithms to predict values and preferences raises the possibility that artificial intelligence technology will be able to serve as a surrogate decision-maker for incapacitated patients. Following Camillo Lamanna and Lauren Byrne, we call this technology the autonomy algorithm (AA). Such an algorithm would mine medical research, health records, and social media data to predict patient treatment preferences. The possibility of developing the AA raises the ethical question of whether the AA or a relative ought to serve as surrogate decision-maker in cases where the patient has not issued a medical power of attorney. We argue that in such cases, and against the standard practice of vesting familial surrogates with decision making authority, the AA should have sole decision-making authority. This is because the AA will likely be better at predicting what treatment option the patient would have chosen. It would also be better at avoiding bias and, therefore, choosing in a more patient-centered manner. Furthermore, we argue that these considerations override any moral weight of the patient's special relationship with their relatives.

Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

In this article, we propose novel techniques to predict a user’s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one’s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user’s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users.

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images

Social networks have attracted the attention of psychologists, as the behavior of users can be used to assess personality traits, and to detect sentiments and critical mental situations such as depression or suicidal tendencies. Recently, the increasing amount of image uploads to social networks has shifted the focus from text to image-based personality assessment. However, obtaining the ground-truth requires giving personality questionnaires to the users, making the process very costly and slow, and hindering research on large populations. In this paper, we demonstrate that it is possible to predict which images are most associated with each personality trait of the OCEAN personality model, without requiring ground-truth personality labels. Namely, we present a weakly supervised framework which shows that the personality scores obtained using specific images textually associated with particular personality traits are highly correlated with scores obtained using standard text-based personality questionnaires. We trained an OCEAN trait model based on Convolutional Neural Networks (CNNs), learned from 120K pictures posted with specific textual hashtags, to infer whether the personality scores from the images uploaded by users are consistent with those scores obtained from text. In order to validate our claims, we performed a personality test on a heterogeneous group of 280 human subjects, showing that our model successfully predicts which kind of image will match a person with a given level of a trait. Looking at the results, we obtained evidence that personality is not only correlated with text, but with image content too. Interestingly, different visual patterns emerged from those images most liked by persons with a particular personality trait: for instance, pictures most associated with high conscientiousness usually contained healthy food, while low conscientiousness pictures contained injuries, guns, and alcohol. These findings could pave the way to complement text-based personality questionnaires with image-based questions.

A psycho-linguistic profile of online grooming conversations: A comparative study of prison and police staff considerations

BACKGROUND

Internet-initiated sexual offences against minors (i.e., online grooming (OG)) is a communicative process of entrapment used by adults to entice minors into sexual activities. Yet, research exploring how the language used by such individuals might reflect their psychological world is scarce. Interestingly, researchers have largely assumed that adults who engage in OG behaviours create a deceptive relationship to reach sexual intent. However, no study to date has investigated the deceptive component of these interactions.

OBJECTIVE

To explore the psycholinguistic and deceptive properties of chats where OG is present, from the perspective of front-line specialists.

PARTICIPANTS AND SETTING

Prison staff (n = 7) and police officers (n = 9) and sixty-five chat logs from adults convicted of an online sexual offence against a minor.

METHODS

A mixed-method approach was employed, whereby qualitative (focus groups) data collection informed subsequent quantitative (the Linguistic Inquiry Word Count (LIWC)) data analyse of adult-child online sexual chats, using the model of OG discourse as the theoretical rationale.

RESULTS

Specialist considered that the online abuse/exploitation of minors can occur without deception. Adults who engage in OG behaviour use language that denotes affiliation with a positive emotional tone (p = .003, η_p² = .59). The communicative focus is the development of an interpersonal relationship, above engagement in sexual talk (p < .001, η_p² = .96).

CONCLUSIONS

Findings challenge the common perception that the relationship is centred on deception and identify the intention of some adults as the development of a perceived genuine interpersonal relationship.

Forbidden knowledge in machine learning reflections on the limits of research and publication

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications.

Toward a Unified View of Cognitive and Biochemical Activity: Meditation and Linguistic Self-Reconstructing May Lead to Inflammation and Oxidative Stress Improvement

Stress appears to be the basis of many diseases, especially myocardial infarction. Events are not objectively “stressful” but what is central is how the individual structures the experience he is facing: the thoughts he produces about an event put him under stress. This cognitive process could be revealed by language (words and structure). We followed 90 patients with ischemic heart disease and 30 healthy volunteers, after having taught them the Relaxation Response (RR) as part of a 4-day Rational–Emotional–Education intervention. We analyzed with the Linguistic Inquiry and Word Count software the words that the subjects used across the study following the progression of blood galectin-3 (inflammation marker) and malondialdehyde (oxidative stress marker). During the follow-up, we confirmed an acute and chronic decrease in the markers of inflammation and oxidative stress already highlighted in our previous studies together with a significant change in the use of language by the subjects of the RR groups. Our results and the precise design of our study would seem to suggest the existence of an intimate relationship and regulatory action by cognitive processes (recognizable by the type of language used) on some molecular processes in the human body.

Understanding breast cancer survivors' information-seeking behaviours and overall experiences: a comparison of themes derived from social media posts and focus groups

OBJECTIVE

Using two different analysis techniques, this study explored differences and similarities in information-seeking discourse and overall breast cancer experiences between posters to a Reddit board and breast cancer survivor focus groups.

DESIGN

This study incorporates two qualitative methods for determining themes in breast cancer survivors' information-seeking behaviours and overall cancer experiences. First, posts from a breast cancer-specific Reddit community were extracted and analysed using the meaning extraction method (MEM) to determine core themes. Then, investigators performed a thematic analysis of two focus groups of breast cancer survivors (N = 18). Finally, themes derived from each analysis method were compared.

MAIN OUTCOME MEASURES

Outcome measures include themes extracted from Reddit posts and themes generated from breast cancer survivor focus groups.

RESULTS

Findings between qualitative methodologies represent similar yet nuanced themes in survivors' discourse. The MEM resulted in seven themes: diagnosis, treatment process, social support, existentialism, risk, information-seeking and surgery. Focus groups revealed the same initial four MEM themes plus the following: disclosure, coping and fears.

CONCLUSIONS

The MEM is a cost-effective research mechanism for informing common themes of experiences of cancer patients and survivors and may offer initial data to guide psychosocial oncology research design and recruitment.

A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media

In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.

The Effect of User Psychology on the Content of Social Media Posts: Originality and Transitions Matter

Multiple studies suggest that frequencies of affective words in social media text are associated with the user's personality and mental health. In this study, we re-examine these associations by looking at the transition patterns of affect. We analyzed the content originality and affect polarity of 4,086 posts from 70 adult Facebook users contributed over 2 months. We studied posting behavior, including silent periods when the user does not post any content. Our results show that more extroverted participants tend to post positive content continuously and that more agreeable participants tend to avoid posting negative content. We also observe that participants with stronger depression symptoms posted more non-original content. We recommend that transitions of affect pattern derived from social media text and content originality should be considered in further studies on mental health, personality, and social media.

Linguistic Predictors of Problematic Drinking in Alcohol-related Facebook Posts

Emerging adults often increase problematic drinking during college. Although they generally do not seek help for problematic drinking, college students discuss their drinking on social media. This study followed college students' Facebook profiles from the inception of their attendance at a university and identified alcohol-related posts. Within 28 days of their first alcohol-related Facebook post, participants were interviewed to assess problematic drinking (binge drinking episodes and number of drinks). Linguistic analysis of alcohol-related Facebook posts found that use of negative emotion language and swear words were related to problematic drinking, in support of proposed hypotheses. Results are situated within alcohol use disorder and health research examining the link between problematic drinking and anxiety, deviant behavior, and negative emotions.

Online writing about positive life experiences reduces depression and perceived stress reactivity in socially inhibited individuals

Therapeutic writing can enhance psychological and physical health. Recent studies have suggested that these kinds of interventions can be effective when delivered online. The present study investigated whether positive emotional writing online can influence psychological and physical health in individuals reporting high levels of negative affectivity, who are most likely to benefit from psychological intervention (N = 72, M_age = 28.5, SD_age = 8.7), and further, to investigate the potential moderating role of social inhibition. Participants completed self-report measures of physical symptoms, perceived stress, perceived stress reactivity, depression and generalised anxiety, before completing either i) positive emotional writing, or ii) a non-emotive control writing task on an online portal, for 20 min per day over three consecutive days. State anxiety was measured immediately after each writing session, and self-report questionnaires were again administered four weeks post-writing. Socially inhibited individuals exhibited significant reductions in depression and perceived stress reactivity four weeks following positive emotional writing, relative to writing about a neutral topic. The present study supports the efficacy of online therapeutic writing in individuals who, due to their socially inhibited nature, are most likely to benefit from online interventions which avoid interaction with a therapist or other clients.

The Development of the Chinese Sentiment Lexicon for Internet

This paper examines the development of the Chinese Sentiment Lexicon for Internet (CSLI), a sentiment lexicon for capturing the valence and arousal in Chinese online social media texts. We first review the current sentiment lexicons and their building process, including the collection of words, judging the emotionality of words, and testing reliability and validity. In Study 1, we develop CSLI and test its initial reliability and validity. In Study 2, we further test the convergent validity of CSLI by examining its correlations with human judgment in 429 aggregated Weibo comments. In Study 3, the predictive validity of CSLI is examined by linking its results to personality traits among 52 undergraduates. Two replication studies are also conducted to verify the findings in Study 2 and 3. The results have generally supported the reliability and validity of CSLI. Therefore, CSLI can be used as a research tool to capture the degree of valence and arousal in Chinese online social media texts. Its potential to promote human well-being is also discussed.

Pathways to conspiracy: The social and linguistic precursors of involvement in Reddit's conspiracy theory forum

Many individuals who engage with conspiracy theories come to do so through a combination of individual and social factors. The interaction between these factors is challenging to study using traditional experimental designs. Reddit.com is a large connected set of online discussion forums, including one (r/conspiracy) devoted to wide-ranging discussion of conspiracy theories. The availability of large datasets of user comments from Reddit give a unique opportunity to observe human behavior in social spaces and at scale. Using a retrospective case control study design, we analyzed how Reddit users who would go on to engage with a conspiracy-related forum differed from other users in the language they use, differences in the social environments where they posted, and potential interactions between the two factors. Together, the analyses provide evidence for self-selection into communities with a shared set of interests which can feed into a conspiratorial world-view, and that these differences are detectable relative to controls even before users begin to post in r/conspiracy. We also suggest that survey-based and experimental studies may benefit from differentiating between passive private endorsement by individuals and active engagement with conspiracy theories in social spaces.

How to tell a happy person: Accuracy of subjective well-being perception from texts

Although perceptions of subjective well-being (SWB) in unacquainted others have been shown to play a major role in impression formation, little is known about how accurate such perceptions are. In two original studies and one pre-registered replication, we explored the accuracy of life satisfaction and happiness judgments from texts and its underlying mechanisms (use of linguistic cues). Participants filled in life satisfaction and happiness measures and completed a brief writing task. Another sample of participants judged the targets’ life satisfaction and happiness from the obtained texts. All three studies demonstrated a small to moderate self-other agreement. A linguistic analysis showed that targets with higher (vs. lower) scores on SWB were less likely to use negation words in their texts, which allowed observers to make accurate judgment of their SWB level. Two studies pointed at negative emotion words as valid and positive emotion words as invalid (but often used) cues to happiness, yet these effects did not replicate in Study 3.

Privacy in the age of psychological targeting

Psychological targeting describes the practice of extracting people's psychological profiles from their digital footprints (e.g. their Facebook Likes, Tweets or credit card records) in order to influence their attitudes, emotions or behaviors through psychologically informed interventions at scale. We discuss how the increasingly blurred lines between public and private information, and the continuation of the outdated practices of notice and consent, challenge traditional conceptualizations of privacy in the context of psychological targeting. Drawing on the theory of contextual integrity, we argue that it is time to rethink privacy and move beyond the questions of who collects what data to how the data are being used. Finally, we suggest that regulations of psychological targeting should be accompanied by a mindset that fosters (1) privacy by design to make it easy for individuals to act in line with their privacy goals, as well as (2) disclosure by choice, to allow individuals to freely decide whether and when they might be willing to forsake their privacy for better service.

Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

Trusting Virtual Agents

We present artificial intelligent (AI) agents that act as interviewers to engage with a user in a text-based conversation and automatically infer the user's personality traits. We investigate how the personality of an AI interviewer and the inferred personality of a user influences the user's trust in the AI interviewer from two perspectives: the user's willingness to confide in and listen to an AI interviewer. We have developed two AI interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four such deployments involving 1,280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to an AI interviewer with a serious, assertive personality in a high-stakes job interview. Moreover, users’ personality traits, inferred from their chat text, along with interview context, influence their perception of and their willingness to confide in and listen to an AI interviewer. Finally, we discuss the design implications of our work on building hyper-personalized, intelligent agents.

On the Content of "Real-World" Sexual Fantasy: Results From an Analysis of 250,000+ Anonymous Text-Based Erotic Fantasies

A recurring problem with the study of sexual fantasy is that of social desirability bias. Study participants may report fantasies that are consistent with general societal expectations of fantasy content, as opposed to themes characterized by their actual fantasies. The wide availability of erotic material on the Internet, however, facilitates the study of sexual fantasy narratives as they are anonymously expressed and viewed online. By extracting approximately 250,000 text-based erotic fantasies from a user-generated website, we sought to examine "real-world" sexual fantasies, determine the themes that were typical of these narratives, and explore the relationship between themes and story popularity (as assessed by story views per day). A principal components analysis identified 20 themes that commonly occurred across the massive corpus, and a path analysis revealed that these themes played a significant role in predicting the popularity of the sexual fantasy narratives. In particular, the empirically identified themes reflecting familial words (e.g., mother, father) and colloquial sexual words (e.g., cock, fuck) were predictive of story popularity. Other themes identified included those not obviously erotic, such as those consisting of words reflecting domesticity (e.g., towel, shower) and colors (e.g., brown, blue). By analyzing a sexual fantasy corpus of unprecedented size, this study offers unique insight into both the content of sexual fantasies and the popularity of that content.

What Does Sexual Arousal Mean to You? Women With and Without Sexual Arousal Concerns Describe Their Experiences

Sexual arousal is frequently characterized by both subjective (i.e., mental) and physiological (e.g., genital) components. The nuances of these components, however, are difficult to capture via self-report instruments. Asking women to describe sexual arousal in their own words may therefore enhance our understanding of this construct. In the present study, women with (n = 190) and without (n = 610) arousal concerns were recruited online and wrote about their experience of sexual arousal. Seven clusters of words were extracted using automated text analysis, and the prominence of these clusters was compared between groups of women. The autonomic arousal cluster differed between groups such that women with arousal concerns invoked this cluster significantly less than did women with no such concerns. Furthermore, the context cluster significantly predicted group membership (odds ratio [OR] = 1.063); greater scores on this cluster were associated with arousal concerns. Results suggest that autonomic arousal and relationship factors may play important roles in arousal concerns. It is suggested that clinicians assess for aspects of the sexual relationship that may facilitate or hinder sexual arousal. Clinicians may also consider inquiring about the presence or appraisal of autonomic arousal (e.g., one's interpretation of an increase in heart rate or respiration) during sexual activity.

Stronger Together: Personality, Intelligence and the Assessment of Career Potential

Personality and intelligence have a long history in applied psychology, with research dating back more than 100 years. In line, early developments in industrial-organizational psychology were largely founded on the predictive power of personality and intelligence measures vis-à-vis career-related outcomes. However, despite a wealth of evidence in support of their utility, the concepts, theories, and measures of personality and intelligence are still widely underutilized in organizations, even when these express a commitment to making data-driven decisions about employees and leaders. This paper discusses the value of personality and intelligence to understand individual differences in career potential, and how to increase the adoption of theories and tools for evaluating personality and intelligence in real-world organizational contexts. Although personality and intelligence are distinct constructs, the assessment of career potential is incomplete without both.

Who says “larger” and who says “smaller”? Individual differences in the language of comparison

When comparing a pair of attribute values, English speakers can use a “larger” comparative (“A is larger/longer/higher/more than B”) or a “smaller” comparative (“B is smaller/shorter/lower/less than A”). This choice matters because it affects people’s inferences about the absolute magnitudes of the compared items, and influences the perceived truthfulness of the comparative sentence itself. In 4 studies (total N = 2335), we investigated the language that people use to describe ordinal relations between attributes. Specifically, we examined whether demography, emotion, and personality predict the tendency to use “larger” comparatives rather than “smaller” ones. Participants viewed pairs of items differing in a single attribute and indicated the word they would use to describe the relationship between them; they also completed a series of self-report measures. Replicating previous work, we found a robust tendency to use “larger” comparatives, both when people chose between two adjectives and when they freely produced their own words in a sentence completion task. We also found that this tendency was more pronounced in older participants, those with positive mood or outlook, and among people high in agreeableness, conscientiousness, and emotional stability. However, these effects were very small, with meta-analytic effect sizes indicating they explain less than 1% of the variance. We conclude that, although people’s use of comparative adjectives is influenced by properties of the items that are being compared, the way that people describe magnitude relations is relatively stable across variation in a range of important traits and dispositions, protecting decision-makers from a potentially undesirable source of bias in their inferences and representations of described options.

Digital Footprints of Sensation Seeking

Abstract. The increasing usage of new technologies implies changes for personality research. First, human behavior becomes measurable by digital data, and second, digital manifestations to some extent replace conventional behavior in the analog world. This offers the opportunity to investigate personality traits by means of digital footprints. In this context, the investigation of the personality trait sensation seeking attracted our attention as objective behavioral correlates have been missing so far. By collecting behavioral markers (e.g., communication or app usage) via Android smartphones, we examined whether self-reported sensation seeking scores can be reliably predicted. Overall, 260 subjects participated in our 30-day real-life data logging study. Using a machine learning approach, we evaluated cross-validated model fit based on how accurate sensation seeking scores can be predicted in unseen samples. Our findings highlight the potential of mobile sensing techniques in personality research and show exemplarily how prediction approaches can help to foster an increased understanding of human behavior.

Women's Word Use in Pregnancy: Associations With Maternal Characteristics, Prenatal Stress, and Neonatal Birth Outcome

Background: Experiencing high levels of stress during pregnancy can impair maternal well-being and fetal development. Consequently, unbiased assessment of maternal psychological state is crucial. Self-report measures are vulnerable to social desirability effects. Thus, implicit measures, such as word choice analysis, may offer an alternative.

Methods: In this longitudinal online-study, 427 pregnant women described their emotional experiences in writing and additionally responded to self-report questionnaires assessing symptoms of prenatal stress and depression. The written texts were analyzed with a computerized text analysis program. After birth, 253 women provided information on birth outcome.

Results: Word use differed significantly depending on maternal socioeconomic (e.g., marital status) and pregnancy-related characteristics (e.g., parity). Prenatal stress and depressive symptoms were associated with more frequent use of negative emotion words and words of anxiety, as well as with less first-person plural, but not singular pronoun use. Negative emotion and cognitive mechanism words predicted birth outcome, while self-report measures did not.

Conclusion: In addition to self-report measures, word choice may serve as a useful screening tool for symptoms of depression and stress in pregnant women. The findings on pronoun use may reflect women’s changing experience of self-identity during the transition to motherhood.

Mental profile mapping: A psychological single-candidate authorship attribution method

Modern authorship attribution methods are often comprised of powerful yet opaque machine learning algorithms. While much of this work lends itself to concrete outcomes in the form of probability scores, advanced approaches typically preclude deeper insights in the form of psychological interpretation. Additionally, few attribution methods exist for single-candidate authorship problems, most of which require large amounts of supplemental data to perform and none of which rely upon explicitly psychological measures. The current study introduces Mental Profile Mapping, a new authorship attribution technique for single-candidate authorship questions that is founded on previous scientific research pertaining to the nature of language and psychology. In the current study, baseline expectations for results and performance are set using an advanced technique known as "unmasking" on the test case of Aphra Behn, a 17th century English playwright. Following this, Mental Profile Mapping is introduced and tested for its psychometric properties, tested using a "bogus insertion" method, and then applied to canonical Aphra Behn plays. Results from both attribution methods suggest that 2 of 5 questioned plays are likely to have been authored by Behn, with the remaining 3 plays exhibiting a poor fit for Behn's psychological fingerprint. Mental Profile Mapping results are then decomposed into deeper psychological interpretation, a quality unique to this new method.

Personality development through natural language

Ego level is a broad construct that summarizes individual differences in personality development ¹ . We examine ego level as it is represented in natural language, using a composite sample of four datasets comprising nearly 44,000 responses. We find support for a developmental sequence in the structure of correlations between ego levels, in analyses of Linguistic Inquiry and Word Count (LIWC) categories ² and in an examination of the individual words that are characteristic of each level. The LIWC analyses reveal increasing complexity and, to some extent, increasing breadth of perspective with higher levels of development. The characteristic language of each ego level suggests, for example, a shift from consummatory to appetitive desires at the lowest stages, a dawning of doubt at the Self-aware stage, the centrality of achievement motivation at the Conscientious stage, an increase in mutuality and intellectual growth at the Individualistic stage and some renegotiation of life goals and reflection on identity at the highest levels of development. Continuing empirical analysis of ego level and language will provide a deeper understanding of ego development, its relationship with other models of personality and individual differences, and its utility in characterizing people, texts and the cultural contexts that produce them.

The Socio-Moral Image Database (SMID): A novel stimulus set for the study of social, moral and affective processes

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.

Online cancer communities as informatics intervention for social support: conceptualization, characterization, and impact

Objectives: The Internet and social media are revolutionizing how social support is exchanged and perceived, making online health communities (OHCs) one of the most exciting research areas in health informatics. This paper aims to provide a framework for organizing research of OHCs and help identify questions to explore for future informatics research. Based on the framework, we conceptualize OHCs from a social support standpoint and identify variables of interest in characterizing community members. For the sake of this tutorial, we focus our review on online cancer communities.

Target audience: The primary target audience is informaticists interested in understanding ways to characterize OHCs, their members, and the impact of participation, and in creating tools to facilitate outcome research of OHCs. OHC designers and moderators are also among the target audience for this tutorial.

Scope: The tutorial provides an informatics point of view of online cancer communities, with social support as their leading element. We conceptualize OHCs according to 3 major variables: type of support, source of support, and setting in which the support is exchanged. We summarize current research and synthesize the findings for 2 primary research questions on online cancer communities: (1) the impact of using online social support on an individual's health, and (2) the characteristics of the community, its members, and their interactions. We discuss ways in which future research in informatics in social support and OHCs can ultimately benefit patients.