Psychological targeting as an effective approach to digital mass persuasion

People are exposed to persuasive communication across many different contexts: governments, companies, and political parties use persuasive appeals to encourage people to eat healthier, purchase a particular product, or vote for a specific candidate. Laboratory studies show that such persuasive appeals are more effective in influencing behavior when they are tailored to individuals’ unique psychological characteristics. Yet, the investigation of large-scale psychological persuasion in the real world has been hindered by the questionnaire-based nature of psychological assessment. Recent research, however, shows that people’s psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook Likes or Tweets. Capitalizing on this new form of psychological assessment from digital footprints, we test the effects of psychological persuasion on people’s actual behavior in an ecologically valid setting. In three field experiments that reached over 3.5 million individuals with psychologically-tailored advertising, we find that matching the content of persuasive appeals to individuals’ psychological characteristics significantly altered their behavior as measured by clicks and purchases. Persuasive appeals that were matched to people’s extraversion or openness-to-experience level resulted in up to 40% more clicks and up to 50% more purchases than their mismatching or un-personalized counterparts. Our findings suggest that the application of psychological targeting makes it possible to influence the behavior of large groups of people by tailoring persuasive appeals to the psychological needs of the target audiences. We discuss both the potential benefits of this method for helping individuals make better decisions and the potential pitfalls related to manipulation and privacy

Building a profile of subjective well-being for social media users

Subjective well-being includes ‘affect’ and ‘satisfaction with life’ (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users’ affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.Gong acknowledges support by the US National Institutes of Health Grant (HD-071988). The study is also supported in part by the MOE Project of the Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies. However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Musical preferences are linked to cognitive styles

Why do we like the music we do? Research has shown that musical preferences and personality are linked, yet little is known about other influences on preferences such as cognitive styles. To address this gap, we investigated how individual differences in musical preferences are explained by the empathizing-systemizing (E-S) theory. Study 1 examined the links between empathy and musical preferences across four samples. By reporting their preferential reactions to musical stimuli, samples 1 and 2 (Ns = 2,178 and 891) indicated their preferences for music from 26 different genres, and samples 3 and 4 (Ns = 747 and 320) indicated their preferences for music from only a single genre (rock or jazz). Results across samples showed that empathy levels are linked to preferences even within genres and account for significant proportions of variance in preferences over and above personality traits for various music-preference dimensions. Study 2 (N = 353) replicated and extended these findings by investigating how musical preferences are differentiated by E-S cognitive styles (i.e., 'brain types'). Those who are type E (bias towards empathizing) preferred music on the Mellow dimension (R&B/soul, adult contemporary, soft rock genres) compared to type S (bias towards systemizing) who preferred music on the Intense dimension (punk, heavy metal, and hard rock). Analyses of fine-grained psychological and sonic attributes in the music revealed that type E individuals preferred music that featured low arousal (gentle, warm, and sensual attributes), negative valence (depressing and sad), and emotional depth (poetic, relaxing, and thoughtful), while type S preferred music that featured high arousal (strong, tense, and thrilling), and aspects of positive valence (animated) and cerebral depth (complexity). The application of these findings for clinicians, interventions, and those on the autism spectrum (largely type S or extreme type S) are discussed.This work was supported by the Medical Research Council

Predicting individual-level income from Facebook profiles

Information about a person's income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person's income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6-16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent

Living in the past, present, and future: measuring temporal orientation with language

OBJECTIVE: Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. METHOD: Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female, aged 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. RESULTS: Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. CONCLUSION: Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships. This article is protected by copyright. All rights reserved.Support for this article was provided by grant #63597 from the Robert Wood Johnson Foundation (M. E. P. Seligman, PI) and by a grant from the Templeton Religion Trust (M.E.P. Seligman, H. A. Schwartz, L. H. Ungar, co-PIs)

The language of religious affiliation: social, emotional, and cognitive differences

Religious affiliation is an important identifying characteristic for many individuals and relates to numerous life outcomes including health, well-being, policy positions, and cognitive style. Using methods from computational linguistics, we examined language from 12,815 Facebook users in the United States and United Kingdom who indicated their religious affiliation. Religious individuals used more positive emotion words (β = .278, p < .0001) and social themes such as family (β = .242, p < .0001), while nonreligious people expressed more negative emotions like anger (β = −.427, p < .0001) and categories related to cognitive processes, like tentativeness (β = −.153, p < .0001). Nonreligious individuals also used more themes related to the body (β = −.265, p < .0001) and death (β = −.247, p < .0001). The findings offer directions for future research on religious affiliation, specifically in terms of social, emotional, and cognitive differences

Sex-biased parental care and sexual size dimorphism in a provisioning arthropod

The diverse selection pressures driving the evolution of sexual size dimorphism (SSD) have long been debated. While the balance between fecundity selection and sexual selection has received much attention, explanations based on sex-specific ecology have proven harder to test. In ectotherms, females are typically larger than males, and this is frequently thought to be because size constrains female fecundity more than it constrains male mating success. However, SSD could additionally reflect maternal care strategies. Under this hypothesis, females are relatively larger where reproduction requires greater maximum maternal effort – for example where mothers transport heavy provisions to nests. To test this hypothesis we focussed on digger wasps (Hymenoptera: Ammophilini), a relatively homogeneous group in which only females provision offspring. In some species, a single large prey item, up to 10 times the mother’s weight, must be carried to each burrow on foot; other species provide many small prey, each flown individually to the nest. We found more pronounced female-biased SSD in species where females carry single, heavy prey. More generally, SSD was negatively correlated with numbers of prey provided per offspring. Females provisioning multiple small items had longer wings and thoraxes, probably because smaller prey are carried in flight. Despite much theorising, few empirical studies have tested how sex-biased parental care can affect SSD. Our study reveals that such costs can be associated with the evolution of dimorphism, and this should be investigated in other clades where parental care costs differ between sexes and species

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another

Dietary n-3 fatty acids have suppressive effects on mucin upregulation in mice infected with Pseudomonas aeruginosa

International audienceMucin hypersecretion and mucus plugging in the airways are characteristic features of chronic respiratory diseases like cystic fibrosis (CF) and contribute to morbidity and mortality. In CF, Pseudomonas aeruginosa superinfections in the lung exacerbate inflammation and alter mucus properties. There is increasing evidence that n-3 polyunsaturated fatty acids (PUFAs) exhibit anti-inflammatory properties in many inflammatory diseases while n-6 PUFA arachidonic acid (AA) favors inflammatory mediators such as eicosanoids prostaglandin E2 (PGE2) and leukotriene B4 (LTB4) that may enhance inflammatory reactions. This suggests that n-3 PUFAs may have a protective effect against mucus over-production in airway diseases. Therefore, we hypothesized that n-3 PUFAs may downregulate mucins expression. We designed an absolute real-time PCR assay to assess the effect of a 5-week diet enriched either with n-3 or n-6 PUFAs on the expression of large mucins in the lungs of mice infected by P. aeruginosa. Dietary fatty acids did not influence mucin gene expression in healthy mice. Lung infection induced an increase of the secreted gel-forming mucin Muc5b and a decrease of the membrane bound mucin Muc4. These deregulations are modulated by dietary fatty acids with a suppressive effect of n-3 PUFAs on mucin (increase of Muc5b from 19-fold up to 3.6 x 10(5)-fold for the n-3 PUFAs treated group and the control groups, respectively, 4 days post-infection and decrease of Muc4 from 15-fold up to 3.2 x 10(4)-fold for the control and the n-3 PUFAs treated groups, respectively, 4 days post-infection). Our data suggest that n-3 PUFAs enriched diet represents an inexpensive strategy to prevent or treat mucin overproduction in pulmonary bacterial colonization