The language of character strengths: Predicting morally valued traits on social media

Natural Language Content Mediates the Association Between Active Interactions on Social Network Services and Subjective Well-Being

Previous studies indicated that active interactions on social networking services (SNS) are positively linked to subjective well-being (SWB). However, how semantic SNS content affects the association between the degree of SNS interaction and SWB has not been investigated. We addressed this issue by conducting a mediation analysis using natural language processing. We first analyzed Twitter data and SWB scores from 217 participants and found that the degree of active interactions on Twitter (i.e., frequency of reply) was positively correlated with SWB. Next, our multivariate mediation analysis demonstrated that positive words served as SWB-promoting mechanisms for highly interactive people, whereas worrying words led to lower SWB for less interactive people, but negative words did not. This study revealed that natural language content explains why individuals who are highly interactive on SNS have higher SWB, whereas less interactive individuals show lower SWB.

Understanding farmers' naturalistic decision making around prophylactic antibiotic use in lambs using a grounded theory and natural language processing approach

The routine use of antibiotics for prevention of disease in neonatal lambs has been highlighted as inappropriate, yet research suggests that many farmers in the UK still carry out this practice. The aim of the study was to understand farmers' naturalistic decision-making around prophylactic antibiotic use in lambs. Data from 431 posts by 133 different users of an online discussion forum were analysed quantitatively using natural language processing and qualitatively using a grounded theory approach. Results from the qualitative analysis identified five categories that influenced farmers risk perceptions around prophylactic antibiotic use in lambs: anticipated regret, negative emotions and experiential avoidance; economic considerations; farmer identity; perception of capability; and perception of social judgement. Natural language processing analysis of the posts by the study group were compared to posts on topics unrelated to antibiotic use by control groups from the same forum to understand the underlying style and tone within the text. Analytical thinking and authenticity scores were significantly lower in the study group compared to the control groups (P < 0.01). Words relating to cognitive processes were significantly higher in the study group compared to the control groups (P < 0.01). Results of the qualitative and quantitative analysis were integrated to assess the fit of the data and enhance findings from either method alone. The key findings were the identification of reasons why farmers used antibiotics prophylactically in neonatal lambs. Farmers disassociated the use of oral antibiotics in neonatal lambs from other types of antibiotics within sheep farming. Farmers used the concept of luck and uniqueness to justify their prophylactic antibiotic use. This may explain farmers' low analytical thinking scores as this reflects a more personal style of thinking as farmers express their thoughts on antibiotic use on an individual basis. Farmers' felt that they did not have the capabilities to control neonatal disease without antibiotics during busy periods and this was exacerbated by external economic constraints. For farmers' who used antibiotics for therapeutic use, fear of social judgement was an important factor in their perceptions of risk around antibiotic use. The high frequency of negations and conjunctions suggested feelings of moral duty. As such, antibiotic use has developed into a symbol of their good farming identity which conflicted with the identity held by those who used antibiotics prophylactically. These results can be used to inform knowledge exchange around prophylactic antibiotic use in lambs to improve antibiotic stewardship in the sheep farming sector.

The language of character strengths: Predicting morally valued traits on social media

OBJECTIVE

Social media is increasingly being used to study psychological constructs. This study is the first to use Twitter language to investigate the 24 Values in Action Inventory of Character Strengths, which have been shown to predict important life domains such as well-being.

METHOD

We use both a top-down closed-vocabulary (Linguistic Inquiry and Word Count) and a data-driven open-vocabulary (Differential Language Analysis) approach to analyze 3,937,768 tweets from 4,423 participants (64.3% female), who answered a 240-item survey on character strengths.

RESULTS

We present the language profiles of (a) a global positivity factor accounting for 36% of the variances in the strengths, and (b) each of the 24 individual strengths, for which we find largely face-valid language associations. Machine learning models trained on language data to predict character strengths reach out-of-sample prediction accuracies comparable to previous work on personality (r_median = 0.28, ranging from 0.13 to 0.51).

CONCLUSIONS

The findings suggest that Twitter can be used to characterize and predict character strengths. This technique could be used to measure the character strengths of large populations unobtrusively and cost-effectively.