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The increasing utilization of social media provides a vast and new source of user-generated ecological data (digital traces), which can be automatically collected for research purposes. The availability of these data sets, combined with the convergence between social and computer sciences, has led researchers to develop automated methods to extract digital traces from social media and use them to predict individual psychological characteristics and behaviors. In this article, we reviewed the literature on this topic and conducted a series of meta-analyses to determine the strength of associations between digital traces and specific individual characteristics; personality, psychological well-being, and intelligence. Potential moderator effects were analyzed with respect to type of social media platform, type of digital traces examined, and study quality. Our findings indicate that digital traces from social media can be studied to assess and predict theoretically distant psychosocial characteristics with remarkable accuracy. Analysis of moderators indicated that the collection of specific types of information (i.e., user demographics), and the inclusion of different types of digital traces, could help improve the accuracy of predictions.

Recent literature suggests that variations in both formal and content aspects of texts shared on social media tend to reflect user-level differences in demographic, psychosocial, and behavioral characteristics. In the present study, we examined associations between language use on Facebook and problematic alcohol use. We collected texts shared on Facebook by a sample of 296 adult social media users (66.9% females; mean age = 28.44 years (SD = 7.38)). Texts were mined using the closed-vocabulary approach based on the Linguistic Inquiry Word Count (LIWC) semantic dictionary, and an open-vocabulary approach performed via Latent Dirichlet Allocation (LDA). Then, we examined associations between emerging textual features and alcohol-drinking scores as assessed using the AUDIT-C questionnaire. As a final aim, we employed the Random Forest machine-learning algorithm to determine and compare the predictive accuracy of closed- and open-vocabulary features over users' AUDIT-C scores. We found use of words about family, school, and positive feelings and emotions to be negatively associated with alcohol use and problematic drinking, while words suggesting interest in sport events, politics and economics, nightlife, and use of coarse language were more frequent among problematic drinkers. Results coming from LIWC and LDA analyses were quite similar, but LDA added information that could not be retrieved only with LIWC analysis. Furthermore, open-vocabulary features outperformed closed-vocabulary features in terms of predictive power over participants’ AUDIT-C scores (r = .46 vs. r = .28, respectively). Emerging relationships between text features and offline behaviors may have important implications for alcohol screening purposes in the online environment.

Research indicates that how individuals utilise language to express themselves reflects individuallevel differences regarding psychosocial characteristics, including perceived Quality of Life (QoL). In this study, we apply a language modelling technique to the natural user-generated language from Facebook to examine associations between language expressed on Facebook and self-reported QoL. Specifically, we collected the user-generated language from a sample of 603 Facebook users (76.3% females), mined emerging text corpora using the LIWC closed-vocabulary approach, and examined associations between LIWC features and self-reported domain-specific QoL (Physical, Psychological, Social), and General QoL. In line with previous research, we found use of pronouns, negative emotions, death and sleep words, and use of profanity to be significantly associated with QoL. Next, we used the Random Forest algorithm to test the predictability of QoL dimensions based on LIWC features and posting activity statistics. The models achieved moderate predictive power (r ranging from .22 to .33), the Psychological and General QoL dimensions showing the highest accuracy. An alternative approach combining LIWC features, posting activity, and predicted scores for domain-specific QoL components showed increased accuracy when predicting General QoL (r = .43). Findings are discussed in light of previous literature. Suggestions for improving models in future studies are provided.

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Companies are called upon to solve the great challenges of the new millennium. The food sector, from this point of view, plays a strategic role. Poverty, malnutrition, hunger, climate change, and social inequalities are just some of the trends which the agri-food sector has to cope with. The digital transformation that companies will need to embrace to survive requires new ways of creating, thinking, and working with technology-driven tools to provide value for their businesses and customers. Digitization, whether it pertains to new technologies, the analysis of big data or the development of on-line and spatial applications, can contribute to achieving systemic food production transformation in a way that aligns the sector more closely with contemporary sustainability and health challenges. Digital techniques are leading established companies to renew and innovate their business models by connecting producers to consumers, setting up innovative marketing channels, and improving logistics. Artificial intelligence for smart farming, precision and urban farming, data management for waste-less, blockchain for supply chain traceability and auditability are just some of the disruptive technologies which have been adopting by both start-ups and an increasing number of established companies, redefining their business models. This chapter aims to analyse how these new paradigms are impacting the food sector by providing examples from the real world.

Frailty increases individual vulnerability to external stressors and involves high risk for adverse geriatric outcomes. To date, few studies have addressed the role of emotion perception and its association with frailty in aged populations. This cross-sectional study aimed to explore whether a significant association between frailty and emotional experience exists in a sample of Italian community-dwelling older adults. Our sample consisted of 104 older adults (age 76±8 years; 59.6% women) living in Piedmont, Italy. Frailty was measured using the Italian version of the Tilburg Frailty Indicator (TFI), and emotion perception was measured with the Positive and Negative Affect Schedule (PANAS). The Mini–Mental State Examination was used as a screening tool for cognitive functions (people with a score ≤20 points were excluded). One-way analysis of covariance (ANCOVA), adjusted for interesting variables, and post hoc tests were performed where appropriate. According to the TFI, 57.7% of participants resulted as frail. Analysis showed a significant greater severity of frailty in the low positive affect (PA) group compared to the high PA group. Similarly, those with high negative affect (NA) showed significantly higher levels of frailty than the low NA group. As expected, significant differences for frailty were also found among the groups composed of 1) people with high PA and low NA, 2) people with low PA or high NA, and 3) people with low PA and high NA. Post hoc tests showed a greater severity of frailty in the second and in the third groups compared to the first one. Lastly, robust participants aged >75 years showed higher levels of PA than the group aged between 60 and 75 years. These findings demonstrate that both PA and NA may influence frailty, giving new insights for the evaluation and prevention of frailty in older adults.

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