Dirty laundry: The nature and substance of seeking relationship help from strangers online

Interpersonal relationships are vital to our well-being. In recent years, it has become increasingly common to seek relationship help through anonymous online platforms. Accordingly, we conducted a large-scale analysis of real-world relationship help-seeking to create a descriptive overview of the nature and substance of online relationship help-seeking. By analyzing the demographic characteristics and language of relationship help-seekers on Reddit (N = 184,631), we establish the first-ever big data analysis of relationship help-seeking and relationship problems in situ among the general population. Our analyses highlight real-world relationship struggles found in the general population, extending beyond past work that is typically limited to counseling/intervention settings. We find that relationship problem estimates from our sample are closer to those found in the general population, providing a more generalized insight into the distribution and prevalence of relationship problems as compared with past work. Further, we find several meaningful associations between relationship help-seeking behavior, gender, and attachment. Notably, numerous gender differences in help-seeking and romantic attachment emerged. Our findings suggest that, contrary to more traditional contexts, men are more likely to seek help with their relationships online, are more expressive of their emotions (e.g., discussing the topic of "heartache"), and show language patterns generally consistent with more secure attachment. Our analyses highlight pathways for further exploration, providing even deeper insights into the timing, lifecycle, and moderating factors that influence who, what, why, and how people seek help for their interpersonal relationships

Language-based personality:a new approach to personality in a digital world

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality — people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at ‘big data’ scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research

Personality Disorder and Verbal Behavior

Today, the development of new technologies means that there are many advanced tools that can be used to improve our understanding of personality disorder, and, in turn, the treatment of personality disorder. One particularly promising tool — indeed, the focus of this chapter — is computerized language analysis. Through the exploration and analysis of verbal behavior, it is possible to empirically develop new insights into personality disorder, broadly defined. That is, by looking at patterns in the way that people with personality disorder use language — the words that they use and the way in which they use them — we can gain access into their broad constellation of thinking, feelings, and behaviors, as well as how precisely each of these features contributes to their pathology. To date, however, there has been very little research at the intersection of verbal behavior and personality pathology. Accordingly, the goal of this chapter is to describe and discuss how personality disorder may become better understood through the application of natural language analysis, providing a rough roadmap for the development of personality disorder studies using modern methods. Specifically, in this chapter we will provide: 1. A brief background and overview of personality disorder; 2. An overview of how natural language processing (NLP) methods have advanced understanding within the wider field of psychology, focusing on personality psychology and psychopathology specifically; 3. Examples that demonstrate how NLP methods can help to resolve some of the fundamental, unanswered questions and debates in the personality disorder literature

Non-collaborative Attackers and How and Where to Defend Flawed Security Protocols (Extended Version)

Security protocols are often found to be flawed after their deployment. We present an approach that aims at the neutralization or mitigation of the attacks to flawed protocols: it avoids the complete dismissal of the interested protocol and allows honest agents to continue to use it until a corrected version is released. Our approach is based on the knowledge of the network topology, which we model as a graph, and on the consequent possibility of creating an interference to an ongoing attack of a Dolev-Yao attacker, by means of non-collaboration actuated by ad-hoc benign attackers that play the role of network guardians. Such guardians, positioned in strategical points of the network, have the task of monitoring the messages in transit and discovering at runtime, through particular types of inference, whether an attack is ongoing, interrupting the run of the protocol in the positive case. We study not only how but also where we can attempt to defend flawed security protocols: we investigate the different network topologies that make security protocol defense feasible and illustrate our approach by means of concrete examples.Comment: 29 page

You Do Not Have to Get through This Alone: Interpersonal Emotion Regulation and Psychosocial Resources during the COVID-19 Pandemic across Four Countries

While experiencing the unpredictable events of the COVID-19 pandemic, we are likely to turn to people in order to regulate our emotions. In this research, we investigate how this interpersonal emotion regulation is connected to affective symptoms, above and beyond intrapersonal emotion regulation. Furthermore, we explore whether perceived psychosocial resources moderate these associations, i.e., if individuals reporting healthier social connections benefit differently from interpersonal emotion regulation. N = 1401 participants from the USA, UK, Germany, and Switzerland completed an online survey that included text samples. Affective symptoms (depression, adjustment disorder, fear of COVID-19) were examined based on self-reported as well as language-based indicators. As psychosocial resources, we examined social support, loneliness, attachment style, and trust. We defined latent variables for adaptive and maladaptive interpersonal emotion regulation and analyzed how they were associated with affective symptoms controlling for intrapersonal emotion regulation. Further, we analyzed how they interacted with psychosocial resources. Maladaptive interpersonal emotion regulation strategies were associated with affective symptoms. With lower psychosocial resources, the associations between interpersonal emotion regulation and depressive symptoms were more pronounced. The results highlight that maladaptive interpersonal emotion regulation is associated with worse mental health. These effects are not buffered by more psychosocial resources and are stronger for people with low psychosocial resources

Natural emotion vocabularies as windows on distress and well-being

To date we know little about natural emotion word repertoires, and whether or how they are associated with emotional functioning. Principles from linguistics suggest that the richness or diversity of individuals’ actively used emotion vocabularies may correspond with their typical emotion experiences. The current investigation measures active emotion vocabularies in participant-generated natural speech and examined their relationships to individual differences in mood, personality, and physical and emotional well-being. Study 1 analyzes stream-of-consciousness essays by 1,567 college students. Study 2 analyzes public blogs written by over 35,000 individuals. The studies yield consistent findings that emotion vocabulary richness corresponds broadly with experience. Larger negative emotion vocabularies correlate with more psychological distress and poorer physical health. Larger positive emotion vocabularies correlate with higher well-being and better physical health. Findings support theories linking language use and development with lived experience and may have future clinical implications pending further research

Novel cyclic di-GMP effectors of the YajQ protein family control bacterial virulence

Bis-(3 ',5 ') cyclic di-guanylate (cyclic di-GMP) is a key bacterial second messenger that is implicated in the regulation of many critical processes that include motility, biofilm formation and virulence. Cyclic di-GMP influences diverse functions through interaction with a range of effectors. Our knowledge of these effectors and their different regulatory actions is far from complete, however. Here we have used an affinity pull-down assay using cyclic di-GMP-coupled magnetic beads to identify cyclic di-GMP binding proteins in the plant pathogen Xanthomonas campestris pv. campestris (Xcc). This analysis identified XC_3703, a protein of the YajQ family, as a potential cyclic di-GMP receptor. Isothermal titration calorimetry showed that the purified XC_3703 protein bound cyclic di-GMP with a high affinity (K-d similar to 2 mu M). Mutation of XC_3703 led to reduced virulence of Xcc to plants and alteration in biofilm formation. Yeast two-hybrid and far-western analyses showed that XC_3703 was able to interact with XC_2801, a transcription factor of the LysR family. Mutation of XC_2801 and XC_3703 had partially overlapping effects on the transcriptome of Xcc, and both affected virulence. Electromobility shift assays showed that XC_3703 positively affected the binding of XC_2801 to the promoters of target virulence genes, an effect that was reversed by cyclic di-GMP. Genetic and functional analysis of YajQ family members from the human pathogens Pseudomonas aeruginosa and Stenotrophomonas maltophilia showed that they also specifically bound cyclic di-GMP and contributed to virulence in model systems. The findings thus identify a new class of cyclic di-GMP effector that regulates bacterial virulence