Language use on Twitter reflects social structure and social disparities.

Large-scale mental health assessments increasingly rely upon user-contributed social media data. It is widely known that mental health and well-being are affected by minority group membership and social disparity. But do these factors manifest in the language use of social media users? We elucidate this question using spatial lag regressions. We examined the county-level (N = 1069) associations of lexical indicators linked to well-being and mental health, notably depression (e.g., first-person singular pronouns, negative emotions) with markers of social disparity (e.g., the Area Deprivation Index-3) and ethnicity, using a sample of approximately 30 million content-coded tweets (U.S. county-level aggregation). Results confirmed most expected associations: County-level lexical indicators of depression are positively linked with county-level area disparity (e.g., economic hardship and inequity) and percentage of ethnic minority groups. Predictive validity checks show that lexical indicators are related to future health and mental health outcomes. Lexical indicators of depression and adjustment coded from tweets aggregated at the county level could play a crucial role in prioritizing public health campaigns, particularly in socially deprived counties.

Text as signal. A tutorial with case studies focusing on social media (Twitter).

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.

The Interactive Functions of Questions in Embodied Collaborative Work.

Researchers have been interested in the investigation of the interactive functions of questions in conversational contexts. However, limited research has been conducted on the interactive functions of questions in embodied collaborative work, that is, work that involves the manipulation of physical objects. This study aimed to identify the interactive functions of questions in embodied collaborative work. To do so, we conducted a systematic qualitative analysis of a dataset of 1,751 question-answer sequences collected from an experimental study where pairs of participants (N = 67) completed a collaborative food preparation task. The qualitative analysis enabled us to identify three functions of questions: anticipation questions, exploration questions, and confirmation questions. We have discussed in this study how the types of questions identified are associated with: (i) the accomplishment of interactional goals and (ii) complementary temporalities in the collaborative activities.

Twitter, time and emotions.

The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count-LIWC; the Valence Aware Dictionary and sEntiment Reasoner-VADER and the Hu Liu sentiment lexicon-Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.

Memory bias toward emotional information in burnout and depression.

A sample of 1015 educational staff members, exhibiting various levels of burnout and depressive symptoms, underwent a memory test involving incident encoding of positive and negative words and a free recall task. Burnout and depression were each found to be associated with increased recall of negative items and decreased recall of positive items. Results remained statistically significant when controlling for history of depressive disorders. Burnout and depression were not related to mistakes in the reported words, or to the overall number of recalled words. This study suggests that burnout and depression overlap in terms of memory biases toward emotional information.

Cultural transmission in a food preparation task: The role of interactivity, innovation and storytelling.

Interactive conversation drives the transmission of cultural information in small groups and large networks. In formal (e.g. schools) and informal (e.g. home) learning settings, interactivity does not only allow individuals and groups to faithfully transmit and learn new knowledge and skills, but also to boost cumulative cultural evolution. Here we investigate how interactivity affects performance, teaching, learning, innovation and chosen diffusion mode (e.g. instructional discourse vs. storytelling) of previously acquired information in a transmission chain experiment. In our experiment, participants (n = 288) working in 48 chains with three generations of pairs had to learn and complete a collaborative food preparation task (ravioli-making), and then transmit their experience to a new generation of participants in an interactive and non-interactive condition. Food preparation is a real-world task that it is taught and learned across cultures and transmitted over generations in families and groups. Pairs were defined as teachers or learners depending on their role in the transmission chain. The number of good exemplars of ravioli each pair produced was taken as measurement of performance. Contrary to our expectations, the results did not reveal that (1) performance increased over generations or that (2) interactivity in transmission sessions promoted increased performance. However, the results showed that (3) interactivity promoted the transmission of more information from teachers to learners; (4) increased quantity of information transmission from teachers led to higher performance in learners; (5) higher performance generations introduced more innovations in transmission sessions; (6) learners applied those transmitted innovations to their performance which made them persist over generations; (7) storytelling was specialized for the transmission of non-routine, unexpected information. Our findings offer new insights on how interactivity, innovation and storytelling affect the cultural transmission of complex collaborative tasks.

Editors' Introduction: Remembering With Others: Conversational Dynamics and Mnemonic Outcomes.

Remembering the past through conversations with others is a uniquely human endeavor. Conversational remembering consists of specific dynamics and can lead to mnemonic outcomes. While conversational dynamics refer to the interactive processes (e.g., the roles speakers and listeners may undertake during the conversation) shaping collaborative remembering, conversational outcomes are about the mnemonic and functional consequences (e.g., forging social bonds) of those processes. Thus, the aim of the present article is to introduce the reader to key concepts and paradigms that have been rigorously developed to empirically investigate the dynamics and outcomes of conversational remembering in cognitive research. The collected review and empirical articles gathered in this topic provide the state-of-the-art in the field.

Storytelling as Adaptive Collective Sensemaking.

Storytelling represents a key element in the creation and propagation of culture. Three main accounts of the adaptive function of storytelling include (a) manipulating the behavior of the audience to enhance the fitness of the narrator, (b) transmitting survival-relevant information while avoiding the costs involved in the first-hand acquisition of that information, and (c) maintaining social bonds or group-level cooperation. We assess the substantial evidence collected in experimental and ethnographic studies for each account. These accounts do not always appeal to the specific features of storytelling above and beyond language use in general. We propose that the specific adaptive value of storytelling lies in making sense of non-routine, uncertain, or novel situations, thereby enabling the collaborative development of previously acquired skills and knowledge, but also promoting social cohesion by strengthening intragroup identity and clarifying intergroup relations.