3 January 2024

Mayor, E., & Bietti, L.M. (2024). Language use on Twitter reflects social structure and social disparities. Heliyon. https://doi.org/10.1016/j.heliyon.2023.e23528

Abstract. 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.

30 November 2023

Bietti, L. M. & Mayor, E. (2023). A longitudinal study of conversational remembering in WhatsApp group messages before, during, and after COVID-19 lockdown. Memory, Mind & Media, 2, E5.  https://doi.org/10.1017/mem.2023.5.

Abstract. Conversational remembering entails that people engage in recalling past experiences, which may themselves have been shared. Conversational remembering comes with social benefits for the person telling the narrative and the one receiving it (e.g., developing and strengthening friendships, fostering entertainment, and consolidating group identity). COVID-19 lockdowns have significantly affected social interaction, including face-to-face interactions where conversational remembering occurs. The aim of this study was to explore how WhatsApp group messages supported conversational remembering in a large group of friends living in Buenos Aires where a complete lockdown was established between 19 March 2020 and 6 November 2020. To accomplish such aim, we conducted a mixed-methods longitudinal study. The data consisted of 32,810 WhatsApp group messages collected over a period of 700 consecutive days, from 13 April 2019 to 13 March 2021. Our study shows that WhatsApp group messages enabled group members to keep connected during the COVID-19 lockdown period. This occurred by remembering together situations, events, and actions associated with the group's identity. The use of WhatsApp group messages may have represented an adaptive collective behaviour in response to changes in global social norms.

1 March 2023

Skjuve, M. & Bietti, L.M. (2023). Remembering with my chatbot. ACM Interactions, 30 (1). https://interactions.acm.org/blog/view/remembering-with-my-chatbot

The article begins “Did you know that humans can develop friendly or even romantic feelings toward social chatbots that can turn into close human-chatbot relationships? The phenomenon of human-chatbot relationships is starting to gain substantial media attention, and research on this topic is now emerging. Social chatbots. “What are they?” you might ask. Well, you’ve probably seen them in the App Store or on Google Play under names such as Replika or Kuki. To put it simply, a social chatbot is a form of conversational AI developed with the purpose of having normal, day-to-day conversations with its users. New developments in AI and NLP created the conditions for the design and development of sophisticated social chatbots capable of becoming your best friend, or even romantic partner. Social chatbots are generally good at showing empathy and providing emotional support and companionship. They have essentially grown into conversational affective machines, which makes it possible for users to form close relationships with them…”

10 February 2023

Baker, M. J., Pallarès, G., Cedar, T., Brandel N., Bietti, L.M., Schwarz, B. & Détienne, F. (2023). Understanding the moral of the story: Collaborative interpretation of visual narratives. Learning, Culture and Social Interaction, 39, 100700, https://doi.org/10.1016/j.lcsi.2023.100700

Abstract. Fostering moral thinking and cultural literacy are major contemporary concerns in Europe and beyond, as means for young people to co-create social futures. We present a theoretical-methodological approach to understanding students’ moral thinking in the context of collaborative interpretation of visual narratives (“wordless texts”) with ethical implications. Six layers of interpretation are defined, from referential reconstruction of characters’ intentions, through semiotic symbolism, to making the moral of the story explicit in terms of conceptualisations of three key European values (empathy, inclusion and tolerance). Within a case-study approach to analysing computer-mediated dialogues, we show the extent to which students are led to discuss and understand ethical implications of a particular narrative, and how this relates to the quality of collaboration.

25 July 2022

Mayor, E., Bietti, L.M. & Canales-Rodríguez, E.J. (2022) Text as signal. A tutorial with case studies focusing on social media (Twitter). Behavior Research Methods. https://doi.org/10.3758/s13428-022-01917-1

Abstract. 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.


4 September 2021

Bietti, L.M. & Bietti, F.U. (2021). The interactive functions of questions in embodied collaborative work. Frontiers in Psychology, 12:704275. doi: 10.3389/fpsyg.2021.704275

Abstract: 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.

27 May 2021

Mayor, E. & Bietti, L. M. (2021). Twitter, time and emotions. Royal Society Open Science. https://doi.org/10.1098/rsos.201900

Abstract: 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.

Our study has been featured in several media outlets, including the Daily Mail, NRK, Aftenposten and Gemini.no.  

31 March 2021

Bietti, L.M., Slakmon, B.Z., Baker, M.J., Détienne, F., Safin, S., & Schwarz, B.B. (2021). The DIALLS Platform: Supporting cultural literacy and understanding of European values over the Internet. In F. Maine & M. Vrikki (eds.) Dialogue for Intercultural Understanding (pp. 87-101). Cham: Springer. 

Abstract: In this chapter we present the process of designing and developing a novel online platform for supporting cultural literacy learning, involving the elaboration and understanding of European values in collaborative dialogue between students, with teacher-led reflection on wordless texts. Wordless texts are books or videos that comprise sequences of pictures which stimulate student readers to reconstruct the attendant narratives. The narratives in question, available publicly, are designed to stimulate discussions relating to European values, notably tolerance, empathy and inclusion. The main questions for platform design were therefore how to facilitate productive discussions involving European values, on or around such wordless texts, and to structure such discussions in a way that is closely anchored in the texts.

15 August 2020

Memory and imagination during the pandemic

Le Monde diplomatique has published an article I wrote in Spanish with Felipe Muller on memory and imagination during the time of COVID-19. The pandemic has made our collective memories and behaviors obsolete, so how can we imagine post COVID-19 collective life under these conditions? This is the topic of the article.


25 June 2020

Collaborative Remembering Sequences

Bietti, L.M. (2020). Collaborative remembering sequences. In B. Wagoner, I. Bresco & S. Zadeh (Eds.), Memory in the Wild (pp. 223-250). Charlotte, NC: Information Age Publishing. Preprint here

The aim of this chapter is to present a unit of analysis (collaborative remembering sequences) that enables us to capture the multiplicity of embodied, social and material resources animating collaborative remembering in the wild. Collaborative remembering sequences (CRSs) allow us to analyze those moments in which people remember with other people in everyday environments. CRSs are an ecologically valid tool to identify and analyze in a systematic fashion when and how people remember together.