Over the past few years, the COVID-19 pandemic has exerted various impacts on the world, notably concerning mental health. Nevertheless, the precise influence of psychosocial stressors on this mental health crisis remains largely unexplored. In this study, we employ natural language processing to examine chat text from a mental health helpline. The data was obtained from a chat helpline called Safe Hour from the “It Gets Better” project in Chile. This dataset encompass 10,986 conversations between trained professional volunteers from the foundation and platform users from 2018 to 2020. Our analysis shows a significant increase in conversations covering issues of self-image and interpersonal relations, as well as a decrease in performance themes. Also, we observe that conversations involving themes like self-image and emotional crisis played a role in explaining both suicidal behavior and depressive symptoms. However, anxious symptoms can only be explained by emotional crisis themes. These findings shed light on the intricate connections between psychosocial stressors and various mental health aspects in the context of the COVID-19 pandemic.

We propose a method to characterize political ideology using network theory. Our analysis is based on the 2015–2016 Chilean constituent process, where self-convened meetings were held throughout the country to discuss which Values, Rights, Duties, and Institutions should be included in the new constitution. Using this unique dataset, co-occurrence networks were constructed by considering the concepts selected in different meetings. The nodes are the concepts, and a link between two nodes represents the association between them. Political ideology is thus analyzed as an emergent network, and we can identify the main ideological communities in Chile and describe their characteristics. Beyond the local results, the proposed methodology enables representing the diversity of a community’s political orientations in a realistic ecological context.

Despite all the elasticity and even ambiguity surrounding the concept of populism, the existing paradigms converge in the recognition of a populist rhetoric. By using Natural Language Processing (NLP) tools we propose a set of linguistic and discursive markers to identify populist markers in Presidential speeches. The performance of these markers is subsequently tested against the Global Populism Database (GPD). We set-up a multinomial regression model to study the predictive power of these markers on the GPD populist score, focusing on left-wing populist leaders in Spanish-speaking countries in Latin America. We are thus able to characterize (left-wing) populism as a style of communication, as well as to understand what is behind this rhetoric. Our results show that ingroup and emotional content are more present in populist speeches. We also find a positive relation between populism and the use future tense and conditional connectors, which suggest an intention to manipulate the audience. These results have implications both for the current understanding of (left-wing) populist rhetoric and for the conceptualization of populism itself.

Traditional methods of citizen deliberation, such as mini-publics, have been criticized for their limited scope. However, the implementation of massive deliberative processes creates new socio-technical challenges, particularly at the systematization stage. In general, the task of processing citizen participation is typically met with the general tools of textual analysis and Natural Language Processing (NLP). However, processing participation is not just a technical problem, but fundamentally a problem of normative design whose solution must be coherent with the philosophical, ethical, and institutional decisions that the participatory process embodies. In this article, we show how, using basic syntactic rules, we can produce simple and highly explainable reconstructions of public opinions. This methodology was tested on datasets from Chilean participatory processes, specifically dialogs from We have to talk about Chile and the 2023 Constituent Process. We show how our strategy enables further information extraction by identifying different syntactic components, providing deeper insights into the answers, while aligning with the goals of citizen participation. Overall, this study presents a compelling alternative for interpreting large volumes of citizen input, particularly in ways that preserve context and advance a more coherent analytical framework aligned with the normative ideals of deliberative democracy.

Internal factors–such as psychological traits or individual attitudes–relate to and explain political cleavages. Yet, little is known about how locus of control, agency, and modal attitudes impact political ideology. Utilizing textual analysis within the Chilean 2015 constituent process context, we go beyond traditional survey methods to explore community clusters in ``Values" and ``Rights" networks built upon 106,000 people's deliberation. Our findings reveal distinct attitudinal patterns across political orientations: the progressive left generally exhibits a more propositive and non-agentic attitude, the traditional left adopts an evaluative stance towards values, and the right-wing community leans towards a factual attitude but shifts to an evaluative stance when discussing rights. These results underscore the role of psychological constructs in shaping political ideologies and introduce textual analysis as a robust tool for psychological and political inquiry. The study offers a comprehensive understanding of the complexities of political behavior and provides a new lens through which to examine the psychology of political ideology.