This study investigates the concept of frames in the realm of online polarization, with a focus on social media platforms. The research extends the understanding of how frames–emerging, complex, and often subtle concepts–become prominent in online conversations that are polarized. The study proposes a comprehensive methodology for identifying and characterizing these frames, integrating machine learning techniques, network analysis algorithms, and natural language processing tools. This method aims for generalizability across multiple platforms and types of user engagement. Two novel metrics, homogeneity and relevancy are introduced for the rigorous evaluation of identified frame candidates. Grounded in several foundational presumptions, including the role of topics and multi-word expressions in framing, the study sheds light on how frames emerge and gain significance within digital communities. The research questions explored include the methods for identifying frames, the variability and significance of these frames, and the effectiveness of different computational techniques in this context. To validate the approach, we present a case study of the 2021 Chilean presidential election, using data from both \(\mathbb {X}\) (formerly known as Twitter) and WhatsApp platforms. This real-world application allows for the examination of how frames fluctuate in response to events and the specific mechanisms of platforms. Overall, the study makes several key contributions to the field, offering new insights and methodologies for analyzing the complexities of online polarization. It serves as groundwork for future research on the dynamics of online communities, especially those associated with distinctly polarized events.

Social media data has emerged as a useful source of timely information about real-world crisis events. One of the main tasks related to the use of social media for disaster management is the automatic identification of crisis-related messages. Most of the studies on this topic have focused on the analysis of data for a particular type of event in a specific language. This limits the possibility of generalizing existing approaches because models cannot be directly applied to new types of events or other languages. In this work, we study the task of automatically classifying messages that are related to crisis events by leveraging cross-language and cross-domain labeled data. Our goal is to make use of labeled data from high-resource languages to classify messages from other (low-resource) languages and/or of new (previously unseen) types of crisis situations. For our study we consolidated from the literature a large unified dataset containing multiple crisis events and languages. Our empirical findings show that it is indeed possible to leverage data from crisis events in English to classify the same type of event in other languages, such as Spanish and Italian (80.0% F1-score). Furthermore, we achieve good performance for the cross-domain task (80.0% F1-score) in a cross-lingual setting. Overall, our work contributes to improving the data scarcity problem that is so important for multilingual crisis classification. In particular, mitigating cold-start situations in emergency events, when time is of essence.

Most existing approaches that use social media for detecting and characterizing emerging crisis events are based on the analysis of messages obtained from social platforms using a predetermined set of keywords [2, 3]. In addition to keyword filters, messages must commonly be post-processed using supervised classification models to determine if messages are referring to a real-time crisis situation or not. However, keyword-based approaches have certain shortcomings; on the one hand they require specific domain knowledge of different crisis events to determine a set of keywords to filter relevant data about an emerging crisis situation; on the other hand, they require supervised methods to determine if the identified data actually corresponds to a new real-time crisis event. Hence, the creation of keyword-independent methods could also help generalize existing approaches so they can be used for cross-lingual events, since each language and culture can have its own particular terms to refer to a same event. The majority of these works also explain phenomenons just for English messages. This limitation avoids replication of methodologies in other languages and countries where emergency events often occur. For this reason, researchers recently have focused on creating domain-independent and multi-lingual approaches for detecting and classifying social media messages during crisis events [1, 4]. These approaches have exploited low-level lexical features with the goal of reaching domain-transfer among different crisis events and languages. Nonetheless, most studies focused on crisis-related messages without testing non related crisis messages such as sporting events or music festivals.