Conflicting narratives and polarization on social media
Narratives are key interpretative devices by which humans make sense of political reality. In this work, we show how the analysis of conflicting narratives, i.e. conflicting interpretive lenses through which political reality is experienced and told, provides insight into the discursive mechanisms of polarization and issue alignment in the public sphere. Building upon previous work that has identified ideologically polarized issues in the German Twittersphere between 2021 and 2023, we analyze the discursive dimension of polarization by extracting textual signals of conflicting narratives from tweets of opposing opinion groups. Focusing on a selection of salient issues and events (the war in Ukraine, Covid, climate change), we show evidence for conflicting narratives along two dimensions: (i) different attributions of actantial roles to the same set of actants (e.g. diverging interpretations of the role of NATO in the war in Ukraine), and (ii) emplotment of different actants for the same event (e.g. Bill Gates in the right-leaning Covid narrative). Furthermore, we provide first evidence for patterns of narrative alignment, a discursive strategy that political actors employ to align opinions across issues. These findings demonstrate the use of narratives as an analytical lens into the discursive mechanisms of polarization.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter
The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the …
Personality Traits and Echo Chambers on Facebook
In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions hav…
MisinformationMeasuring Social Media Polarization Using Large Language Models and Heuristic Rules
Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and d…
Sentiment AnalysisA More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs
Group polarization is an important research direction in social media content analysis, attracting many researchers to explore this field. Therefore, how to effectively measure group polarization has become a critical to…
Stance DetectionZero-Shot Stance DetectionRELATIO: Text Semantics Capture Political and Economic Narratives
Social scientists have become increasingly interested in how narratives -- the stories in fiction, politics, and life -- shape beliefs, behavior, and government policies. This paper provides an unsupervised method to qua…