political polarization

Background: The 2024 Iranian presidential election intensified political polarization on Persian Twitter (X), where ideological factions engaged in networked contestation. This study employs social network analysis to examine polarization mechanics, mapping key actors, echo chambers, and discursive strategies. Findings illuminate digital factionalism, algorithmic amplification, and the role of influencers in political mobilization.Aims: This study explores the mechanisms driving political polarization on Persian Twitter (X) during the 2024 Iranian presidential election.Methodology: Utilizing computational social science methodologies, the research combines social network analysis (SNA) and thematic analysis to examine over 133,000 active users and numerous election-related hashtags. Data collection spanned June 1–16, 2024, leveraging Twitter's API to identify clusters, user interactions, and thematic trends. Key tools included the Louvain algorithm for community detection and centrality measures for network analysis, visualized through Gephi software.Findings: Findings reveal a fragmented political landscape characterized by ideological divides, echo chambers, and limited interaction between opposing factions. Analysis identified six major clusters, each aligned with distinct political affiliations, including reformists, conservatives, and opposition groups. Thematic analysis further highlighted the rhetoric surrounding leading candidates Masoud Pezeshkian and Saeed Jalili, revealing polarized sentiments and distinct narratives among their supporters and detractors. The study also underscores the role of platform algorithms, influencer strategies, and group identities in deepening polarization.Conclusions: This research contributes to the understanding of digital polarization within Iran's socio-political context. It emphasizes the dual role of social media as both a space for public discourse and a driver of ideological segregation. Recommendations include strategies for promoting digital literacy, fostering inclusive discussions, and enhancing algorithmic transparency to mitigate polarization in Iran.

This study investigates the negotiation strategies used by Democratic and Republican candidates in the 2024 U.S. presidential debates, focusing on how language functions as a tool to navigate ideological divides in an increasingly polarized political climate. By applying a mixed-method approach, combining qualitative discourse analysis and survey data, the research examines debate transcripts alongside citizen feedback to assess the effectiveness of candidates’ rhetorical strategies. Drawing on Politeness Theory, Speech Act Theory, and Pragma-Crafting Theory, the study reveals distinct linguistic patterns: Democratic candidates employ inclusive, cooperative language to foster unity, while Republican candidates use direct, assertive language to project confidence and decisiveness. The findings highlight the role of language in shaping voter perceptions of leadership and ideological alignment, offering insights into how candidates' rhetoric resonates with their respective bases and impacts broader electoral dynamics. This research contributes to the fields of political communication and negotiation theory by offering a comparative analysis of debate strategies and providing practical implications for political strategists and campaign teams navigating the complexities of modern electoral discourse. This research contributes to the fields of political communication and negotiation theory by offering a comparative analysis of debate strategies and providing practical implications for political strategists and campaign teams navigating the complexities of modern electoral discourse.