The rise of populism in Europe, exemplified by the success of parties like the Rassemblement National (RN) in France, is a significant concern in the post-truth era. This research focuses on the RN's strategic use of social media, specifically Twitter, to reshape its image and expand its voter base. The RN's remarkable growth, culminating in electoral victories exceeding expectations, is attributed to a sophisticated communication plan. Existing research has examined new media and social networks' influence on political communication, but this paper specifically addresses the RN's strategies within the context of the French extreme right. The study investigates how the RN leverages Twitter to project a changed image, aiming to appear as a viable option for a broader electorate while maintaining its populist identity. This research is crucial for understanding the evolving relationship between populism, cyberdemocracy, and disinformation in the digital age, and the tactics employed by populist movements to manipulate public opinion through social media.

The paper reviews existing literature on populism, defining its key characteristics such as reliance on charismatic leaders, simplistic solutions to complex problems, and apocalyptic rhetoric. It discusses the weakening of traditional parties and ideological blurring as factors contributing to populism's rise. The literature also addresses the malleability of populist discourse and its adaptability to both left and right-wing ideologies. The study highlights the role of mass media and social networks in amplifying populist messages, enabling direct communication with citizens without mediation. Existing research on the use of Twitter by political organizations and populist parties is reviewed, examining its use for strategic communication, information dissemination, audience participation, and image promotion. However, there is limited research on the pan-European implications of these strategies. The paper emphasizes the importance of understanding the functions of Twitter within populist discourse, including public relations, message distribution, audience engagement, and leadership personalization.

This study employs a hybrid quantitative methodology combining computational and manual procedures. The data consists of 1256 tweets from the official RN Twitter account (@RNational_off) during the 2014 and 2019 European election campaigns. Content analysis was performed using a structured coding sheet, encompassing variables such as tweet subject, authorship, purpose, content type (text, image, video), and engagement metrics (retweets, likes, comments). The data was analyzed using IBM SPSS software to identify patterns and correlations. The coding sheet categorized tweets by subject matter (immigration, foreign affairs, economy, social policies, justice, electoral campaign, others), authorship (RN profile, Marine Le Pen, Jordan Bardella, other RN members, media, rivals, others), purpose (propaganda, information, media promotion, confrontation, complaint, vote appeal, emotional message), and content type. Engagement metrics were categorized into intervals based on previous research. The methodology enables a comparative analysis of the RN's Twitter strategy across the two election campaigns.

The study found a significant increase in the RN's Twitter activity from 2014 to 2019, with a 400% increase in tweets. The use of multimedia content, especially images and videos, also increased substantially. The party shifted from a unidirectional communication style in 2014 to a more interactive approach in 2019, although this interaction was largely limited to provocative engagement rather than genuine dialogue. A key finding is the diversification of tweet authorship in 2019, with tweets originating from various RN members in addition to the official account. This strategic change was linked to a significant increase in engagement, demonstrating the effectiveness of a multi-voice approach in enhancing reach and impact. The analysis revealed a correlation between the diversification of authorship and increased retweets and likes, indicating the influence of specific figures like Marine Le Pen and Jordan Bardella on engagement rates. Despite the image-whitening strategy, the study found that the RN retained its populist characteristics, particularly in its creation of an 'enemy' and its use of polarized language. The party focused its negative rhetoric on opponents, mainly the media and political rivals, while maintaining a positive tone when promoting itself and its actions.

The findings suggest that the RN's social media strategy successfully broadened its reach and engagement while retaining its populist identity. The increase in tweet volume, multimedia content, and diverse authorship contributed to greater visibility and impact. The study supports the hypothesis that the RN strategically evolved its online communication to attract a wider voter base, employing techniques common to populist movements. However, the findings also highlight the limitations of 'image whitening,' as the party's core populist characteristics remained, suggesting that such strategies may not fundamentally alter the nature of extremist groups. The study underscores the importance of studying social media strategies employed by populist and extremist parties to better understand their influence on democratic processes. Further research should explore the long-term impacts of such strategies and the role of algorithms in shaping online political discourse.

This study demonstrates the RN's strategic adaptation of Twitter for electoral gains. By increasing volume, using multimedia, diversifying authorship, and engaging in calculated interactions, the party effectively broadened its reach and heightened engagement. While the RN attempted image-whitening, it retained core populist traits. Future research should examine the effectiveness of such image-shifting strategies in the longer term and explore the broader impact of social media on populist mobilization and electoral success. It is also important to explore the role of algorithmic bias and the limitations of content analysis in capturing the full nuances of online political communication.

The study's focus on Twitter data limits the understanding of the RN's overall communication strategy. Other social media platforms and traditional media outlets should be considered for a more comprehensive analysis. The quantitative methodology, while providing valuable insights, may not fully capture the qualitative aspects of political rhetoric and persuasive techniques. The study's reliance on self-reported data from the RN's official Twitter account may introduce bias. Furthermore, future studies should consider investigating the audience's responses and perceptions to gain a deeper understanding of the effectiveness of these communication strategies.