Social media platforms play an increasingly important role in political campaigning, enabling parties to bypass traditional media and mobilize support directly. While prior research highlights the online prominence of far-right and radical populist actors, most studies are limited to single platforms or national contexts. This study presents the first cross- platform and cross-national analysis of digital campaign communication by 401 parties across all 27 EU member states during the 2024 Euro- pean Parliament election. Using data from Facebook, Instagram, TikTok, X/Twitter, and YouTube, we examine party activity and audience engage- ment. By linking digital trace data with expert surveys, we test whether populist radical right parties disproportionately succeed in raising engage- ment online. Our findings confirm strong platform-specific advantages of radical populist parties, particularly on TikTok, YouTube and Facebook. We also observe high engagement for far-left populist parties with similar emotional and anti-elitist communication strategies. The more Eurosceptic positions a party holds, or the more frequently experts describe them to use emotional appeals or anti-elitist communication, the more audience engagement they received across several platforms. Overall the findings emphasize a disproportionate online support for radical populist parties across the European Union.

Political debates, campaigns, and advertising increasingly take place on online platforms. However, research on election communication has been significantly hampered as formerly accessible data sources have been withdrawn or commercialised. With the implementation of the EU Digital Services Act (DSA) a number of platforms have (re-)established data access modalities for ‘public’ data via APIs or specific portals. How access to public data is implemented, what it contains, and who gets access all depends on decisions by the platforms. This leads to a series of inconsistencies, challenges, and limitations for election research. In this contribution, we discuss the implications of regulated data access under the DSA for election research. We first review central research questions and relevant data types for election research. Then, we provide a historical overview of how data access modalities have changed over the last two decades. Next, we discuss relevant articles of the DSA that aim to improve data access for academic research as well as different data access paths and modalities, including alternatives to APIs such as web scraping and data donations. Finally we summarise key challenges and formulate requirements for data access to enable robust and reproducible election research.

This paper analyses the adoption of data-driven campaigning (DDC) by German and French parties in recent national elections using data from an original post-election survey of 27 parties (12 German, 15 French) and a new purpose-built DDC campaign index. Specifically, we investigate two main research questions: (1) Do countries and parties vary in the extent to which DDC is practised? (2) If so, what explains those differences? We find that while in both countries DDC adoption is limited in comparison to other campaign modes, differences exist across countries and parties based on a range of macro (systemic) and meso (organisation-level) factors. Most notably, contrary to normalisation theory, we find that minor parties with a 'netroots' base and newcomers who are digital 'natives' engage more in DDC than the 'legacy' major parties. Diese Studie untersucht den Einsatz datengesteuerter Kampagnen (DDC) durch deutsche und franz & ouml;sische Parteien bei den vorangegangenen nationalen Wahlen. Aus Daten aus einer Nachwahlbefragung von 27 Parteien (12 deutsche, 15 franz & ouml;sische) wird ein neuer DDC-Kampagnenindex entwickelt. Die Studie widmet sich zwei Hauptforschungsfragen: (1) Gibt es L & auml;nder- und Parteienunterschiede in der Anwendung von DDC? (2) Wenn ja, welche Faktoren erkl & auml;ren diese Unterschiede? In beiden L & auml;ndern zeigt sich der Einsatz von DDC im Vergleich zu anderen Wahlkampfmitteln als begrenzt. Es zeigen sich jedoch Unterschiede zwischen L & auml;ndern und Parteien, die auf einer Reihe von Makro- (systemischen) und Mesofaktoren (auf Organisationsebene) beruhen. Entgegen der Normalisierungsthese nutzen kleinere Parteien mit einer << Netroots >> -Basis und Newcomer, die digitale << Natives >> sind, DDC intensiver als die etablierten << Altparteien >>. Cet article analyse l'adoption des campagnes guid & eacute;es par les donn & eacute;es (CGD) par les partis allemands et fran & ccedil;ais lors des derni & egrave;res & eacute;lections nationales, en utilisant des donn & eacute;es provenant d'une enqu & ecirc;te post-& eacute;lectorale originale, men & eacute;e aupr & egrave;s de 27 partis (12 allemands, 15 fran & ccedil;ais). Il s'appuie sur la cr & eacute;ation d'un indice CGD con & ccedil;u & agrave; cet effet. Nous & eacute;tudions deux questions de recherche principales: (1) Existe-t-il des variations dans l'usage des campagnes guid & eacute;es par les donn & eacute;es (CGD) selon les pays et les partis? (2) Si oui, qu'est-ce qui explique ces diff & eacute;rences? Nous constatons que si, dans les deux pays, l'adoption des CGD reste limit & eacute;e comparativement & agrave; d'autres modes de campagne, il existe des diff & eacute;rences entre les pays et les partis en fonction d'une s & eacute;rie de facteurs macro (syst & eacute;miques) et m & eacute;so (au niveau de l'organisation). En particulier, et contrairement & agrave; la th & eacute;orie de la normalisation, nous constatons que les petits partis disposant d'une base internaute, ainsi que les organisations << nativement num & eacute;riques >> s'engagent davantage dans les CGD que les grands partis << traditionnels >>. L'articolo analizza l'adozione del data-driven campaigning (DDC) da parte dei partiti tedeschi e francesi nelle recenti elezioni nazionali, utilizzando i dati di un'indagine post-elettorale originale su 27 partiti (12 tedeschi, 15 francesi) e un nuovo indice DDC appositamente costruito. Due le principali domande di ricerca: (1) esistono delle differenze fra questi due paesi Paesi e i rispettivi partiti per quanto riguarda l'uso di DDC? (2) Se s & igrave;, cosa spiega queste differenze? L'analisi dimostra che, mentre in entrambi i Paesi l'adozione di DDC & egrave; limitata rispetto ad altre modalit & agrave; di campagna, esistono differenze basate su una serie di fattori macro (sistemici) e meso (a livello di organizzazione). In particolare, e contrariamente alla teoria della normalizzazione, troviamo che i partiti minori con una base "netroots" e i "nativi" digitali usano maggiormente DDC rispetto ai partiti tradizionali.

Who governs—and who should govern—online communication? Social media companies, international organizations, users, or the state? And by what means? A range of rhetorical devices have been used to simplify the complexities associated with the governance of online platforms. This includes “constitutional metaphors”: metaphorical allusions to traditional political concepts such as statehood, democracy, and constitutionalism. Here, we empirically trace the ascent of a powerful constitutional metaphor currently employed in the news media discourse on platform governance: characterizations of Facebook’s Oversight Board (OB) as a “supreme court.” We investigate the metaphor’s descriptive suitability and question its normative and political ramifications. We argue that uncritical characterizations of the OB as Facebook’s “supreme court” obscure its true scope and purpose. In addition, we argue that appropriating the socio-cultural symbolism and hence political legitimacy of a supreme court and mapping it onto a different type of actor poses a threat to responsible platform governance.

Social media campaigning is increasingly linked with anti-democratic outcomes, with concerns to date centring on paid adverts, rather than organic content produced by a new set of online political influencers. This study systematically compares voter exposure to these new campaign actors with candidate-sponsored ads, as well as established and alternative news sources during the US 2020 presidential election. Specifically, we examine how far higher exposure to these sources is linked with key trends identified in the democratic deconsolidation thesis. We use data from a national YouGov survey designed to measure digital campaign exposure to test our hypotheses. Findings show that while higher exposure to online political influencers is linked to more extremist opinions, followers are not disengaging from conventional politics. Exposure to paid political ads, however, is confirmed as a potential source of growing distrust in political institutions.

Political campaign activities are increasingly digital. A crucial part of digital campaigning is communication efforts on social media platforms. As a forum for political discourse and political communication, parties and candidates on Twitter share public messages and aim to attract media attention and persuade voters. Party or prominent candidate hashtags are a central element of the campaign communication strategy since journalists and citizens search for these hashtags to follow the current debate concerning the hashed party or political candidate. Political elites and partisans use social media strategically, e.g., to link their messages to a broader debate, increase the visibility of messages, criticize other parties, or take over their hashtags (hashjacking). This study investigates the cases of the most recent 2017 and 2021 German federal elections called 'Bundestagswahlen'. The investigation (1) identifies communities of partisans in retweet networks in order to analyze the polarization of the most prominent hashtags of parties, 2) assesses the political behavior by partisan groups that amplify messages by political elites in these party networks, and 3) examines the polarization and strategic behavior of the identified partisan groups in the broader election hashtag debates using #BTW17 and #BTW21 as the prominent hashtags of the 2017 and 2021 elections. While in 2017, the far-right party 'Alternative für Deutschland' (AfD) and its partisans are in an isolated community, in 2021, they are part of the same community as the official party accounts of established conservative and liberal parties. This broader polarization may indicate changes in the political ideology of these actors. While the overall activity of political elites and partisans increased between 2017 and 2021, AfD politicians and partisans are more likely to use other party hashtags, which resulted in the polarization of the observed parts of the German political twitter sphere. While in 2017, the AfD polarized German Twitter, 2021 shows a broader division along the classical left–right divide.

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk -Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures—the number of ‘corona’ words in each report and the average text negativity of the sentences mentioning corona in each industry—that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.

Twitter influences political debates. Phenomena like fake news and hate speech show that political discourses on social platforms can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of ‘hashjacking’(The use of someone else’s hashtag in order to promote one’s own social media agenda.). For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative für Deutschland (AfD) and their potential use of ‘hashjacking’ in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by ‘hashjacking’ COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.

The COVID-19 pandemic caused high uncertainty regarding appropriate treatments and public policy reactions. This uncertainty provided a perfect breeding ground for spreading conspiratorial anti-science narratives based on disinformation. Disinformation on public health may alter the population's hesitance to vaccinations, counted among the ten most severe threats to global public health by the United Nations. We understand conspiracy narratives as a combination of disinformation, misinformation, and rumour that are especially effective in drawing people to believe in post-factual claims and form disinformed social movements. Conspiracy narratives provide a pseudo-epistemic background for disinformed social movements that allow for self-identification and cognitive certainty in a rapidly changing information environment. This study monitors two established conspiracy narratives and their communities on Twitter, the anti-vaccination and anti-5G communities, before and during the first UK lockdown. The study finds that, despite content moderation efforts by Twitter, conspiracy groups were able to proliferate their networks and influence broader public discourses on Twitter, such as #Lockdown in the United Kingdom.

The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of Covid-19 related risk assessments by industry. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of all US employees. We construct two measures - the number of 'corona' words in each report and the average text negativity of the sentences mentioning corona in each industry - that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment data and preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides unique granularity with regards to the particular contexts of the crisis and the concerns of individual industries about them. The data presented here help researchers and decision makers to measure, the previously unobserved, risk awareness of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macro-economic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard in real time.

While the coronavirus spreads around the world, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations have plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the expected economic shocks. To achieve higher effectiveness with cyclical and fiscal policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure the industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This data set allows for a real-time analysis of risks, revealing that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments by weeks. The risk reports differ substantially between industries, both in magnitude and in nature. Based on natural language processing techniques, we can identify-specific corona-related risk topics and their relevance for different industries. Our approach allows to cluster the industries into distinct risk groups. The findings of this study are summarised and updated in an online dashboard that tracks the industry-specific risks related to the crisis, as it spreads through the economy. The tracking tool can provide crucial information for policy-makers to effectively target financial support and to mitigate the economic shocks of the current crisis.

With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a "hashjacking" strategy. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.

Twitter influences political debates. Phenomena like fake news and hate speech show that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of 'hashjacking'. For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative für Deutschland (AfD) and their potential use of 'hashjacking' in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by 'hashjacking' COVID-19 related hashtags, like #CoronaVirusDE or #FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.

While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.

A substantial portion of contemporary public discourse and social interaction is conducted over online social media platforms, such as Facebook, YouTube, Reddit, and TikTok. Accordingly, these platforms form a core component of the digital public sphere which, although subject to private ownership, constitute a digital infrastructural resource that is open to members of the public. As private entities, platforms can set their own rules for participation, in the form of terms of service, community standards, and other guidelines. The content moderation systems deployed by such platforms to ensure that content posted on the platform complies with these terms, conditions, and standards have the potential to influence and shape public discourse by mediating what members of the public are able to see, hear, and say online. Over time, these rules may have a norm-setting effect, shaping the conduct and expectations of users about what acceptable discourse looks like. Thus, the design and implementation of content moderation systems have a powerful impact on the freedom of expression of users and their access to dialogic interaction on the platform. With great power comes great responsibility: the increasing trend towards the adoption of algorithmic content moderation systems that have a questionable track record as regards their ability to safeguard freedom of expression gives rise to urgent concerns on the need to ensure that content moderation is regulated in a manner that safeguards and fosters robust public discourse in the online sphere.

With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a "hashjacking" strategy - the use of someone else’s hashtag in order to promote one's own social media agenda. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.

Twitter is a digital forum for political discourse. The emergence of phenomena like fake news and hate speech has shown that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Recent findings suggest that some political actors might employ strategies to actively facilitate polarisation on Twitter. With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a “hashjacking” strategy (The use of someone else’s hashtag in order to promote one’s own social media agenda.). Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by “hashjacking” the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.

With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a" hashjacking" strategy-the use of someone else’s hashtag in order to promote one's own social media agenda. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by" hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (eg migration), but as a direct consequence of their digital political communication strategy.