Electoral news sharing: a study of changes in news coverage and Facebook sharing behaviour during the 2018 Mexican elections

Patterns of news consumption are changing drastically. Citizens increasingly rely on social media such as Facebook to read and share political news. With the power of these platforms to expose citizens to political information, the implications for democracy are profound, making understanding what is shared during elections a priority on the research agenda. Nevertheless, to the best of our knowledge, no study has yet explicitly explored how elections transform news sharing behaviour on Facebook. This study begins to remedy this by (a) investigating changes in news coverage and news sharing behaviour on Facebook by comparing election and routine periods, and by (b) addressing the ‘news gap’ between preferences of journalists and news consumers on social media. Employing a novel data set of news articles (N = 83,054) in Mexico, findings show that during periods of heightened political activity, both the publication and dissemination of political news increases, the gap between the news choices of journalists and consumers narrows, and that news sharing resembles a zero-sum game, with increased political news sharing leading to a decrease in the sharing of other news

Instaworthy? Examining the Effects of (Targeted) Civic Education Ads on Instagram

The last few years have witnessed a growing societal and scholarly interest in the potential of online political microtargeting to affect election outcomes in favor of parties and candidates. It has often been rightly pointed out that political microtargeting can pose risks to electoral integrity in democracies. But can political microtargeting also benefit democratic functioning? Very little is known about the potential of political microtargeting to affect citizens’ attitudes towards politics and increase their civic participation. To address this paucity, this article presents a preregistered online experiment conducted in Germany among young adults (N = 445), examining whether (targeted) civic education ads on Instagram increase political interest, efficacy, and civic participation. An innovative methodological approach to studying political microtargeting is deployed, exposing respondents to civic education ads in a mock Instagram feed, personalized in real-time based on individual preferences. We find no direct evidence of (targeted) civic education ads, leading us to believe that (targeted) ads do not unconditionally affect political interest, efficacy, or civic participation

Heineken in the House: Improving Online Media Reputation through Featuring a Sponsored Brand Community

Nowadays, more and more organizations use social media to promote their sponsorships of big events. Heineken has created a major brand community by facilitating the Holland Heineken House during the Olympic Games. This study investigates to what extent featuring a sponsored brand community on social media affected Heineken’s online media reputation. A quantitative content analysis on Heineken’s Facebook, Twitter and Instagram posts before, during and after the Olympic Games in 2016 was performed to analyse the relation between the promotion of the Holland Heineken House and this company’s online media reputation. The use of humour, hashtags, key events and videos have been taken into account. The results show that the promotion of the Holland Heineken House positively influences Heineken’s online media reputation merely on Twitter. This study thus provides valuable insights into how an organization’s online media reputation can score with sponsoring a brand community

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Still going strong? The role of traditional media in the 2021 Dutch parliamentary elections

Previous research has demonstrated that both visibility of parties, party leaders, candidates, and topics, and the sentiment of this coverage can affect people’s decision in the ballot box. Most of this research was, however, done in the period before social media gained importance which has drastically changed the media consumption of citizens. The main aim of this paper is to investigate whether, and if so to what extent, traditional media use during the 2021 Dutch parliamentary elections has (still) affected vote choice in this era of social media. To study this, two-wave panel survey data from the Dutch Parliamentary Election Survey (DPES) are combined with an automated content analysis of newspaper articles (N = 35,511). We created respondent-specific content variables to conduct a linkage analysis. Our analysis, relying on a pooled analysis of respondent–party combinations (N = 54,162), demonstrates that political parties profit electorally from being visible in both newspapers and online outlets. This is in particular true for parties that are not part of parliament yet, thus increasing the further fragmentation and division in Dutch politics. Contrary to the expectations, sentiment in online media has a negative effect, with negative coverage increasing electoral support

Beyond Discrete Genres: Mapping News Items onto a Multidimensional Framework of Genre Cues

In the contemporary media landscape, with the vast and diverse supply of news, it is increasingly challenging to study such an enormous amount of items without a standardized framework. Although attempts have been made to organize and compare news items on the basis of news values, news genres receive little attention, especially the genres in a news consumer’s perception. Yet, perceived news genres serve as an essential component in exploring how news has developed, as well as a precondition for understanding media effects. We approach this concept by conceptualizing and operationalizing a non-discrete framework for mapping news items in terms of genre cues. As a starting point, we propose a preliminary set of dimensions consisting of “factuality” and “formality”. To automatically analyze a large amount of news items, we deliver two computational models for predicting news sentences in terms of the said two dimensions. Such predictions could then be used for locating news items within our framework. This proposed approach that positions news items upon a multidimensional grid helps deepening our insight into the evolving nature of news genres

Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks

WhatsApp with Politics?! : Examining the Effects of Interpersonal Political Discussion in Instant Messaging Apps

With an increasing number of people, especially adolescents, using more private online platforms, such as WhatsApp, for news, an important question for democracy is whether such platforms can facilitate learning about politics and current events. In this study, we examine adolescents’ affective (emotions, feelings), behavioral (actions and behavioral intentions), and cognitive (political knowledge) responses to interpersonal political discussion on WhatsApp. We conducted a preregistered field experiment at six secondary schools in the Netherlands (N = 230). We assigned respondents with strong ties to a WhatsApp group. For seven days, respondents received a link to an online political news item on a daily basis; and (1) either had to read or (2) read and discuss it. The results indicate that interpersonal discussion evokes stronger positive emotions and feelings, as well as issue-specific knowledge. In addition, elaboration on the content of political discussion was positively related to issue-specific knowledge. In this way, instant messaging apps may serve as a resource for engaging adolescents with politics and current events