Nation Binding: How Public Service Broadcasting Mitigates Political Selective Exposure.
ABSTRACT: Recent research suggests that more and more citizens select news and information that is congruent with their existing political preferences. This increase in political selective exposure (PSE) has allegedly led to an increase in polarization. The vast majority of studies stem from the US case with a particular media and political system. We contend that there are good reasons to believe PSE is less prevalent in other systems. We test this using latent profile analysis with national survey data from the Netherlands (n = 2,833). We identify four types of media use profiles and indeed only find partial evidence of PSE. In particular, we find that public broadcasting news cross-cuts all cleavages. This research note offers an important antidote in what is considered a universal phenomenon. We do find, however, a relatively large segment of citizens opting out of news consumption despite the readily available news in today's media landscape.
Project description:In the age of data processing, news videos are rich mines of information. After all, the news are essentially created to convey information to the public. But can we go beyond what is directly presented to us and see a wider picture? Many works already focus on what we can discover and understand from the analysis of years of news broadcasting. These analysis bring monitoring and understanding of the activity of public figures, political strategies, explanation and even prediction of critical media events. Such tools can help public figures in managing their public image, as well as support the work of journalists, social scientists and other media experts. News analysis can also be seen from the lens of complex systems, gathering many types of entities, attributes and interactions over time. As many public figures intervene in different news stories, a first interesting task is to observe the social interactions between these actors. Towards this goal, we propose to use video analysis to automatise the process of constructing social networks directly from news video archives. In this paper we are introducing a system deriving multiple social networks from face detections in news videos. We present preliminary results obtained from analysis of these networks, by monitoring the activity of more than a hundred public figures. We finally use these networks as a support for political studies and we provide an overview of the political landscape presented by the Japanese public broadcaster NHK over a decade of the 7 PM news archives.
Project description:As the digitization is integrated into daily life, media including video and audio are heavily transferred over the Internet nowadays. Voice-over-Internet Protocol (VoIP), the most popular and mature technology, becomes the focus attracting many researches and investments. However, most of the existing studies focused on a one-to-one communication model in a homogeneous network, instead of one-to-many broadcasting model among diverse embedded devices in a heterogeneous network. In this paper, we present the implementation of a VoIP broadcasting service on the open source-Linphone-in a heterogeneous network environment, including WiFi, 3G, and LAN networks. The proposed system featuring VoIP broadcasting over heterogeneous networks can be integrated with heterogeneous agile devices, such as embedded devices or mobile phones. VoIP broadcasting over heterogeneous networks can be integrated into modern smartphones or other embedded devices; thus when users run in a traditional AM/FM signal unreachable area, they still can receive the broadcast voice through the IP network. Also, comprehensive evaluations are conducted to verify the effectiveness of the proposed implementation.
Project description:This paper evaluates the influence of online news consumption on attitudes toward the European Union in a context of protracted economic crisis. Using data from the 2011 Irish National Election Study, we combine location-specific information on broadband availability with respondent geo-location data, which facilitates causal inference about the effects of online news consumption via instrumental variable models. Results show that Irish citizens who source political information online are more prone to blame the EU for the poor state of the economy than those who do not. There is evidence of preference reinforcement among those with negative predispositions toward the EU, but not among pro-EU citizens. We complement this analysis with a study of voting behavior in the European Fiscal Compact Referendum, employing a similar methodological approach. The results from this second survey confirm the anti-EU influence of online news consumption among Irish citizens, although evidence suggests a pro-EU effect among voters who browsed the website of the politically neutral Irish Referendum Commission. Our paper contributes to the literature on public opinion, the EU, and political attitudes in times of crisis.
Project description:BACKGROUND:Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data. METHOD:The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. RESULTS:We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. CONCLUSIONS:Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the "Politoscope", a macroscope that delivers some of our results in an interactive way.
Project description:Online social media has recently irrupted as the last major venue for the propagation of news and cultural content, competing with traditional mass media and allowing citizens to access new sources of information. In this paper, we study collectively filtered news and popular content in Twitter, known as Trending Topics (TTs), to quantify the extent to which they show similar biases known for mass media. We use two datasets collected in 2013 and 2014, including more than 300.000 TTs from 62 countries. The existing patterns of leader-follower relationships among countries reveal systemic biases known for mass media: Countries concentrate their attention to small groups of other countries, generating a pattern of centralization in which TTs follow the gradient of wealth across countries. At the same time, we find subjective biases within language communities linked to the cultural similarity of countries, in which countries with closer cultures and shared languages tend to follow each other's TTs. Moreover, using a novel methodology based on the Google News service, we study the influence of mass media in TTs for four countries. We find that roughly half of the TTs in Twitter overlap with news reported by mass media, and that the rest of TTs are more likely to spread internationally within Twitter. Our results confirm that online social media have the power to independently spread content beyond mass media, but at the same time social media content follows economic incentives and is subject to cultural factors and language barriers.
Project description:News coverage of Islamic extremism is reigniting debates about the media's role in promoting prejudice toward Muslims. Psychological theories of media-induced prejudice date to the 1950's, and find support from controlled experiments. However, national-scale studies of media effects on Muslim prejudice are lacking. Orthogonal research investigating media-induced prejudice toward immigrants has failed to establish any link. Moreover, it has been found that people interpret the news in ways that confirm pre-existing attitudes, suggesting that media induced Muslim prejudice in liberal democracies is unlikely. Here, we test the association between news exposure and anti-Muslim prejudice in a diverse national sample from one of the world's most tolerant societies, where media effects are least likely to hold (N = 16,584, New Zealand). In support of media-induced Islamophobia, results show that greater news exposure is associated with both increased anger and reduced warmth toward Muslims. Additionally, the relationship between media exposure and anti-Muslim prejudice does not reliably vary with political ideology, supporting claims that it is widespread representations of Muslims in the news, rather than partisan media biases, that drives anti-Muslim prejudice.
Project description:This paper examines the link between reliance on Facebook for news, political knowledge, and political engagement in the Philippines. We tested five hypotheses using data gathered from an online survey of 978 Filipinos conducted from February 1 to March 31, 2016. Findings support the hypothesis that those who rely less on social media as a news source exhibit higher levels of perceived knowledge about politics than those who rely more on it for news. Controlling for traditional news use, following political officials or institutions on social media is associated with higher levels of political interest and engagement, those with more politically active friends on Facebook have higher levels of exposure to political content online, and there is a positive correlation between Facebook being a source of information about politics and discussing politics more often with others. However, the hypothesis that those with more friends on their network who are politically active, will have greater political knowledge and more political engagement than those who have few politically active friends on their Facebook network is not supported.
Project description:This paper addresses the subject of letters to the editor as one of the longest standing forums for public discussion and debate by ordinary citizens. To show how the voice of ordinary citizens is presented in letters to the editor during national election campaigns over a period of ten years (2008, 2013 & 2017), we are focusing on the Austrian Kronen Zeitung: A newspaper with an exceptionally high market share of up to 40% during the examination period, a heavy focus on the letters section with three pages per day, and a self-declared willingness to take a stance, especially during election periods. Based on a quantitative content analysis of 530 letters to the editor and 525 articles in the politics section as well as survey data from the Austrian national election study on the political positions of the Kronen Zeitung's readers, we find that letters to the editor in the Kronen Zeitung do not reflect, but complement the articles in the politics section. The tone of the letters is more negative than that of news articles, but the letters closely reflect the readers' political positions, therefore offering identification with the paper.
Project description:In this paper, we introduce a scalable machine learning approach accompanied by open-source software for identifying violent and peaceful forms of political protest participation using social media data. While violent political protests are statistically rare events, they often shape public perceptions of political and social movements. This is, in part, due to the extensive and disproportionate media coverage which violent protest participation receives relative to peaceful protest participation. In the past, when a small number of media conglomerates served as the primary information source for learning about political and social movements, viewership and advertiser demands encouraged news organizations to focus on violent forms of political protest participation. Consequently, much of our knowledge about political protest participation is derived from data collected about violent protests, while less is known about peaceful forms of protest. Since the early 2000s, the digital revolution shifted attention away from traditional news sources toward social media as a primary source of information about current events. This, along with developments in machine learning which allow us to collect and analyze data relevant to political participation, present us with unique opportunities to expand our knowledge of peaceful and violent forms of political protest participation through social media data.
Project description:Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platform algorithms preferentially display content from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content ("fake news"). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans-mostly due to distrust of mainstream sources by Republicans-every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.