Suchergebnisse

El éxito de internet como nueva plataforma comunicativa ha generado una profunda transformación política y social. El objetivo de este artículo es ofrecer un análisis de estas consecuencias, utilizando como hilo conductor la principal transformación que conlleva la revolución digital: una reducción radical en los costes de entrada a la comunicación de masas. En contraste con el optimismo inicial sobre la democratización de la esfera política en la era de internet, y la actual preocupación sobre los efectos negativos de las redes sociales, mi argumento es que estas consecuencias no son universalmente positivas o negativas. En mi análisis, me centro en tres aspectos centrales de la revolución digital: la posible polarización resultante del consumo social de información política a través de internet, el uso de herramientas tecnológicas para la coordinación de movimientos sociales y los efectos del uso de redes sociales en la calidad de la democracia representativa y las políticas públicas.

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are embedded can be a source of information about their ideological positions. Under the assumption that social networks are homophilic, I develop a Bayesian Spatial Following model that considers ideology as a latent variable, whose value can be inferred by examining which politics actors each user is following. This method allows us to estimate ideology for more actors than any existing alternative, at any point in time and across many polities. I apply this method to estimate ideal points for a large sample of both elite and mass public Twitter users in the United States and five European countries. The estimated positions of legislators and political parties replicate conventional measures of ideology. The method is also able to successfully classify individuals who state their political preferences publicly and a sample of users matched with their party registration records. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior during the 2012 US presidential election campaign is clustered along ideological lines.

The growth of social media—and, in particular, Twitter and Facebook—has led scholars to study its effects on mass behavior and protest. But leaders are also active on social media. They use their accounts to communicate with domestic and international audiences. By the end of 2014, more than 76 percent of world leaders had an active presence on social media. What explains variation in their adoption and use of social media? We look at several different potential hypotheses: higher income per capita and internet penetration (modernization), social pressure, level of democratization, and geographic spread of adoption (diffusion). We find strong support that (1) increased political pressure from social unrest and (2) higher levels of democratization correlate with leader adoption of social media platforms. Although our findings are correlational, they reveal that institutional and political pressures are related to social media adoption and the political communication of world leaders.

Corruption scandals have been found to have significant but mild electoral effects in the comparative literature (Golden 2006). However, most studies have assumed that voters punish all kinds of illegal practices. This article challenges this assumption by distinguishing between two types of corruption, according to the type of welfare consequences they have for the constituency. This hypothesis is tested using data from the 2011 Spanish local elections. We exploit the abundance of corruption allegations associated with the Spanish housing boom, which generated income gains for a wide segment of the electorate in the short term. We find that voters ignore corruption when there are side benefits to it, and that punishment is only administered in those cases in which they do not receive compensation.

AbstractVoting Advice Applications (VAAs), which provide citizens with information on the party that best represents their political preferences, are often cited as evidence of the empowering capabilities of digital tools. Aside from the informational benefits of these voter guides, observational studies have suggested a strong effect on political participation and vote choice. However, existing impact evaluations have been limited by a reliance on convenience samples, lack of random assignment, or both. This raises questions about self-selection and the precise mechanisms underlying how voters learn about politics. Here, we provide evidence from a field experiment with survey outcomes conducted with a sample of over 1,000 German citizens in the 2017 federal election campaign. Using linked panel survey and digital trace data combined with a randomized encouragement to complete a VAA, we assess respondents' compliance with treatment and observe how the use of this tool affects political behavior, attitudes, media consumption, political knowledge, and even social media activity. Our findings reveal that the overwhelming consensus in favor of positive effects on turnout and vote choice should be treated with caution, as we find no such effects. Rather, the actual virtue of VAAs in a complex online information environment lies in increasing knowledge about parties' positions on issues—exactly the kind of information these tools were designed to provide.

How do leaders communicate during domestic crises? We provide the first global analysis of world leader communication on social media during social unrest. We develop a theory of leaders' digital communication strategies, building on the diversionary theory of foreign policy, as well as research on the role of democratic institutions in explaining elite responsiveness. To test our theory, we construct a new dataset that characterizes leader communication through social media posts published by any head of state or government on Twitter or Facebook, employing a combination of automated translation and supervised machine learning methods. Our findings show that leaders increase their social media activity and shift the topic from domestic to foreign policy issues during moments of social unrest, which is consistent with a conscious strategy to divert public attention when their position could be at risk. These effects are larger in democracies and in particular in the run-up to elections, which we attribute to incentives created by democratic institutions. Our results demonstrate how social media provide meaningful comparative insight into leaders' political behavior in the digital age.

AbstractWhy do some issues receive more interest from the public, while others do not? This paper develops a theoretical and empirical approach that explains the degree to which issues expand from the elite to the public. We examine how candidates in the 2014 European Parliament elections talked about EU issues, in comparison to other political issues. We rely on data collected from Twitter and use a combination of human coding and machine learning to analyse what facilitates interactions from the public. We find that most political actors did not try to engage with the public about EU issues, and lack of engagement results in less interactions from the general public. Our findings contribute to understanding why EU issues still play a secondary role in European politics, but at the same time highlight what low‐cost communicational tools might be useful to overcome this expansion deficit.

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic and tone. Here, we provide a guide to help researchers navigate the consequential decisions they need to make before any measure can be produced from the text. We consider, both theoretically and empirically, the effects of such choices using as a running example efforts to measure the tone ofNew York Timescoverage of the economy. We show that two reasonable approaches to corpus selection yield radically different corpora and we advocate for the use of keyword searches rather than predefined subject categories provided by news archives. We demonstrate the benefits of coding using article segments instead of sentences as units of analysis. We show that, given a fixed number of codings, it is better to increase the number of unique documents coded rather than the number of coders for each document. Finally, we find that supervised machine learning algorithms outperform dictionaries on a number of criteria. Overall, we intend this guide to serve as a reminder to analysts that thoughtfulness and human validation are key to text-as-data methods, particularly in an age when it is all too easy to computationally classify texts without attending to the methodological choices therein.

AbstractIn this note, we provide direct evidence of cheating in online assessments of political knowledge. We combine survey responses with web tracking data of a German and a US online panel to assess whether people turn to external sources for answers. We observe item-level prevalence rates of cheating that range from 0 to 12 percent depending on question type and difficulty, and find that 23 percent of respondents engage in cheating at least once across waves. In the US panel, which employed a commitment pledge, we observe cheating behavior among less than 1 percent of respondents. We find robust respondent- and item-level characteristics associated with cheating. However, item-level instances of cheating are rare events; as such, they are difficult to predict and correct for without tracking data. Even so, our analyses comparing naive and cheating-corrected measures of political knowledge provide evidence that cheating does not substantially distort inferences.