We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014–2019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on the other, their retweeting behavior. We make use of two diverse datasets in the analysis. The first one is the roll-call vote dataset, where cohesion is regarded as the tendency to co-vote within a group, and a coalition is formed when the members of several groups exhibit a high degree of co-voting agreement on a subject. The second dataset comes from Twitter; it captures the retweeting (i.e., endorsing) behavior of the MEPs and implies cohesion (retweets within the same group) and coalitions (retweets between groups) from a completely different perspective. We employ two different methodologies to analyze the cohesion and coalitions. The first one is based on Krippendorff’s Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. We give general insights into the cohesion of political groups in the European Parliament, explore whether coalitions are formed in the same way for different policy areas, and examine to what degree the retweeting behavior of MEPs corresponds to their co-voting patterns. A novel and interesting aspect of our work is the relationship between the co-voting and retweeting patterns.
The second important aspect of our study is related to analyzing the behavior of participants in social networks, specifically Twitter. Twitter is studied by researchers to better understand different political processes, and in some cases to predict their outcomes. @cite_28 consider the number of tweets by a party as a proxy for the collective attention to the party, explore the dynamics of the volume, and show that this quantity contains information about an election's outcome. Other studies @cite_18 reach similar conclusions. @cite_35 predicted the political alignment of Twitter users in the run-up to the 2010 US elections based on content and network structure. They analyzed the polarization of the retweet and mention networks for the same elections @cite_33 . @cite_9 analyzed user activity during the Spanish presidential elections. They additionally analyzed the 2012 Catalan elections, focusing on the interplay between the language and the community structure of the network @cite_21 . Most existing research, as Larsson points out @cite_24 , focuses on the online behavior of leading political figures during election campaigns.
@cite_35 @cite_18 @cite_33 @cite_28 @cite_9 @cite_21 @cite_24
2161834943 2079373549 2342270822 91442942
Parties, candidates, and voters are becoming increasingly engaged in political conversations through the micro-blogging platform Twitter. In this paper I show that the structure of the social networks in which they are embedded has the potential to become a source of information about policy positions. Under the assumption that social networks are homophilic (, 2001), this is, the propensity of users to cluster along partisan lines, I develop a Bayesian Spatial Following model that scales Twitter users along a common ideological dimension based on who they follow. I apply this network-based method to estimate ideal points for Twitter users in the US, the UK, Spain, Italy, and the Netherlands. The resulting positions of the party accounts on Twitter are highly correlated with oine measures based on their voting records and their manifestos. Similarly, this method is able to successfully classify individuals who state their political orientation publicly, and a sample of users from the state of Ohio whose Twitter accounts are matched with their voter registration history. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior is polarized along ideological lines. Using the 2012 US presidential election campaign as a case study, I nd that public exchanges on Twitter take place predominantly among users with similar viewpoints. Transmitting messages in the most efficient way as possible has always been one of politicians’ main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians’ actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermo... The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold. In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.