Echo Chambers in Parliamentary Twitter Networks:The Catalan Case

Social media is transforming relations among members of parliaments, but are members taking advantage of these new media to broaden their party and ideological communication environment, or they are mainly communicating with other party members and ideologically aligned peers? This article tests whether parliamentarians’ use of Twitter is opening communication flows or confining them to representatives of the same party or ideology. The study is based on a data set spanning the period January 1, 2013, to March 31, 2014, which covers all relations (4,516), retweets (6,045), and mentions (19,507) among Catalan parliamentarians. Our results indicate that communication flows are polarized along party and ideological lines. The degree of polarization of this network depends, however, on where the interactions occur: The relations network is the most polarized; cross-party and cross-ideological interactions are greater in the retweet network and most present in the mention network

Political Systems and Political Networks:The Structure of Parliamentarians’ Retweet Networks in 19 Countries

Social scientists have long studied international differences in political culture and communication. An influential strand of theory within political science argues that different types of political systems generate different parliamentary cultures: Systems with proportional representation generate cross-party cohesion, whereas majoritarian systems generate division. To contribute to this long-standing discussion, we study parliamentarian retweets across party lines using a database of 2.3 million retweets by 4,018 incumbent parliamentarians across 19 countries during 2018. We find that there is at most a tenuous relationship between democratic systems and cross-party retweeting: Majoritarian systems are not unequivocally more divisive than proportional systems. Moreover, we find important qualitative differences: Countries are not only more or less divisive, but they are cohesive and divisive in different ways. To capture this complexity, we complement our quantitative analysis with Visual Network Analysis to identify four types of network structures: divided, bipolar, fringe party, and cohesive.</p

Three Facets of Online Political Networks: Communities, Antagonisms, and Polarization

abstract: Millions of users leave digital traces of their political engagements on social media platforms every day. Users form networks of interactions, produce textual content, like and share each others' content. This creates an invaluable opportunity to better understand the political engagements of internet users. In this proposal, I present three algorithmic solutions to three facets of online political networks; namely, detection of communities, antagonisms and the impact of certain types of accounts on political polarization. First, I develop a multi-view community detection algorithm to find politically pure communities. I find that word usage among other content types (i.e. hashtags, URLs) complement user interactions the best in accurately detecting communities. Second, I focus on detecting negative linkages between politically motivated social media users. Major social media platforms do not facilitate their users with built-in negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Here, I present the SocLSFact framework to detect negative linkages among social media users. It utilizes three pieces of information; sentiment cues of textual interactions, positive interactions, and socially balanced triads. I evaluate the contribution of each three aspects in negative link detection performance on multiple tasks. Third, I propose an experimental setup that quantifies the polarization impact of automated accounts on Twitter retweet networks. I focus on a dataset of tragic Parkland shooting event and its aftermath. I show that when automated accounts are removed from the retweet network the network polarization decrease significantly, while a same number of accounts to the automated accounts are removed randomly the difference is not significant. I also find that prominent predictors of engagement of automatically generated content is not very different than what previous studies point out in general engaging content on social media. Last but not least, I identify accounts which self-disclose their automated nature in their profile by using expressions such as bot, chat-bot, or robot. I find that human engagement to self-disclosing accounts compared to non-disclosing automated accounts is much smaller. This observational finding can motivate further efforts into automated account detection research to prevent their unintended impact.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Twitter in local government: A study of Greater London authorities

Copyright @ 2011 SIG eGOVMicroblogging services are considered an emerging opportunity for authorities seeking to establish new communication channels with their public. Potential benefits evolve around enhancing transparency and interactivity, as well as sharing information regularly or during emergency events. The purpose of this exploratory study is to advance our empirical understanding of microblogging in local government. In particular, we reflect on online data collected to profile the use of Twitter by 29 Greater London local authorities (LAs). The study shows that London LAs have been accumulating significant experience with Twitter mainly over the past two years. In fact, many of them appear to incorporate conversational characteristics in their Tweets other than simply disseminating information. Furthermore, an analysis of Tweets during the August 2011 riots in England indicates the usefulness of the medium for responsibly informing the public and preventing rumours. Nevertheless, the study also identifies several points of improvement in the way public authorities are building their online networks; for example, in terms of connecting with each other and exploiting even more the conversational characteristics of Twitter