Pascal Jürgens

Small worlds with a difference: new gatekeepers and the filtering of political information on Twitter

Political discussions on social network platforms represent an increasingly relevant source of political information, an opportunity for the exchange of opinions and a popular source of quotes for media outlets. We analyzed political communication on Twitter during the run-up to the German general election of 2009 by extracting a directed network of user interactions based on the exchange of political information and opinions. In consonance with expectations from previous research, the resulting network exhibits small-world properties, lending itself to fast and efficient information diffusion. We go on to demonstrate that precisely the highly connected nodes, characteristic for small-world networks, are in a position to exert strong, selective influence on the information passed within the network. We use a metric based on entropy to identify these New Gatekeepers and their impact on the information flow. Finally, we perform an analysis of their input and output of political messages. It is shown that both the New Gatekeepers and ordinary users tend to filter political content on Twitter based on their personal preferences. Thus, we show that political communication on Twitter is at the same time highly dependent on a small number of users, critically positioned in the structure of the network, as well as biased by their own political perspectives.

Forecasting the pulse: : How deviations from regular patterns in online data can identify offline phenomena

Purpose – The steady increase of data on human behavior collected online holds significant research potential for social scientists. The purpose of this paper is to add a systematic discussion of different online services, their data generating processes, the offline phenomena connected to these data, and by demonstrating, in a proof of concept, a new approach for the detection of extraordinary offline phenomena by the analysis of online data. Design/methodology/approach – To detect traces of extraordinary offline phenomena in online data, the paper determines the normal state of the respective communication environment by measuring the regular dynamics of specific variables in data documenting user behavior online. In its proof of concept, the paper does so by concentrating on the diversity of hashtags used on Twitter during a given time span. The paper then uses the seasonal trend decomposition procedure based on loess (STL) to determine large deviations between the state of the system as forecasted by ...

Through a Glass, Darkly: Tactical Support and Symbolic Association in Twitter Messages Commenting on Stuttgart 21

Political actors increasingly use the microblogging service, Twitter, for the organization, coordination, and documentation of collective action. These interactions with Twitter leave digital artifacts that can be analyzed. In this article, we look at Twitter messages commenting on one of the most contentious protests in Germany’s recent history, the protests against the infrastructure project Stuttgart 21. We analyze all messages containing the hashtag #s21 that were posted between May 25, 2010, and November 14, 2010, by the 80,000 most followed Twitter users in Germany. We do this to answer three questions: First, what distinguishes events that resulted in high activity on Twitter from events that did not? Second, during times of high activity, does the behavior of Twitter users vary from their usual behavior patterns? Third, were the artifacts (retweets, links) that dominated conversations during times of high activity indicative of tactical support of the protests or of symbolic association with it?

Wahlkampf vom Sofa aus : Twitter im Bundestagswahlkampf 2009

Der deutsche Bundestagswahlkampf im Superwahljahr 2009 war mit seinen Online-Elementen im Wesentlichen durch zwei Umstande gepragt: Die Erwartungen von Offentlichkeit und Medien einen starken Online-Wahlkampf zu erleben und den Online-Experimenten der Parteien im direkten Vorlauf zur Bundestagswahl.

The Mediation of Politics through Twitter: An Analysis of Messages posted during the Campaign for the German Federal Election 2013

Patterns found in digital trace data are increasingly used as evidence of social phenomena. Still, the role of digital services not as mirrors but instead as mediators of social reality has been neglected. We identify characteristics of this mediation process by analyzing Twitter messages referring to politics during the campaign for the German federal election 2013 and comparing the thus emerging image of political reality with established measurements of political reality. We focus on the relationship between temporal dynamics in politically relevant Twitter messages and crucial campaign events, comparing dominant topics in politically relevant tweets with topics prominent in surveys and in television news, and by comparing mention shares of political actors with their election results.

Digital Trace Data in the Study of Public Opinion : An Indicator of Attention Toward Politics Rather Than Political Support

In this article, we examine the relationship between metrics documenting politics-related Twitter activity with election results and trends in opinion polls. Various studies have proposed the possibility of inferring public opinion based on digital trace data collected on Twitter and even the possibility to predict election results based on aggregates of mentions of political actors. Yet, a systematic attempt at a validation of Twitter as an indicator for political support is lacking. In this article, building on social science methodology, we test the validity of the relationship between various Twitter-based metrics of public attention toward politics with election results and opinion polls. All indicators tested in this article suggest caution in the attempt to infer public opinion or predict election results based on Twitter messages. In all tested metrics, indicators based on Twitter mentions of political parties differed strongly from parties’ results in elections or opinion polls. This leads us to question the power of Twitter to infer levels of political support of political actors. Instead, Twitter appears to promise insights into temporal dynamics of public attention toward politics.

Communities of Communication: Making Sense of the “Social” in Social Media

As social media usage permeates peoples lives, an increasing portion of their daily behavior leaves digital traces to be used by researchers. Social scientists can hope to gain new insight into the previously hidden but digitally recorded aspects of our digital social lives. Beyond aggregate and individual-level studies of user behavior, the digital traces also enable scientific examination of the structure of social interaction through networks. At the same time, the large scale and networked nature of social media data pose a new set of challenges to be overcome through the development of sound methodologies. We take stock of current methodological promises and challenges in social media analysis. Community detection, a set of methods for the discovery of closely knit groups, is then presented as an intermediary step that enables application of existing traditional and network analytical approaches in a smaller setting more suited to social scientific questions. In closing, we argue that this network proximity-based clustering is often more useful for social media analysis than demographic grouping.

Stuttgart’s Black Thursday on Twitter: Mapping Political Protests with Social Media Data

Event detection based on textual data is an approach often used in the social sciences. The method has been used predominantly in the fields of international politics (Schrodt 2010) and public opinion research (Landmann and Zuell 2008). Event detection presupposes that major events leave traces in textual documents. By automatically identifying events in publicly available documents, researchers can establish timelines of events relevant to their research. For example, in international politics, researchers work on how to reliably identify political actors, time and topics from official documents, hoping to establish comprehensive and detailed maps of international treaties and conflicts. Based on these maps, they aim to develop models of the dynamics of conflict (Brandt et al. 2011). In public opinion research, one goal is to automatically deduce major events from newspaper coverage. This might be a first step in calculating the impact of these events on changes in public opinion (Landmann and Zuell 2008).

The Use of Twitter during the 2009 German National Election

During the 2009 election campaign, Twitter not only served as a source of news for the media but also became a public stage for active political users. In particular, hopes were raised about a pluralistic grass-roots sphere of public communication in which political information can be shared in a non-ideological, decentralised and egalitarian manner. To test whether Twitter led to new patterns of political interaction and to determine the beneficiaries, we present findings from a large-scale network analysis investigating about four million tweets by more than 33,000 users including citizens, journalists and politicians in the 2009 National Election. Our analysis identifies the most popular users, contents and topics in this political sphere, revealing the Pirate Party movement as the most influential group during the campaign. A network analysis of the participating actors confirms the strong position of established online activists and bloggers in contrast to traditional mass media, politicians and parties.

A Tutorial for Using Twitter Data in the Social Sciences: Data Collection, Preparation, and Analysis

The ever increasing use of digital tools and services has led to the emergence of new data sources for social scientists, data wittingly or unwittingly produced by users while interacting with digital tools. The potential of these digital trace data is well-established. Still, in practice, the process of data collection, preparation and storage, and subsequent analysis can provide challenges. With this tutorial, we provide a guide for social scientists to the collection, preparation, and analysis of digital trace data collected on the microblogging service Twitter. This tutorial comes with a set of scripts providing researchers with a starter kit of code allowing them to search, collect, and prepare Twitter data following their specific research interests. We will start with a general discussion of the research process with Twitter data. Following this, we will introduce a set of scripts for data collection on Twitter. After this, we will introduce various scripts for the preparation of data for analysis. We then present a series of examples for typical analyses that could be run with Twitter data. Here, we focus on counts, time series, and networks. We close this tutorial with a discussion of challenges in establishing digital trace data as a normal data source in the social sciences.