Because of their ever-growing importance, elite actors from the political sphere and news media have integrated social network sites and especially Twitter into their communication strategies. However, the extent of these adaptation processes is not yet fully understood. This article presents lists of U.S. actors from politics, news media and government. As an exploratory analysis, the influence of elites in U.S. political Twitter debates is investigated by applying basic measures of Twitter influence to two test datasets. online political communication; Twitter; politics; news media; government
Oftentimes it is assumed that the power and agenda-setting role of
established political and media elites are severely weakened on
the less hierarchical social web that promotes a collaborative
production of content [
Previous studies analyzed the influence of elites in political
Twitter debates ex post, based on the most retweeted messages or
centrality metrics [e.g. 2, 3, 7]. However, such inductive
procedures miss important communication in the long tail of elites
on Twitter that can only be captured by defining actors ex ante.
The contribution of this article is twofold: First, it presents lists of
twitter handles of actors from the U.S. government, news media
and politics [
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Published as part of the #Microposts2016 Workshop proceedings, available online as CEUR Vol-1691 (http://ceur-ws.org/Vol-1691)
The present study concentrates on political and news media actors who traditionally occupy the most powerful positions in representative democracy. The definition of actor groups is the most critical question when estimating the impact of elites on Twitter. As elites from political parties, I defined the sitting members of U.S. congress, incumbent governors, national party accounts and presidential candidates. In light of the constitutional separation of powers in the U.S., the group of government actors is classified separately and is comprised of the President and his social media accounts, the government departments as well as their respective secretaries.
The definition of news media actors is the most intricate
conceptual challenge, since the production and dissemination of
news is becoming increasingly fuzzy on the social web. Clay
Shirky, who is an often-cited source with regard to collaborative
news production, proposed definitional boundaries between “a
mass amateurization” of news production on the social web and a
“professional class” that “implies specialized functions, minimum
tests for competence, and a minority of members” [
Studies have shown that elites still have considerable influence in
political debates on Twitter [
I extracted publicly available Twitter lists from @gov, @cspan
and a collection of influential news media accounts from Daniel
Romero1 as a starting point for generating the actor lists [
The estimation of actor importance and the identification of
influential users are recurring topics in web science [
In order to generate empirical test datasets, I extracted tweets containing the main debate hashtags on two political topics. #SOTU was the main hashtag accompanying Barack Obama’s State of the Union Address in front of U.S. Congress in January 2015. #NetNeutrality refers to a policy discussion on the regulation of data traffic on the internet. The debates differ with regard to the time period covered and the tweet volume generated. As the data mining was based on Twitter hashtags (Table 1), issue-related tweets without these particular hashtags were not captured. This could be problematic if the use of hashtags varies systematically across actor groups. These limitations are discussed in the empirical section.
Figure 1 is a log-log plot displaying the number of followers
plotted by the rank of an actor in its respective group.3 In all three
groups, the follower distribution is heavily skewed. The decay of
the tails resembles a linear pattern until the curve drops steeply at
the lower ends of the distribution. The unequal distribution of
attention is a typical phenomenon on the web in general and
especially in the political sphere, as Hindman, among others,
showed in his study of power laws in the political blogosphere [
The present chapter applies the actor lists to the two test datasets.
Figure 2 shows the influence of elite actors according to the two
most important conversation practices on Twitter. Displayed is the
share of elites in the aggregate number of retweets and
@-mentions. The most evident pattern is the influential role of the
government with a mention share of 29% in #SOTU and 56% in
#NetNeutrality. In both debates, government actors were the
central political figures. President Obama as the speaker attracted
the highest share of attention during the #SOTU, whereas in the
#NetNeutrality debate the FCC and its chairman Tom Wheeler
were the accounts to which most users referred.4 The high number
of mentions reflects their perceived importance in the two debates.
However, this does not equal an endorsement. Government actors
get retweeted, which is a stronger signal of political homophily
[
Deviations from the preceding analysis that focused on follower counts are evident when looking at the relatively sparse attention share of news media and actors from politics. When inspecting tweet contents and URLs, it becomes apparent that this is mostly due to their own restrained tweeting patterns. The main event hashtags were rarely used by these groups, although they regularly commented on the events. Further qualitative research should investigate the social media strategies of those in charge of the Twitter accounts in America’s newsrooms and politics. Figure 3 amplifies the skewed distribution of attention on the web by showing the retweet per tweet ratios of actor groups in the two debates. The relative outreach that elites generated per tweet is considerable when compared to non-elite users. Non-elite users had a retweet outreach per tweet below zero, even though this category also includes a multitude of influential accounts like NGOs. This urgently points towards the need to further disaggregate the residual category of others. By adding more lists, political Twitter debates can be analyzed in even more detail. 4 The FCC is a regulatory agency. Its five commissioners are appointed by the President. The varying results for government actors in the #NetNeutrality debate underline the diverging political and social meanings of Twitter metrics. The total share of retweets originating from government accounts is relatively marginal (Figure 2). However, when the government used the hashtag #NetNeutrality, it generated 492 retweets per tweet (Figure 3). When central figures in political events intensify their efforts to influence hashtag communities, they have success in doing so.
Figure 3 confirms that the most influential actors in the two
debates were affiliated with the government, while tweets from
news media and politics diffused less widely on the web. This
finding depicts “going public” strategies by the government [
This paper presented lists of elite actors enriched with additional information that enable researchers to investigate a broad range of research questions related to the activity of U.S. elites and nonelites in online political communication, such as: Are politicians from the Democratic Party more successful in framing political debates on Twitter than Republicans? What can we learn about the dynamics of communication between elites and between elites and their audience when looking at conversation patterns? In an empirical application of the actor lists, government and news media were most influential according to their number of followers and also when assessing retweets and @-mentions. Yet, within these groups and throughout the study, the skewed distribution of influence on the political web became apparent. Party politicians in particular played a minor part in all of the analyses. Their role needs to be contextualized by analyzing Twitter communication on additional political topics.
Methodologically, the analysis remained at a basic level of
analysis by focusing on distributions of Twitter metrics. There is a
great potential to further elaborate on these preliminary results by
applying the toolkit of network science. Subsequent applications
of the actor lists should include topically relevant hashtag
populations and keywords as well as information diffusion via
URLs [