This paper investigates political homophily on Twitter. Using a combination of machine learning and social network analysis we classify users as Democrats or as Republicans based on the political content shared. We then investigate political homophily both in the network of reciprocated and nonreciprocated ties. We find that structures of political homophily differ strongly between Democrats and Republicans. In general, Democrats exhibit higher levels of political homophily. But Republicans who follow official Republican accounts exhibit higher levels of homophily than Democrats. In addition, levels of homophily are higher in the network of reciprocated followers than in the nonreciprocated network. We suggest that research on political homophily on the Internet should take the political culture and practices of users seriously.
for very helpful comments and discussions on previous versions of this paper. We also benefited from presenting previous versions at the 2014 annual meeting of the Academy of Management and at a 2015 OTREG meeting. We also thank the European Commission for research funding (EC Marie Skłodowska-Curie Actions European Fellowship scheme).
If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Organization legitimacy is a general reflection of the relationship between an organization and its environment. By adopting an institutional approach and defining moral legitimacy as "a positive normative evaluation of the organization and its activities", the goal of this paper is to investigate which corporate communication strategy adopted in online social media is more effective to create convergence between corporations' corporate social responsibility (CSR) agenda and stakeholders' social expectations, and thereby, to increase corporate legitimacy. Design/methodology/approach -Using the entire Twitter social graph, a network analysis was carried out to study the structural properties of the CSR community, such as the level of reciprocity, and advanced data mining techniques, i.e. topic and sentiment analysis, were carried out to investigate the communication dynamics. Findings -Evidence was found that neither the engaging nor the information strategies lead to alignment. The assumption of the more the dialog, the more the communality seems to fail to portray the complexity of the communicational dynamics, such as the persistence of different, or simply a dialog without alignment. Empirical findings show that, even when engaging in a dialogue, communication in social media is still conceived as a marketing practice to convey messages about companies. Originality/value -This paper originally investigates organizational legitimacy in the context of social media by applying advanced data-mining techniques that allow the analysis of large amounts of information available online.
The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times (Berger and Milkman, 2010) finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media (Galtung and Ruge, 1965) emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media (Kwak et al., 2010). The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.
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