The Dynamics of Message Selection in Online Political Discussion Forums: Self-Segregation or Diverse Exposure?

Abstract

While the online sphere is believed to expose individuals to a wider array of viewpoints, a worry about self-reinforcing political echo chambers also persists. We join this scholarly debate by focusing on individual motives for political discussion and dyadic- and structural-level mechanisms that can drive one’s message-selection decision in online discussion settings. Using unobtrusively logged behavioral data matched with panel survey responses, our temporal exponential random graph model (TERGM) analysis indicates that message selection in online discussion settings is largely driven by the similarity of one’s candidate evaluative criteria and various endogenous structural factors, whereas the impact of overt partisan preference in shaping message selection is much more limited than is often assumed.

Showing off Your Social Capital: Homophily of Professional Reputation and Gender in Journalistic Networks on Twitter

“…What is more, future research should consider the beat, the medium type, the geographic location of a journalist, the network-endogenous measures (e.g., reciprocity), and the concrete content of Twitter messages, all of which were not within the scope of the current analysis. This is not only relevant to identify tangible patterns of communication to garner peer recognition but also to further examine homophily by looking at textual similarity or the topical focus of tweets (aspects suggested, for example, by Song, Cho, and Benefield 2020). Moreover, while it paid off that we had a more nuanced measurement of professional reputation by using three aspects with different functions, further studies should look more into their complex interrelation.…”

Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization

“…Nonetheless, a broadening of search terms could be possible when individuals pick up ideas discussed and shared within their social networks to use in searches. Although social network construction is self-selective, ideas available in social networks, especially those based on online platforms, could go beyond the boundary of self and be more diverse than selfgenerated ideas (Kwak et al, 2010;Song et al, 2020). Further, in the context of social media, Messing and Westwood (2014) found that individuals often consume news from ideologically misaligned sources when it is recommended by their friends.…”

“…A great deal of research suggests that citizens enabled by a high degree of user control, tend to selectively seek information and maintain communication networks consistent with their political orientations, which eventually reinforces the partisan divide in society (Bennett & Iyengar, 2008;Gentzkow & Shapiro, 2011). On the other hand, there also exists ample evidence that, taking advantage of the increasing choice opportunities in the online sphere, citizens often expose themselves to a range of ideas that cut across partisan or ideological lines (Beam, 2014;Garrett & Stroud, 2014;Song, Cho, & Benefield, 2020). This research points out that information consumption and political discussion online are perhaps more complicated than suggested by the selective exposure literature (Bakshy et al, 2015;Flaxman et al, 2016).…”

Peer Review #2 of "A survey on exponential random graph models: an application perspective (v0.1)"

“…In the case of political conversations in social networks, there is always the dilemma whether this freedom has resulted in more communications between people with different ideologies or adversely it will cause people with same viewpoints tend to dominate most of the conversation thereby self-reinforcing the same way of thinking. (Song, Cho & Benefield, 2018) addressed this issue by studying the network of PeerJ Comput. Sci.…”