Social (Media) Ties – It’s Complex Multiplex

Reading social media studies and social network (analysis) literature I am regularly surprised at how unidimensional the operationalization of social connections is. Ties are often just treated as present or absent – but not as a “channel” for interaction flows that occur more or less intensly, depending on the type of relation. A Facebook “friendship” or a reciprocal Twitter following/follower relationship serves as an invitation to interact and exchange content. This content can have various forms: likes, shares, comments, wall posts (e.g., birthday wishes), private messages, chats, picture tags etc. The content flow between “friends” or followers – not just the mere presence/absence of the connection – should be considered when analyzing the relations. Hence, the activation/latency question requires more attention. Of course, such an analysis of content flows is very complex since it entails the combination of different data types and also poses serious privacy risks.

Another aspect of the unidimensionality in thinking about social connections concerns the neglection of mulitplexity. Multiplexity describes the existence and interplay of different types of ties in social networks. For example, co-workers can have friendships, advice ties, trust relationships and online communication connections with one another and some of these tie forms might overlap: A friend might also (but need not necessarily) be someone I trust or communicate online with. When several forms of relations overlap, we have multiplexity.

I think the interesting thing about multiplexity is not so much the overlap but the interaction between different types of ties. Let’s transfer this thought to social media and let’s assume that there exists a “like – private message” multiplexity between me and one of my Facebook friends. How does the fact that I write personal messages with my friend affect my propensity to like his/her stuff and vice versa? Do I like more of his/her content because I write personal messages? Do I like less of her/his content (because I might assume that personal messages form a container to convey my opinions in a more comprehensive and personal way)? Or is there no effect? Such questions about multiplexity merit more in-depth analysis.

We can borrow the multiplexity concept from social network analysis and transfer it to other areas. One research domain where it makes sense to use multiplexity as a framework is online participation. Many Internet users participate in various contexts: They share interesting articles on Twitter, comment their friends Facebook photos, do some online voting and engage in a discussion board of their topic of interest, let’s say silent movies from the 1919-1921 period. Such multiplexity in participation can stand next to each other without interacting (I don’t want to bother my Twitter followers with boring 1919-1921 silent movies or bombard my Facebook friends with links to – for them – uninteresting articles). However, interaction or mutual influence can also occur. If I’m smart and context-aware enough, I can conciously bring in skills and knowledge acquired from one participation area to the other. For example, the etiquette for a “good” Facebook photo comment can be adapted to provide a great post in the 1919-1921 silent movies discussion board or to cast a sensible vote in an online voting. Such a transfer might also happen unconsciously and subtly, though – and I think that’s the more interesting form. How could we investigate such interplays and subtle influences? I don’t know exactly (and would have to further think about it) but qualitative and especially ethnographic approaches are probably most suitable at this point.

Summed up, multiplexity is a good, yet not enough used concept. Its increased application in social media and Internet questions could lead to insightful results.

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