Social Network Analysis

Social Network Analysis (SNA) has been used to investigate teachers’ practitioner- based social capital in an informal advice community (Baker-Doyle & Yoon, 2011) and to analyse the structure and the interactions of Twitter chats (Gao & Li, 2016; Rehm & Notten, 2016).

Baker-Doyle and Yoon’s (2011) mixed-methods study found SNA useful for revealing the structure of informal teacher networks and the strategies teachers use for accessing and sharing information. From their study the authors concluded that teachers’ relations within networks need to be balanced, and that networks “need to make effective use of the ‘experts’ and ‘bridge builders’ in the group.” (p. 89). Similar to Baker-Doyle and Yoon’s (2011) study, Rehm and Notten (2016) drew on theories of social capital to investigate the Twitter hashtag network #EdchatDE, a network which is predominantly used by German school teachers. In the context of networked learning social capital can be understood as “a way of thinking of the benefits accrued from relationship building” (Fox & Wilson, 2015, p. 94). In their longitudinal study Rehm and Notten (2016) found that “participating in hashtag conversation on Twitter does indeed contribute to individual teachers’ formation of structural capital” (p. 220). However, the study also indicated a positionality of social capital gains, i.e. teachers with a central position within the complete network or within sub-groups possess more opportunity to accrue social capital than teachers who are at the periphery.

Gao and Li (2016) analysed teacher interactions in a one-hour synchronous Twitter chat of the #Edchat Twitter network. In their SNA approach Gao and Li used

the software NodeXL for analysing the network structure and relied on Grounded Theory (GT) for analysing textual data from the tweets. Employing SNA the authors found different types and levels of interactions among participants and a range of topics that were discussed in relation to the chat topic. From their investigation the authors concluded that “such online chat events could be an effective activity for participants to brainstorm ideas, gain various perspectives, share resources and build social

connections” (Gao & Li, 2016, p. 12).

In my pilot study I built on Gao and Li’s experiences and applied NodeXL to an analysis of the Twitter hashtag network #mfltwitterati. As described above, I collected tweets and retweets from the Twitter language teacher network #mfltwitterati to learn about the structures that underpin language teachers’ tweeting activities. To that end I collected and analysed 451 tweets and retweets from 241 network actors (see Table 4.1). The software NodeXL was also used for all social network analyses and network visualisations during the main study (see Chapter 5). However, for the main study I used the paid-for version, NodeXL Pro, which offers more functionalities than the free version, such as advanced network metrics.

For Marin and Wellman (2014) a social network is “a set of socially relevant nodes connected by one or more relations. Nodes, or network members, are the units that are connected by the relations whose patterns we study” (p. 11). Thus, Social Network Analysis (SNA) could be seen as an attempt to make sense of the structure of relations between network actors. In this thesis Twitter users are ‘nodes’ and their tweets and retweets are the relations that are investigated. Nodes are also known as ‘vertices’ and relations are frequently referred to as ‘ties’ or ‘edges’ in the literature (Wassermann & Faust, 1994). In order to avoid confusion I will refer to the nodes of a

network as ‘actors’ and to their relations with other network actors as ‘tweets and retweets’.

SNA uses measurements to investigate the structural relations between network actors and their positions in a network: “Central to social network analysis is the contention that one’s location in a social structure shapes one’s opportunities and outcomes.” (Carolan, 2014, p. 8). Network-level structural measures provide an overview of the network’s structure and the pattern of relations among the actors of a network. In this thesis I concentrated on five network-level structural measures which were supported by the software NodeXL to describe the Twitter network #mfltwitterati (pilot study) and to analyse the networks #ELTchat, #TBLTchat and #LTHEchat (main study). These measures are size, density, diameter, clustering and centralisation.

The analysis of the #mfltwitterati network showed that there were 241 actors and 475 tweets and retweets during the three-day investigation period in this directed

network. This means that the tweets and retweets were directed to other network actors, who did not necessarily reciprocate, leading to asymmetrical relations. The size of a network is important, because it “influences the structure of relations, as actors only have so many resources and capacities for creating and maintaining ties with others” (Carolan, 2014, p. 101). The bigger a network, the more likely is it that the connectivity of network actors varies greatly. Within the #mfltwitterati network a significant number of actors (10.37% of all network actors) did not have any interactions with other actors of the network at all. These actors directed their tweets to #mfltwitterati by including this hashtag in their tweets, but did not mention individual Twitter users or replied to any users. Since tweets from these actors were neither retweeted nor replied to, they did not become part of the mainstream communication in this network, which effectively led to these actors becoming isolates.

Network density is closely linked to size and measures the strength of a network. It is calculated by dividing the number of actual ties by the number all possible ties there could be between actors in this network. Put differently, network density relates the number of connections that could possibly exist between all network actors to the number of connections that actually exist in the network. The highest density would be 1, which means that every network actor is connected to all other network actors. A calculation of #mfltwitterati’s network density showed it to be 0.0042. A network density of 0.0042 means that there were only 4 actual tweets out of 1,000 possible tweets, which could have been in this network, indicating a low density of the overall network.

Another useful network-level property is the diameter of a network, which measures how fast resources, such as information, travel within a network:

“A network's diameter refers to the longest path between any two actors. This property is important, as networks that have the same size (equal numbers of actors) and even the same density (equal percentages of ties present) can have different diameters.” (Carolan, 2014, p.105)

#mfltwitterati’s maximum distance between any two actors was 9, indicating that it took 9 tweets for the two most distant network actors in the network to reach each other. The average path length was 3.9, which means that on average it took about four tweets for information to travel from one network actor to another, assuming that every actor can connect to all other actors in the network. However, this is not always the case, as bigger networks tends to form clusters.

“High clustering indicates that there are numerous pockets in which some actors are connected to each other but not to others. Low clustering, on the other hand, suggests that relations are more evenly distributed across the network with very few pockets of dense connectivity among subsets of actors.” (Carolan, 2014, p. 106).

Clusters can also be called sub-groups. For #mfltwitterati 37 sub-groups could be distinguished. Looking at the sub-groups more closely, it became apparent that the five largest sub-groups comprised almost half (47.69%) of all network actors, with the remaining actors being spread across the remaining 32 sub-groups. This signifies a fragmented network with different interaction patterns in its sub-groups.

The positon of network actors within the complete network and/or within a particular sub-group is linked to the flow of resources within this network. ‘Popular’ actors with many connections within a sub-group or within a complete Twitter hashtag network have more influence over the process of resource distribution than actors who have only few connections within the network. Put differently, ‘popular’ actors are more central to the flow of resources. In a directed network centralisation for the whole network can be measured by counting the number of ties, e.g. tweets, that are directed towards a particular actor (in-degree) and the number of ties that go from this actor to other actors (out-degree). A high out-degree score in a Twitter hashtag network means that a particular actor often contributes to the communications that take place in this network, whereas a high in-degree score indicates that a particular actor is frequently referred to by other network actors in their tweets and retweets. However, the position of an actor within a network is not only linked to the number of connections but also to his/her position with regard to the connections that other actors have in a network. For #mfltwitterat, one sub-group was central to the network communications within the

three days that were investigated. This group was the most active sub-group in the entire network. It showed a very dense interaction pattern within its sub-community and distinctive interactions with other sub-communities. Incidentally, this sub-group contained five actors out of the 20 most influential network actors, among them the founder of #mfltwitterati. This actor in particular acted as a bridge between different sub-groups, as indicated by his high betweenness centrality score.

Betweenness centrality “captures how actors control or mediate the relations between pairs of actors that are not directly connected” (Carolan, 2014, p. 157). Actors with a high betweenness centrality have a gatekeeper function. They can significantly increase or change the flow of resources, e.g. by tweeting and retweeting specific information in a Twitter hashtag network. Actors with a high betweenness centrality can also act as ‘bridges’ between network clusters that are not connected to each other. Conversely, gatekeepers can also hinder the flow of resources, simply by not tweeting or retweeting information that other actors have no other means of accessing within the Twitter hashtag network. Overall, actors with high betweenness centrality therefore “may have considerable influence within a network by virtue of their control over information passing between others” (Newman, 2010, p. 186). The betweenness centrality scores for #mfltwitterati revealed that the founder of #mfltwitterati acted as the main bridge in this network.

However, metrics derived from Social Network Analysis only provide a limited picture of relations between Twitter users, because they rely on measurable activities, such as tweeting or retweeting. These metrics do not ‘see’ when a Twitter user reads a tweet, although reading is the first activity that occurs when a Twitter user opens his/her timeline. Furthermore, the quantitative measures of SNA do not reveal anything about the situation in which tweeting or retweeting occurred, i.e. which other elements worked

in this situation. Haynes (2010) asserted that a network approach is useful for describing a wide range of diverse phenomena but raised criticism that “such models lack a consistent and robust ontological framework” (p. 1). From his own research Haynes concluded that networks can provide useful descriptions, whereas assemblages offer powerful explanations for social phenomena.