The Trend of Social Network Analysis
As the social network sites (SNSs) play an increasingly prominent role in our lives, more studies are conducted to examine the interactions between SNS users within the theoretical frame of network analysis of social science. Donath and boyd (2004) argued that SNSs could accumulate the weak ties one could create and maintain with others because of the affordance of social media technology. Weak ties refer to the loose connections between people who share practical information and resources with each other but not emotional support (Granovetter, 1982). Similarly, Haythornthwaite (2005) pointed that one impact of the new communication technology is to create new weak connections where they did not exist before, but meanwhile she found that some online SNS users also reported having strong ties with other users engaging emotional support.
With the increase of social networking studies, social networking data from these platforms becomes easily accessible. NodeXL is dedicated to collect data from multiple platforms, and then to analyze and visualize the data with community graphs. Smith et al (2014) summarized the community graphs made with NodeXL into six types: Polarized Crowd, Tight Crowd, Brand Clusters, Community Clusters, Broadcast Network and Support Network. Two graphs were selected from NodeXL Gallery to illustrate two types of community graphs.
Tight Crowd: WhenCallsTheHeart
The first graph to analyze is the network of 1,119 Twitter users whose tweets included the keywords“WhenCallsTheHeart” OR “When Calls the Heart”OR “WCTH_TV” from August 21, 2017 to September 2, 2017 , or who were replied to or mentioned in those tweets, as shown below. When Calls the Heart is a Canadian-American TV series, debuting in January 2014 on Hallmark Channel.
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=121633
As Smith et al. (2014) summarized, strong interconnections existed both within and between subgroups in Tight Crowd communities, where users share the same interests and talk about the same topic without extremely polarized disagreements. From the graph above, it is obvious that the three largest groups are densely interconnected and users within each are also connected despite very few isolated users. The most frequent hashtags in each subgroup are #hearties, #whencallstheheart, #wcth, and etc, which demonstrates that almost all the users share the common interest, the TV drama When Calls the Heart. The top influencers in the whole graph are @wcth_tv with 47.8K followers (the official Twitter account of When Calls the Heart), @hallmarkchannel with 179K followers (the official Twitter account of Hallmark Channel), followed by @erinkrakow with 74.4K followers (the Twitter account of Erin Krakow, leading actress in the drama).
Brand Cluster: Volkswagen:
The second one is the network of 4,157 Twitter users whose tweets contained “#Volkswagen OR #VW” between September 8, 2018 and September 14, 2018, or who were replied to or mentioned in those tweets.
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=168067
Brand Cluster networks are featured with the limited interconnection among the subgroups and large portions of isolated users. Most participants in the discussion mentioned the keywords of certain brands without mentioning or connecting to each other. As shown in the above graph, G1 contains a large number of isolated users, while the rest parts are small subgroups with very limited interconnection among some of them like G3, G4 and G5, represented by the red edges between users in different subgroups. The top influencers in this graph are @volkswagen with 129K followers (the official Twitter account of Volkswagen), @cjbmotorsports with 785K followers (an automobile KOL in Colombia) and @vzbv with 14.9K followers (the Twitter account of Germany Consumer Federal Association), as these users are more powerful in the communities.
As the top hashtag list showed, despite the two brand hashtags #Volkswagen and #VW, the most frequent hashtags of the whole graph also contained words like #beetle and #vwbeetle, triggered by the Volkswagen announcement of stopping the productions on this September 14. However, except for the top hashtags mentioned above, the top hashtag lists of each subgroup barely overlapped with each other, meaning that each subgroup were discussing different topics around the same brand.
Weak Tie vs. Networked Individualism
Like what Rainie & Wellman (2012) said, the era of networked individualism is fraught with loose and fragmented networks that shared information, resource and aid. The second graph examined above involved purely information sharing about a specific brand and product, and thus the connections are loose and segmented. Similarly, the interest-based network of the first graph, WhenCallsTheHeart, is not definitely emotionally close and connected, though the participants appear more densely connected.
But on the bright side, the social networking platforms like Twitter provide people with more freedom to express their opinions, talk to others who share the common interests, and find the communities they may belong to, no matter how small the communities are. People, empowered with capabilities to actively search and gather information, are more as “connected individuals” and less as “embedded group members” in Rainie & Wellman’s words (2012, p.12).
References:
Donath, J., & boyd, d. (2004). Public displays of connection. BT Technology Journal, 22(4), 71.
Granovetter, M. S. (1982). The strength of weak ties: A network theory revisited. In P. V. Mardsen & N. Lin (Eds.), Social Structure and Network Analysis (pp. 105–130). Thousand Oaks, CA: Sage Publications.
Haythornthwaite, C. (2005). Social networks and Internet connectivity effects. Information, Community & Society, 8(2), 125-147.
Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. Cambridge, Massachusetts: MIT Press.
Smith, M. A., Rainie, L., Shneiderman, B., & Himelboim, I. (2014). Mapping Twitter topic networks: From polarized crowds to community clusters. Pew Research Center, 20, 1-56.