As Joe Murphy mentioned in the preceding post, Twitter can provide a unique and efficient glimpse into conference dialogue that may be difficult to capture in other ways. We can measure when conversations occur between tweeters, the subject of those conversations, and other interesting patterns such as the most frequent words appearing in Tweets or the most popular Tweet (as indicated via retweets). There’s one analytic possibility, though, that’s frequently overlooked: social network analysis (SNA).
As I discussed in my poster presented at this year’s AAPOR conference, something inherent and potentially very useful to researchers is the network aspect of social networking sites. Inspired by a growing Twitter presence among the AAPOR community, and Joe’s analysis of the AAPOR Twittersphere, I decided to put some of the SNA concepts I discussed in my poster to practice. Specifically, in my attempt to examine public opinion in a different light, I asked the question “who is talking to whom?”
For those of you familiar with SNA, or are just curious as to how the measures I discuss are actually quantified, I’ve pulled the equations and explanations from my poster and inserted them for reference. In the first image below, a birds-eye view captures what the AAPOR Twitter conversation looked like. By conversation, I’m specifically referring only to Tweets where one user Tweeted to another using the hashtag #AAPOR. For instance, if (hypothetically speaking) Joe (@joejohnmurphy) Tweets to me “hey @AdamSage, your poster was A-M-A-Z-I-N-G #AAPOR,” the link connecting Joe to me would be directed from Joe to me.
At first glance, this birds-eye view looks kind of cool (or like a big mess depending on your perspective) – but what does it mean? Each dot, or node, represents a Twitter user that at one point used the hashtag #AAPOR and mentioned another Twitter user in a Tweet. Each line represents the association that’s created when such a Tweet occurred. The degree of each node is number of different connections made as a result of these Tweets. In the AAPOR Twittersphere, the average degree, or the average number of people to whom a given Twitter user is linked via these dialogues, is slightly over 2 (2.017). This is important because an average degree of 1 would mean we’re not a community, but rather a group of 1 on 1 dyadic cliques, which makes ideas difficult to spread. While these did occur, I excluded them in these graphs and focused what is called a giant component in SNA, or the largest group of connected individuals in a network.
Excluding those conversations occurring outside of the giant component, the diameter of the entire conference Twitter conversation was 8. In other words, the furthest any two people were away from each other during this Twitter conversation was 8 steps. Imagine yourself at the largest Tweet-up in AAPOR history consisting of those AAPOR attendees participating in a dialogue on Twitter (using #AAPOR of course). As people traverse from conversation to conversation, everything you say would be no more than 8 conversations away from anyone in the room. So that rumor about my awesome poster could theoretically spread quite quickly. Hopefully it’s starting to become clear how great papers and keynote addresses can become popular Twitter topics in a given year. Great papers resonate and travel quickly; great keynotes are viewed by a lot of people and travel even faster.
But wait, it gets better. Ideas spreading among the AAPOR community wouldn’t traverse the network as you might think. Looking at the overall graph below you will notice several color-coded groups of tweeters. Each color represents a community, which is essentially defined as those tweeters who are mentioning each other in their Tweets more than others. For instance, community around @AAPOR (the green colored node with several links in the top middle) does not include many of the users with high betweenness and closeness. Betweenness measures the strength of one’s connection to the entire network (i.e., how interconnected or embedded one is to the entire network), and closeness measures one’s distance to the overall network (an average distance). They are measured as:
As you might expect, these people tend to drive conversations because they have many connections, which when analyzed using certain measures of network centrality, can reveal likely members of such conversations. For instance, just by measuring mere mentions within Tweets, clustering coefficients (defined as a given groups number of connections divided by the number of possible connections) allow us to distinguish “communities” from the rest of the network. Because this graph is directed, meaning I can tell if someone is doing the Tweeting or being Tweeted at, I know a majority of @AAPOR links were created by people mentioning @AAPOR in a Tweet, rather than @AAPOR mentioning others. The community around @AAPOR (the green colored node with several links in the top middle circled in red) does not include many of the users with high betweenness and closeness because @AAPOR’s propensity to draw Tweets to it naturally steals the thunder of everyone in close proximity.
While I haven’t analyzed the content of the conversations occurring among these communities, my guess is there are general topics (e.g., polling, elections, survey methods, statistics etc). After all, we tend to Tweet about what we’re interested in. In fact, just looking at the categories identified in Joe Murphy’s post, Tweets coming from @AAPOR and the highest frequency Tweeter (who I’ll leave unnamed, but I have circled in yellow) were generally categorized differently with the former much more often covering general conference information and the latter results from paper sessions.
While this is a quick analysis of the Twittersphere, these analyses can reveal characteristics of conversations and conversation participants that can potentially lead to other discoveries, such as the propensity for ideas to spread (or fade) among certain groups, identifying which individuals (and their behaviors) are key to group cohesiveness, and how we can eventually focus efforts to create a more robust environment for innovation, e.g., creating a hashtag to link people and ideas in the survey and public opinion research Twittersphere, much like market researchers use #MRX.