The Election is done. Just the other day, we saw an analysis of twitter activities by Yudhanjaya, where he observed that few accounts shape the tone of the twitter LKA community.

In this post, I am taking a detailed look at the retweet network, further drilling into twitter community. The dataset is the retweets graph for twitter hashtags #GESL15 and #GenElecSL collected between 4-22 of august.The archive includes 14k tweets of which about 9k are retweets. There are 2480 tweets accounts have participated. I collect the data through hawkeys.info. The analysis is done using a set of R scripts.

Following are some interesting observations.

How does the community look like?

The graph shows a visualization of the community. Each vertex represents an account. The first dense chart shows an edge for each retweet and the second sparse graph only shows an edge if five or more retweets have happened between the two accounts. The size of each node shows the number of retweets the node has received.

The community is arranged around few accounts that act as hubs, and first 10 authors have received about 40% retweets all retweets. This confirms Yudhanjaya‘s observations.

Furthermore, both graphs are well connected. Even the sparse graph is fully connected. Often in political conversions, different groups tend to segregate and cross talk is minimal. However, that is not the case in the LKA twitter graph. Maybe the presence of journalists as hubs in the network have enabled cross talk between groups.

The first table below shows the 15 accounts that had most retweets and the second table shows vertex betweenness values. Vertex betweenness is a measure of each node’s ability to connect different parts of the network.

Four accounts appears on both measures, which further confirms their prominence.

What suggests a good reach?

Following two charts try to find any correlation between the number of tweets or the number of followers vs. retweets.

However, data does not show such behavior. There are several bots that generate lots of tweets, but they do not generate much retweets. Furthermore, the accounts that receive many retweets have only about 50-100 tweets ( about 4-5 per day). This is evidence supporting that it is content, not the network strcture drives retweets, although it is not concusive.

Also, the relationship between the number of followers and retweets also is not very clear. Although there are few account that have many followers and tweets, there are notable exceptions in the graph. Most likely this is caused by highly connected nature of the network, where followers are replaced by fast propergation through the network. Hence you can have lot of reach without having lot of followers.

What did they talk about?

The following picture shows an word cloud generated using all the tweets. However, there are not much surprises there.

In contrast, top retweets in each day provide a superb chronicle on what happens in each day over time. You can get a view closer to this by typing #GenElecSL into twitter search box.

Conclusion

Twitter network community for Sri Lankan general election 2015 has a very well connected retweets graph. Although few accounts shape the tone of the discussion, they seem to do a good job of enabling cross-communication between different groups. Reach, measured via retweets, seem to be independent of attributes like the frequency of tweets and the number of followers the account have. Finally, most retweeted tweets in each day seem to provide a useful chronicle of what happens in the election each day.

Note: It is worth noting that the community graph does not show the followers. Hence, the retweet can happens via another account as well (e.g. B retweets A’s message, and C having seen B’s retweet, he retweets A’s post. Then both C and B will have an edge from A in this graph. Therefore, these results does not discredit follower network in the community.