As part of my research for The Voting Wars, I have been reading a great deal of computer science and related research on Twitter and politics. Some of the most interesting work is coming out of Indiana University’s School of Informatics and Computing.
The research on astrotweeting, though too tangential to my book, is worth flagging for readers. In this paper, the authors describe how political operatives set up fake accounts to spread rumors or false statements about candidates. This is done in a way to make it appear as though the information is coming from numerous sources, to convey the impression that the information is reliable. Here is a description of one such effort the researchers uncovered:
How Chris Coons budget works- uses tax $ 2 attend dinners and fashion shows
This is one of a set of truthy memes smearing Chris Coons, the Democratic candidate for U.S. Senate from Delaware. Looking at the injection points of these memes, we uncovered a network of about ten bot accounts. They inject thousands of tweets with links to posts from the freedomist.com Web site. To avoid detection by Twitter and increase visibility to different users, duplicate tweets are disguised by adding different hashtags and appending junk query parameters to the URLs. This works because many URL-shortening services ignore querystrings when processing redirect requests.
To generate retweeting cascades, the bots also coordinate mentioning a few popular users. These targets get the appearance of receiving the same news from several different people, and are more likely to think it is true, and spread it to their followers. Most of the bot accounts in this network can be traced back to a single person who runs the freedomist.com Web site. The diffusion network corresponding to this case is illustrated in Figure 7(D).
Here is the abstract for the paper:
Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that
leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations.