We came across Navid Hassanpour’s research at Yale through his submission for a SXSW panel “Mapping Social Media Debates: India, Iran, Russia.” We were intrigued by looking and comparing the use of social media in these three countries. You can vote for Navid and his colleagues SXSW presentation here, and you can follow Navid on Twitter at @navidhassanpour.
1. For your SXSW panel, you’re looking at the the dynamics of social media exchange in relation to political activity in India, Iran and Russia. What were some of your more interesting findings?
I started from following the results of a daily phone poll among Iranians on the 2013 Iran election at IPOS in the days prior to the voting. There were candidates who were favorites, and those who caught up later on in the volatile game of Iranian electoral politics. Meanwhile I followed the Farsi election discussions on Twitter. In particular, I could see that some of the candidates had put resources into Twitter astroturfing, while others enjoyed a more organic attention, and all this was happening in a country in which Twitter is banned and blocked. Nevertheless, most of the dignitaries including the head of the state have active Twitter accounts.
I noticed that mere volume of tweeting does not tell you what is “trending” or is about to trend, instead what matters the most for the dynamics of proliferation is “how” the conversation is had. That is important because most of what we hear on sentiment analysis and similar topics are the dynamic frequency profile of tweets, not the type of conversation structure that develops through time. For example, Barack Obama tweeting to you as a follower, among 35 million others, will not have the same effect as one of your friends directly tweeting to you and starting a conversation on the same topic. The Tweet count can be the same.
2. How do you think social media has changed the politics for both leaders and citizens? For example, the Syrian Presidency now has its own account on Instagram.
The immediacy of engagement between leaders and their constituents on Twitter and alike is unprecedented and that introduces a house of new potentials. A lot of emphasis is put on the side of the constituents, for example how they mobilize and advertise on Twitter, but I think the leaders’ side is as interesting.
For example, during the Iranian election, as the competition became more and more fierce, it was fascinating to see how political figures, such as Rouhani and Aref, chose to actively tweet in a country where Twitter is blocked and banned. At the end I learned the results of the election from Rouhani’s Twitter feed a few hours ahead of the official announcement.
Here there is hope and a cautionary tale: I remember going through Medvedev’s Facebook account and seeing questions such as “the enemies of the great Russian nation are telling us that the election was rigged, is that true? what do you think?” and all of a sudden what you have is five thousand comments acting as a direct barometer of the electorate’s sentiments. I think this trend is going to continue, when you do not know the exact implications and stakes of Twitter mobilization, why not being a part of it? It would be the best insurance mechanism. Traditionally authorities know that, on the ground, blackshirts work effectively against grassroots mobilization. Now this leveling the playing field transforms censorship from its traditional mode of disruption to something more nuanced.
3. Part of your research revolved around censorship in social media. How have you found that citizens work their way around censorship and how do censors keep up with social media?
The situation in Iran where Twitter is banned verges on the level of bizarre. Social media is banned but then the leaders have Twitter accounts. In China censorship of online expression is outsourced to private entities, individuals who run a grassroots censorship campaign. The more the censors engage in tweeting and alike, the more important a study of the types of interaction on social media become. For example, it is timely to ask what might be potential patterns of manipulation in Twitter discussions? How is an astroturfed discussion different from one that grows rapidly by itself? Ironically these are the the types of questions that excite consumer marketers as much as electioneers.
4. What were some of your biggest takeaways working with Twitter data?
I work with a wonderful team of collaborators, Pablo Barbera and Joshua Tucker from NYU’s SMaPP (Social Media and Political Participation Lab), and Erik Borra from Digital Methods Initiative at University of Amsterdam, to extract patterns of conversation in Twitter data. To give you an example, what we can see in the Iran data is that network parameters of the conversation networks differ meaningfully from one candidate to another. For example, the discussion around Rouhani is more clustered than the others. For Rouhani himself, cliquishness of the discussion around him increased when he started to surge in the polls.
It’s obvious that the platform imposes a certain type of diffusion grammar. For example retweets always refer to the source tweet and the trail of retweets are lost in the data we collect from the API, and I think that also influences how users retweet.
I have noticed the network characteristics of conversation patterns we see can differentiate contrived discussions from viral grassroots discussions. The dynamics of these interaction parameters have been eye-opening. If there is manipulation, the network structure of the forced conversation would be different from something that is not forced and we would be able to detect that. Manipulators leave a trace–even in something as chaotic as Twitter where there is so much noise.
There is much talk about users and their attributes, but conversation structure itself is as important. If you know the structure of conversation around a candidate you can tell something meaningful about the potentials of the campaign going viral on Twitter–and hopefully on the ground.
A simple keyword frequency profile does not tell you about the swarming effect that defines Twitter–a tweet from CNN can generate millions of retweets, at the same time something simmering in many clusters for a while could also lead to a sudden breakout – that hidden simmering is what I really like about Twitter data and would like to understand better. In the process we learn more about similar historical dynamics that we have no access to, such as public opinion during a revolution in the past, or quick reversals of religion and identity.
5. What other research possibilities around social media and politics excite you?
Understanding the dynamic of “trending” is what excites me the most. This has potential applications to the study of markets as well as elections. I am not saying that Twitter data reflects reality perfectly, but what is exciting is that now we have an opportunity to understand mechanisms that we had no idea about before, just because there did not exist the right exploratory platform for observations.
Thanks for Navid for doing the interview! If you have any suggestions for our next Data Story, please let us know in the comments!
If you’re interested in more Data Stories, please check out some of our other interviews.