Online movements spread explosively rather than diffusively

I’m very happy that a favourite!! paper of mine is finally published in EPJ Data Science. The paper that is titled “Rapid rise and decay in petition signing” tries to analyse and model the dynamics of popularity of online petitions.

Traditionally, collective action is known to follow a chain-reaction type of dynamics with a critical mass and a tipping point that could be all described with an S-shaped curve (schematically shown in Figure below), however, we spent about 3 years to only fail at finding any type of Sigmoid function that can fit our data!

Screen Shot 2017-10-02 at 19.17.22

The S-curve of success that is not relevant anymore!

Instead, we tried to a fit a multiplicative model with a strong decay modification. That was a much better fit to the data. It grows exponentially at the beginning, but then comes a very rapid decay in the novelty of the movement. Remember, our attention span is very short in the digital age!

Apart from the mathematical details of this fitting exercise, there are important consequences emerging from this observation:

  1. Online collective actions have very different dynamics to what we know from traditional offline movements.
  2. Online movements are explosive and much less predictable.
  3. The typical time-scale of such movements is in the range of hours and few days at longest, not weeks or years!
  4. This fast dynamic is independent of the extent of the success and prevalence of the movement.
  5. Instead of reaching a critical mass in later stages of a movement, one has to try to have a large initial momentum in order to success.

There is more to this obviously and if you’re interested, please have a look at the paper here.

The abstract of the paper reads:

Contemporary collective action, much of which involves social media and other Internet-based platforms, leaves a digital imprint which may be harvested to better understand the dynamics of mobilization. Petition signing is an example of collective action which has gained in popularity with rising use of social media and provides such data for the whole population of petition signatories for a given platform. This paper tracks the growth curves of all 20,000 petitions to the UK government petitions website (http://epetitions.direct.gov.uk) and 1,800 petitions to the US White House site (https://petitions.whitehouse.gov), analyzing the rate of growth and outreach mechanism. Previous research has suggested the importance of the first day to the ultimate success of a petition, but has not examined early growth within that day, made possible here through hourly resolution in the data. The analysis shows that the vast majority of petitions do not achieve any measure of success; over 99 percent fail to get the 10,000 signatures required for an official response and only 0.1 percent attain the 100,000 required for a parliamentary debate (0.7 percent in the US). We analyze the data through a multiplicative process model framework to explain the heterogeneous growth of signatures at the population level. We define and measure an average outreach factor for petitions and show that it decays very fast (reducing to 0.1% after 10 hours in the UK and 30 hours in the US). After a day or two, a petition’s fate is virtually set. The findings challenge conventional analyses of collective action from economics and political science, where the production function has been assumed to follow an S-shaped curve.

 

 

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Using Twitter data to study politics? Fine, but be careful!

The role of social media in shaping the new politics is undeniable. Therefore the volume of research on this topic, relying on the data that are produced by the same technologies, is ever increasing. And let’s be honest, when we say “social media” data, almost always we mean Twitter data!

Twitter is arguably the most studied and used source of data in the new field of Computational Political Science, even though in many countries Twitter is not the main player. But we all know why we use Twitter data in our studies and not for instance data mined from Facebook: Twitter data are (almost) publicly available whereas it’s (almost) impossible to collect any useful data from Facebook.

That is understandable. However, there are numerous issues with studies that are entirely relying on Twitter data.

In a mini-review paper titled “A Biased Review of Biases in Twitter Studies on Political Collective Action“, we discussed some of these issues. Only some of them and not all, and that’s why we called our paper a “biased review”.

The reason that I’m reminding you of the paper now is mostly the new surge of research on “politics and Twitter” in relation to the recent events in the UK, US, and the forthcoming elections in European countries this summer.

Here is the abstract:

In recent years researchers have gravitated to Twitter and other social media platforms as fertile ground for empirical analysis of social phenomena. Social media provides researchers access to trace data of interactions and discourse that once went unrecorded in the offline world. Researchers have sought to use these data to explain social phenomena both particular to social media and applicable to the broader social world. This paper offers a minireview of Twitter-based research on political crowd behavior. This literature offers insight into particular social phenomena on Twitter, but often fails to use standardized methods that permit interpretation beyond individual studies. Read more….

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Social Media: an illustration of overestimating the relevance of social media to social events from XKCD. Available online at http://xkcd.com/1239/