Just came across a Korean article titled ‘It takes 3,000 tweets to become a ‘public enemy’‘. In that article, a Korean social consulting firm Treum has looked into incidents of public furore in the country in recent years and argues that, as the title indicates, a company is likely to fall into a PR crisis if its mishap gets mentioned 3,000 times or more a day in the Twitter sphere.
Looking for an indicator of a social phenomenon from social media data is an increasingly popular method. Examples off the top of my head include:
‘Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact‘ (Gunther Eysenbach, 2011, Journal of Medical Internet Research 13(4): e123)
‘Petition Growth and Success Rates on the UK No. 10 Downing Street Website‘ (Scott A. Hale, Helen Zerlina Margetts & Taha Yasseri, 2013, Proceedings of the 5th Annual ACM Web Science Conference)
This paper uses a ‘big data’ approach to track the growth of over 8,000 petitions to the UK Government on the No. 10 Downing Street website for two years, analysing the rate of growth per day and testing the hypothesis that the distribution of daily change will be leptokurtic (rather than normal) as previous research on agenda setting would suggest. This hypothesis is confirmed, suggesting that Internet-based mobilisation is characterised by tipping points (or punctuated equilibria) and explaining some of the volatility in online collective action.
Speaking of tipping points, here’s another interesting piece of study that I have been meaning to blog about for some time.
The aforelinked article is a summary of the findings published in ‘Social consensus through the influence of committed minorities‘, Physical Review E, in 2011. In short, the authors of the paper have found that “when just 10 percent of the population holds an unshakable belief, their belief will always be adopted by the majority of the society”. They are also planning to further their study and question “how the percentage might change when input into a model where the society is polarized[: e.g.] Democrat versus Republican”.