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copied!<p>This question is really about <strong>time-series analysis</strong>. Since you are interested in determining the cut-off point, a good place to start would be by reading up on <strong><a href="http://en.wikipedia.org/wiki/Control_chart" rel="nofollow noreferrer">Control Charts</a></strong>. If you want to delve deeper into the statistics (beyond control charts), then look into <a href="http://www.variation.com/cpa/tech/changepoint.html" rel="nofollow noreferrer">Change Point Analysis</a>, and also look up Structural changes in time-series. </p> <p><strong>Python Modules</strong>: To perform this analysis in Python, <em>NumPy</em> and <em>pandas</em> modules are relevant. This <a href="http://www.statalgo.com/2011/09/01/pandas-timeseries-and-dataframes-in-python/" rel="nofollow noreferrer">post in statalgo</a> will get you on the right track in terms of Python code. (If you are open to using R for your analysis, consider the CRAN packages <strong>tseries</strong> and <strong>strucchange</strong>.)</p> <p><strong>Relavant Question in SE (stats)</strong>: <a href="https://stats.stackexchange.com/questions/17623/how-to-detect-a-significant-change-in-time-series-data-due-to-a-policy-change">How to detect a change in time series data?</a></p> <p><strong>Pertinent Real life example</strong>: In the aftermath of Osama Bin Laden's death, there was a good deal of <a href="http://vortex.feinheit.ch/2011/10/18/patterns-information-spread-social-media/" rel="nofollow noreferrer">analysis done on how that piece of news spread</a> on Twitter. The article even has a section specifically related to your question about the <strong>stop</strong> of the spread of the news.</p> <p>Finally, you might also consider asking this in the <strong><a href="https://stats.stackexchange.com/">Stats SE site</a></strong>.</p> <p>Hope that helps.</p>

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