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There is reason to believe that an increasing proportion of the news consumers receive is not from news producers directly but is recirculated through social network sites and email by ordinary citizens. This may produce some fundamental changes in the information environment, but the data to examine this possibility have thus far been relatively limited. In the current paper, we examine the changing information environment by leveraging a body of data on the frequency of (a) views, and recirculations through (b) Twitter, (c) Facebook, and (d) email of New York Times stories. We expect that the distribution of sentiment (positive-negative) in news stories will shift in a positive direction as we move from (a) to (d), based in large part on the literatures on self-presentation and imagined audiences. Our findings support this expectation and have important implications for the information contexts increasingly shaping public opinion.

Enns et al. respond to recent work by Grant and Lebo and Lebo and Grant that raises a number of concerns with political scientists’ use of the general error correction model (GECM). While agreeing with the particular rules one should apply when using unit root data in the GECM, Enns et al. still advocate procedures that will lead researchers astray. Most especially, they fail to recognize the difficulty in interpreting the GECM’s “error correction coefficient.” Without being certain of the univariate properties of one’s data it is extremely difficult (or perhaps impossible) to know whether or not cointegration exists and error correction is occurring. We demonstrate the crucial differences for the GECM between having evidence of a unit root (from Dickey–Fuller tests) versus actually having a unit root. Looking at simulations and two applied examples we show how overblown findings of error correction await the uncareful researcher.