Forex news latest tweets plugin
We approached the analysis in two ways: first, we used ordinary least squares OLS in which each of the two paths was captured by a separate regression model. Second, we used three-stage least squares 3SLS to estimate both path coefficients in a single model simultaneously. The NYT articles captured by the word cloud contained a total of , unique words.
The expected sign of the regression coefficient is shown next to each path and is identical for both approaches OLS and 3SLS. The delay between media coverage and diversionary tweets is assumed to be shorter than the subsequent response of the media to the diversion. A recent quantitative analysis of more than , news articles in the NYT through a combination of machine learning and human judgment involving a sample of nearly judges has identified the New York Times as being quite close to the political center, with a slight liberal leaning which was found to be smaller than, e.
Similarly, ABC News is known to be favored by centrist voters without however being shunned by partisans on either side In the online news ecosystem, ABC combined with Yahoo! Specification of diversionary topics on Twitter is challenging a priori because potentially any topic, other than the Mueller investigation itself, could be recruited by the president as a diversionary effort. We addressed this problem in two ways. First, we conducted a targeted analysis in which the diversionary topics were stipulated a priori to be those that President Trump prefers to talk about, based on our analysis of his political position and rhetoric during the first 2 years of this presidency.
This analysis allowed for the possibility that Trump would divert by highlighting topics other than those that he consistently favors. Both analyses included a number of controls and robustness checks, such as randomization, sensitivity analyses, and the use of placebo keywords, to rule out artifactual explanations.
Both analyses were approached in two different ways. The first approach fitted two independent ordinary least squares OLS regression models that a predicted diversion from Mueller coverage and b captured a suppression of Mueller coverage in the media as a downstream consequence of diversion see Fig. The second approach used a three-stage least squares 3SLS regression model. In a 3SLS regression, multiple equations are estimated simultaneously; in our case there are two equations that capture diversion and suppression, respectively.
This approach is particularly suitable when phenomena may be reciprocally causal. The analysis also asked whether that diversion, if it is triggered, might in turn suppress media coverage of the Mueller investigation. Our choice of keywords was based on the following considerations. The president has also made China-related issues some of his main international policy topics e.
The only other countries mentioned more than once were Israel 9 , Iran 4 , Canada 3 , and Japan 3. The word cloud on the right of Fig. The Supplementary information reports a full exploration of the autocorrelation structure of the data Tables S2 — S5. All analyses included the relevant lags to model autocorrelations. The first model predicted diversion, represented by the number of times the three diversionary keywords appeared in tweets, from adverse media coverage on the same day and is shown in the first three columns of the table.
The table shows that this was indeed observed for all media coverage NYT, ABC, and the combination of the two formed by averaging their standardized coverage. The magnitude of the associations is illustrated in the top row of panels in Fig. Table 1 Predicting diversionary tweets CJI from threatening media coverage Russia-Mueller; columns 1—3 and predicting suppression from diversionary tweets columns 4—6. Full size table Fig. Full size image The second model, which predicted media coverage as a function of the number of diversionary tweets on the previous day, is shown in the rightmost three columns in Table 1.
The table shows that threatening media coverage was negatively associated with diversionary tweets. The magnitude of the association is illustrated in the bottom row of panels in Fig. Table 1 provides an existence proof for the relationships of interest. The Supplementary information reports an additional, more nuanced set of analyses for different combinations and subsets of the three critical keywords Tables S7 — S These analyses generally confirm the overall pattern in Table 1.
One problematic aspect of this initial analysis is that artifactual explanations for the pattern cannot be ruled out. In particular, although one interpretation of these estimates is consistent with our hypotheses of 1 media coverage causing diversion and 2 the diversion in turn suppressing media coverage, the available data do not permit an unequivocal interpretation. Specifically, remaining endogeneity concerns measurement error, reverse causality, and omitted variables threaten a pure interpretation of these results as causal.
We address each of those concerns in turn, and the associated conclusions suggest endogeneity would be unlikely to fully explain away our findings. First, measurement error is unlikely to explain the relationships we found given that we draw from the universe of all NYT articles, ABC News segments, and Trump tweets in our sample period.
Even if we were to miss some articles, news segments, or tweets perhaps because our keywords did not fully catch all relevant articles or news segments , it is not clear how this would produce a systematic bias in one direction that could fundamentally influence our estimates. We support this judgment by displaying word clouds of all selected content e. If we systematically mismeasured the content of tweets and media coverage, the word clouds would reveal the error through intrusion of unexpected content or absence of content that would be expected to be present based on knowledge of the topic.
Similarly, there are reasons to believe that reverse causality cannot fully account for our results. Given the lag time of news reports, even in the digital age, there is limited opportunity for a tweet to generate coverage within the same h period. The reverse, however, motivated our analysis, namely the hypothesis that when Trump is confronted with uncomfortable media coverage, he tweets about unrelated topics.
However, that would, if anything, work against us detecting suppression. However, we observed a statistically significant negative relationship, which is the opposite of what is expected under the hypothetical anticipation scenario. The negative association is, however, entirely consonant with our hypothesis that diversion may be effective and may reduce inconvenient media coverage. By contrast, we consider the hidden role of omitted variables to be the largest threat to causal identification.
In the absence of controlled experimentation or another empirical identification strategy suited to identify causality , one can never be certain that an effect is not caused by hidden omitted variables that interfere in the presumed causal path. This is an in-principle problem that no observational study can overcome with absolute certainty.
It is, however, possible to test whether omitted variables are likely to explain the observed pattern. Our first line of attack was to conduct a sensitivity analysis to obtain a robustness value for the diversion and suppression models involving average media coverage columns 3 and 6 in Table 1 The robustness value captures the minimum strength of association that any unobserved omitted variables must have with the variables in the model predictor and outcome to change the statistical conclusions.
The details of the sensitivity analysis are reported in the Supplementary information Fig. S1 and Table S6. The results further lend support to our hypothesis that adverse media coverage causes the president to engage in diversion, and that this diversion, in turn, causes the media to reduce that coverage, although endogeneity from potentially omitted variables remains less likely to be a concern for the diversion model than the suppression model see Fig.
S1 and Table S6 for detailed quantification. Table 2 reveals that the 3SLS results replicated the overall pattern of the OLS analysis, although the significance of the suppression is attenuated. A noteworthy aspect of our 3SLS analysis is that it used two ways to model the temporal offset between tweets and subsequent, potentially suppressed, media coverage. This parallels the OLS analyses from Table 1. The pattern is remarkably similar across both panels: There are strong and nearly-uniformly statistically significant coefficients of adverse media coverage predicting diversion.
Conversely, all coefficients for suppression are negative, although their level of significance is more heterogeneous. Table 2 Three-stage least squares 3SLS models to predict diversionary tweets CJI from threatening media coverage Russia-Mueller; columns 1—3 and predicting suppression from diversionary tweets columns 4—6 simultaneously. Full size table The 3SLS results further diminish the likelihood of an artifactual explanation: for omitted variables to explain the observed joint pattern of diversion and suppression, those confounders would have to simultaneously explain a positive association between two variables on the same day and a negative association from one day to the next across two different intervals—namely from yesterday to today as well as from today to tomorrow.
Moreover, those omitted variables would have to exert their effect in the presence of more than other control variables and a large number of lagged variables. We consider this possibility to be unlikely. Finally, to further explore whether the observed pattern of diversion and suppression was a specific response to harmful coverage, we conducted a parallel analysis using Brexit as a placebo topic.
Unlike Mueller, however, Brexit was not potentially harmful to the president—on the contrary, British campaigners to leave the European Union were linked to Trump and his team Table 3 shows the results of a model predicting diversionary tweets using the same three Twitter keywords but NYT coverage of Brexit as a predictor using 24 days of lagged variables as suggested by an analysis of autocorrelations. Figure 3 illustrates the content of the Brexit coverage and confirms that the topic does not touch on issues that are likely to be politically harmful to the president.
ABC News did not report on Brexit with sufficient frequency to permit analysis. Neither of the coefficients involving NYT are statistically significant, as one would expect for media coverage that is of no concern to the president. Table 3 Predicting diversionary tweets CJI from Brexit media coverage column 1 and predicting suppression from diversionary tweets column 2.
Size of each word corresponds to its frequency, as does font color. The darker the font, the greater the frequency. Within a SUR framework, the consequences of constraining individual parameters can be jointly estimated for the two models. That diversion, in turn, appears to be followed by suppression of the inconvenient coverage.
Because we have no experimental control over the data, this conclusion must be caveated by allowing for the possibility that the results instead reflect the operation of hidden variables. However, additional analyses to explore that possibility produce results to discount that possibility, at least for the diversion model. We acknowledge that the status of the suppression effect is less robust in statistical terms. The expanded analysis further buttresses our conclusion by showing its generality and robustness.
For each word pair, we modeled diversion as a function of Russia-Mueller media coverage and suppression of subsequent coverage either using two independent OLS models, or using a single 3SLS model for both equations simultaneously. Each data point represents a pair of words whose co-occurrence in tweets is predicted by media coverage position along X-axis and whose association with subsequent media coverage is also observed position along Y-axis. Each point thus represents a diversion and a suppression regression simultaneously.
The further to the right a point is located, the more frequently the corresponding word pair occurs on days with increasing Russia-Mueller coverage. The lower a point is located, the less Russia-Mueller is covered in the media on the following day as a function of increasing use of the corresponding word pair. If there is only diversion, then the point cloud should be shifted to the right. If there is diversion followed by suppression, then a notable share of the point cloud should fall into the bottom-right quadrant.
The top row of panels shows the results for the NYT panels a—c. The center d—f and bottom g—i row of panels show results for ABC News and the average of both media outlets, respectively. In each panel, the axes show jittered t-values of the regression coefficients for diversion X-axis and suppression Y-axis. Each point represents diversion and suppression for one pair of words in the Twitter vocabulary. The blue rugs represent univariate distributions.
Full size image Figure 4 shows that irrespective of how the data were analyzed OLS or two variants of 3SLS; columns of panels from left to right , in each instance a notable share of the point cloud sits outside the significance bounds red lines in the bottom-right quadrant summarized further in the Supplementary Table S The results are remarkably similar across rows of panels, suggesting considerable synchronicity between the NYT top row and ABC center.
The synchronicity is further highlighted in the bottom row of panels, which show the data for the average of the standardized values of coverage in the NYT and ABC. To provide a chance-alone comparison, the figure also shows the results for the same set of regressions when the Twitter timeline is randomized for each word pair.
The contrast between the observed data and what would be expected from randomness alone is striking. To illustrate the linguistic aspects of the observed pattern, the word cloud in Fig. The prominence of the keywords from our targeted analysis is immediately apparent. The Supplementary information presents additional quantitative information about those tweets Table S The pair structure is ignored in this representation. Full size image We performed two control analyses involving placebo keywords to explore whether the observed pattern of diversion and suppression in Fig.
The first control analysis involved NYT Brexit coverage ABC coverage was too infrequent for analysis, with only a single mention during the sampling period. The pattern for Brexit Fig. Although Brexit coverage stimulates Twitter activity by President Trump i. The keywords were chosen to span a variety of unrelated domains and were assumed not to be harmful or threatening to the president.
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