8 three.02 2.23 four.36 3.29 6.40 .82 two.Pr(jzj) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.07 0.8.eight two.50 two.42 0.60 0.44 0.70 0.53 0.50 .7 0.75 .29 0.42 0.6Note: While not shown right here, supply accounts (excluding `Alert
eight three.02 2.23 four.36 3.29 six.40 .82 two.Pr(jzj) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.07 0.eight.eight 2.50 two.42 0.60 0.44 0.70 0.53 0.50 .7 0.75 .29 0.42 0.6Note: Even though not shown here, supply accounts (excluding `Alert Boston’ for any baseline) are included as dummy variables to straight estimate fixed effects. Table three below shows these effects. Dispersion parameter: 2.07 (Theta .56) Null Deviance: 9398 on 697 degrees of freedom. Residual Deviance: 7802 on 664 degrees of freedom. AICc: 7876 p .05, p .00 doi:0.37journal.pone.034452.tPLOS One particular DOI:0.37journal.pone.034452 August two, Message Retransmission inside the Boston Marathon Bombing Responsemodel has been discussed in detail in prior sections. We also involve the logged quantity of incoming Followers on the sending account in the time each and every original message was posted; the Follower count is definitely an aspect of network structure that we predict to become connected with increasing message exposure, and therefore enhanced retweet prices. As shown in Table 2, incoming ties do indeed possess a constructive effect around the quantity of retweets per message (having a doubling inside the number of Followers growing the expected quantity of retweets by a issue of around 5.66). As noted above, we account for unChebulagic acid site observed heterogeneity among supply accounts that could have an effect on the dependent variable through senderlevel fixed effects. The reference organization here is the `AlertBoston’ account. (One account, `NWSBoston,’ showed too little posting activity during the period for its conditional imply to become reliably estimated, as reflected inside the massive typical error for its fixed impact within Table 3. We retain it here for completeness.) The negative binomial coefficients are interpreted as affecting the anticipated log count with the variety of retweets. For instance, a message containing emotion, judgment, or evaluative content increases the expected log count in the number of retweets by .29, i.e. increasing the expected retweet price by two.62 times when compared with a tweet that will not contain emotion, judgment, or evaluative content material (all else held continuous). To aid in interpretation of those effects (especially inside the context of various predictors), we locate it valuable to consider the predicted retweet count for various predictors interest, reported in percentages. To simplify interpretation, we describe effect sizes here with regards to the number of added retweets that could be gained or lost relative towards the baseline upon adding or removing a message function. Thus, a feature that multiplies the anticipated retweet price by a element of .5 is described as adding 50 more retweets, even though a function that multiplies the rate by a element of 0.75 is described as resulting in 25 fewer retweets. Impact sizes stated with regards to multipliers may well be discovered in Table 2. We talk about some of these variables presently as they correspond towards the primary question: what makes a distinction in the behavioral outcome of retweeting; message thematic content, style functions, or network exposure (Follower count) Very first, we address the extent to which thematic message content material affects the predicted variety of retweets in our observed data. These effects are summarized graphically in Fig . We find that messages containing hazard effect, advisory, or emotiveevaluative thematic content would be the strongest predictors of message retransmission. Messages that include content on hazard influence are predicted to PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/24134149 lead to, on typical, 22 much more (i.e further) retweets than t.
