Understanding the magnitude of the effect as expressed through the regression coefficient depends entirely on the scale on which the X variables are measured. Remember, the coefficient in a GLM model represents the change in Y for a one unit change in X. In this example, a number of the predictors are binary variables (those with the 'orNot' suffixes). Thus, for these variables, a one unit change represents moving from the minimum value (0) to the maximum value (1). Variables such as Followers and Followees are continuous variables, and thus a one unit change in these X variables means something very different as they range from a minimum of zero to a maximum of (possibly) several thousand.
Accordingly, it is not possible to directly compare the estimated coefficients for different variables in a GLM model without taking into account the scale on which those variables are measured.