measuring flip-flop behaviour across several topics
So I have a theory for this. We want to be able to distinguish users who are emotional and biased from those that are unemotional and unbiased. 
$$M = emotionality\\
B = Bias\\
t_i = tweet\ number\ i\\
b_i = bad\ words\ in\ t_i\\
p_i = good\ words\ in\ t_i\\
w_i = words\ in\ t_i\\
M = \frac{(b_i+p_i)}{w_i}\\
B = \frac{(b_i-p_i)^2}{w_i}\\
$$

M is the emotionality because it calculates the frequency that word is going to be an emotional one (instead of a descriptive one).

B is the bias because biased tweets will have more good (or bad) words than ones that hold both sides equally.

I don't know if this prediction will hold true, but i reckon if you plot each tweets x as bias and y as emotionality with a hue of good or bad, there will be two clusters (i am eager to be shown to be mistaken this certainly sounds like an interesting problem)!