Regression when the value of one independent variable is dependent on another independent variable I need to predict the number of retweeets a tweet receives as a function of (1) whether there is a hyperlink within the tweet's text and (2) the position of the hyperlink within the tweet. If a tweet doesn't contain a hyperlink then the position of the hyperlink within the tweet is a null value. Example data for this situation would be:
numberOfRetweets     hyperlinkInTweet?     positionOfHyperlink
(dependent var)    (binary, 0=no, 1=yes)   (integer, 1 to 135)    
      5                      0                    null
     10                      1                     50
     35                      1                     12
      0                      0                    null
      0                      1                      5
      :                      :                      :

If a tweet doesn't contain a hyperlink (hyperlinkInTweet = 0) then it cannot have a position within the tweet text where the hyperlink begins (positions range from 1 to 135 inclusive). I've toyed with the idea of using zero in place of null, but I have reservations about doing this. Exploratory analysis of the data indicates that about half of the tweets contain a hyperlink.
I'm wondering if this is a nested regression problem, but would be grateful for a second opinion on the best course of action for this problem. Many thanks.     
 A: Since you have a count variable, we think about Poisson regression. But other count variable models could be substituted, such as negative binomial regression. The most important aspect is that these models have a multiplicative expectation structure, which is important if you have complications, such as number of retweets over different time periodsa, when time period length can enter as a new variable. 
I think you can substitute zero for your null values, in this context. Let us see what this means.  The poisson regression model is
$$  \DeclareMathOperator{\P}{Pr}
   \P(Y_i=y_i) = e^{-\lambda_i} \frac{\lambda_i^y}{y_i!}
$$  with $\lambda_i >0$ the parameter. For each observation $i$, we have the model
$\lambda_i = \exp(x_i^T \beta) = \exp(\eta_i)$ where $\eta_i$ is called the linear predictor. 
Here we have $\beta=(\beta_0, \beta_1, \beta_2)$ and $x=(1, x_1, x_2)$ where
$$
\begin{align}
    x_1 &= \text{$1$ if no hyperlink, $0$ if hyperlink}  \\
    x_2 &= \text{$0$ if no hyperlink, else length of hyperlink (or some non-linear function of length)}
\end{align}
$$
Then, for your example data (line 1,2,3) we get
$$
\begin{align}
\eta_1 &= \beta_0 +\beta_1 \\
\eta_2 &= \beta_0 + 50\beta_2 \\
\eta_3 &= \beta_0 + 12\beta_2 \\
\end{align}  $$
(Probably you will need some non-linear function of length, such as log. Some plot may tell you!).  This looks to me as an eminently sensible and interpretable model.
The parameter $\beta_1$ can be seen as a kind of "equivalent length of hyperlink" for the cases without a hyperlink.
