I'm working on a project where the goal is to predict sales of certain generic products. There are many features, but social media metrics are causing me currently some headache. Social media metrics are a decent indicator of success. I have data related to Twitter and Facebook. The count of 'likes' seems to be a quite simple but effective feature in this context. However, not all products are marketed on both platforms. ~50% of observations are present on both platforms. ~80% in Facebook and ~60% in Twitter. ~10% are not present on either platform.  

The VIF for both variables is >15. The pearson correlation coefficient is 0.68. If I put them into the same OLS regression model, Twitter has negative coefficient, while Facebook remains positive. In separate models both are positive and significant. 

**What I have tried:**
1) If I remove the NaN rows the model will work only on 50% of cases. 
2) If I filled the NaN values with 0 it creates a huge penalty for the products that are only present in Twitter, as the coefficient is negative due to multicollinearity.
3) Summing the values is not a solution either, because the Twitter follower numbers tend to be about twice bigger.

**What I have not tried:**
1) Use standard scaler and then sum the values?

**Current solution:**

What I have done so far is creating a new variables social_media_audience_size which is a a linear combination of the two features based on the univariate beta coefficients as multipliers. y = a*x_1  + b*x_2 a and b are the coefficient of Facebook likes and Twitter followers. However, I'm not sure if this is an appropriate solution to my problem. 

**Question:**

What could be a generally accepted way to address the specified issue? 

Also, does this problem have a name? I'm having hard time finding any sources dealing with this issue, but I don't see why this would be very unique problem. I would also be interested in any of academic references where this problem is addressed!