There is always a sampling frame.  You have a few options.  

* One option is to consider all possible people you could reach using your sampling method and identify this as your target population.  You could simply describe your sampling mechanism as a means of identifying your population.
* Another option is to identify all possible people you could reach using your sampling method and assume that this population is representative of some other target population of interest.  You could simply descrive your sampling mechanism as a means of identifying your population and then clearly state the target population of interest.  (You could assume just a parameter such as the mean is representative if not the entire population.)  
* Yet another approach would be to identify all possible people you could reach using your sampling method and use inverse-probability-weights to make this population representative of some other target population of interest (assuming the weights are known).  
* Lastly, you could consider all possible responses you would get in repeated experiments *using only the people in your sample.*  Your population is not a broader population of people, it is a population of repeated experiments on the same people.  This last approach is akin to flipping a coin.  The population is the collection of all possible flips of the coin and your data is a sample from this population.

This sort of issue happens quite often even in randomized clinical trials.  There is always the consideration of sampling bias $-$ that you are not sampling in a representative way from the target population you are interested in.