Using data from multiple sources with same features in classification problem Suppose that I am doing a classification problem where I classify people into two categories as bullied or not. In such type of research there are several datasets available such as; cyberbullying in students, cyberbulling in social media, cyberbulling in women etc. i.e. the problem is same (i.e. cyberbullying), but the test population is different.
What I currently do is using my features in each dataset and train models seperately for each dataset using 10-fold cross validation at get results.
However, since the underlying concept of these datasets are same (i.e. it tries to detect the bullied people), I am wondering if I can train machine learning model in one dataset using all its data points (say cyberbulling in students dataset), then test it on remaining datasets fully using its data points (say cyberbulling in social media, cyberbulling in women etc.) and get only the results of the tested datasets. However, I am not sure, what is the conclusion that I am trying to make through this experiment.
Is it like I am saying, I trained the model fully on student data, and applied it on social media and women. Does it show any generalisability of features? What are the conclusions that I can make through this?
 A: Since you wrote that you actually use the same features for all data sets it is of course possible treating them as one data set.
My suggestion would be joining them in one data set while introducing an identifier for the original source data set they came from, e.g. a categorical feature 'source' that indicates whether its from the women, social media, students data sets etc. .
The answer of possible generalization hasn't even been touched though, based on what you describe. For that you need to investigate the relationship between the new 'source' feature and the class, which can be approached in multiple ways.
A start to this could be training a classification model on the merged data set while leaving out the 'source' feature, then examine the resulting predictions with regard to the source of the instances.
Have a look how the classification performance is for the different data sources - if the source of the data does not matter and the concept of 'bullied or not' is similar in all source contexts, we'd assume that we observe no difference regarding the classification.   
