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?

• Do these data sets share any relevant common features? Generally you can do some research into domain-adaptation and transfer-learning, as those cover the described scenario well. – deemel Dec 5 '19 at 8:12
• @deemel thank you for the comment. The objective of my dataset is same (i.e. they are all labelled using same conditions). However, the population is different (i.e. students, social media, women etc.). Since the objective is same, just wondering whether transfer learning is suited to me? – EmJ Dec 5 '19 at 11:06
• The populations might be different, but are they at least described with some common features? – deemel Dec 5 '19 at 11:49
• @deemel yes, I am using the same feature set in every dataset. My features are only linguistic features, so, they can be extracted easily using the text in each dataset. So, the same features are used in the datasets. Please let me know your thoughts. Thank you :) – EmJ Dec 5 '19 at 22:47

• I see, I think I get what you described in the answer now. However, I am not clear the conclusion that we make from this line 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. If the source does not matter and the results is mostly similar when doing it individually or merged, what does that mean? Looking forward to hearing from you. Thank you :) – EmJ Dec 6 '19 at 10:07