I have a big dataset containing almost 0.5 billions of tweets. I'm doing some research about how firms are engaged in activism and so far, I have labelled tweets which can be clustered in an activism category according to the presence of certain hashtags within the tweets.
Now, let's suppose firms are tweeting about an activism topic without inserting any hashtag in the tweet. My code won't categorized it and my idea was to run a SVM classifier with only one class.
This lead to the following question:

Is this solution data-scientifically feasible?
Does exists any other one-class classifier?
(Most important of all) Are there any other ways to find if a tweet is similar to the ensable of tweets containing activism hashtags?

Thanks in advance for your help!
Sam H has a great answer about using your dataset as-is, but I would strongly recommend annotating data so you have a few hundred negative examples, which should take less than an hour. Depending on how broad your definition of "activism" is that should be plenty to make a good classifier using standard methods.
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