Spamming Twitter for Fun and Profit.
Two winters ago I left a position as a system administrator that was paying pretty well and moved cross-country to a region with less jobs than where I moved from. Three months later, I was still unemployed, broke, and bored. I was talking to my good friend Japhy on IRC one day and he was explaining to me how the tf-idf algorithm works. For reasons involving boredom more than any other reason, I dreamed up an idea: I would write software that would take a given document and generate book suggestions based on its content.
I think that most programmers would agree with me that we put in longer hours on code when we’re not working for anybody. We don’t stop learning, either. To us, unemployment is a brief sprint of academia spent in our home office, the local coffee shop, or our parent’s house.
My imagination dreamed up this fairly straightforward process:
Take a given document and calculate tf-idf scores on all terms
Select X number of the highest scoring terms
Pass these high-scoring terms to an Amazon ItemSearch query
Receive a list of recommended books (with URLs) from Amazon
Spam on Twitter is becoming more of a problem due to the nature of @replies being very low barriers to entry. There are numerous measures to help block this but it’s still becoming a problem.