How can someone be persuaded to loan money to a random stranger on the internet? Does the requestor need to be polite or explain the impact the loan would have? Or perhaps they should demonstrate their credibility or use facts to help others understand their need. Does one of these strategies lead to success more than another?
Researchers at Georgia Tech wanted to find out and surprisingly, went to Reddit to find the answer.
To better understand how language and the structure of language can be used to persuade others, researchers from the Machine Learning Center at Georgia Tech (ML@GT) looked at requests made on r/Borrow, a subreddit where people who need money can request a temporary loan from fellow Reddit users.
After training their model on more than 48,000 examples from the subreddit, the team annotated 900 requests, noting the order of the content and rhetorical strategies used. This is the first natural language processing research conducted that takes content order into account.
“Most requests on r/Borrow follow a pattern where someone introduces themselves, they promise to pay you back, and then are polite by thanking you. We wanted to understand where it made sense to use each strategy within the request and what order of strategies led to the most success,” said Omar Shaikh, first-author of the paper and third-year undergraduate student in the Social and Language Technologies (SALT) lab and Polo Data Club.
To organize the dataset, the lab identified five core rhetorical strategies that 93 percent of people used when making requests. The strategies are concreteness (use of facts), reciprocity (assure users they will be paid back), impact (highlight impact of request), credibility (use of credentials to establish trust), and politeness.
Using machine learning techniques, the researchers analyzed triplets, or groups of three, of these strategies to find patterns across the requests. Requests that relied heavily on concreteness, but that ignored strategies like politeness, tended to be the least successful. These requests appeared to come off as too demanding to potential lenders. Meanwhile, requests that consistently used politeness and followed conversational norms were more likely to be loaned the funds.
“You can’t just say fact after fact after fact and not include any emotional appeal in your request. Being polite and expressing gratitude or positive human emotion generally led to borrowers being loaned the money or at least having their request looked at,” said Shaikh.
In the future, the lab has ideas for the many avenues that they’d like to explore. These include examining strategies in contexts that are less sensitive than borrowing money, like requests being made on the subreddit r/Random Acts of Pizza. They would also like to look into how comments from third-party users affect the success rate of a request.
Eventually, Shaikh would like to turn this research into a product that helps people avoid falling prey to certain marketing emails. For example, a piece of software would alert a user when an email is conveying big savings and employing a scarcity strategy, when in reality the consumer is not getting as good of a deal as the email suggests.
“Understanding what strategies are being used in communications can be used for both good and bad purposes. We hope that by better understanding the strategies we can help people identify dark patterns and make good decisions regarding their purchasing or lending behavior,” said Shaikh. This research was accepted into to the Findings of Empirical Methods in Natural Language Processing (EMNLP), a coinciding sister publication to the main EMNLP conference, both occurring online Nov. 16 through 20. For more information on ML@GT’s additional work at EMNLP, visit ML@GT at EMNLP
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