“Friendsourcing” information seeking by posting questions to a social networking site, like Twitter, is becoming increasingly common. However, unlike conventional search engines, friendsourced information seeking does not always return an answer. Examining over 85,000 information-seeking tweets from the public Twitter firehose, we found that only about a third received replies at all.
We created a Twitter agent that monitors the public feed for information-seeking questions, generates an answer via crowdsourced labor, and tweets the reply back to the original asker. This image shows an example:
Raters evaluating naturally occurring “friendsourced” answers to questions on Twitter and the answers generated via our crowdsourcing project rated them as being of equally high quality. Indeed, the people who received replies from our agent were generally quite pleased with the experience — over a third of them thanked our agent in some way, such as by favoriting the tweet or sending a reply message on Twitter.
Our work illustrates ways in which a “socially embedded search engine” can augment basic social network Q&A experiences.