Recommending Targeted Strangers from Whom to Solicit Information on Social Media

TL;DR

This paper introduces a system that predicts and recommends Twitter users likely to respond to information requests, enhancing targeted social media information collection.

Contribution

It develops a predictive model and recommendation algorithm based on social behavior features to identify responsive strangers on Twitter.

Findings

The system effectively predicts responders with high accuracy.

Real-world experiments show increased response rates.

The approach outperforms baseline methods.

Abstract

We present an intelligent, crowd-powered information collection system that automatically identifies and asks target-ed strangers on Twitter for desired information (e.g., cur-rent wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize ones willingness and readiness to respond based on their exhibited social behavior, including the content of their tweets and social interaction patterns. Second, we use the identified features to build a statistical model that predicts ones likelihood to respond to information solicitations. Third, we develop a recommendation algorithm that selects a set of targeted strangers using the probabilities computed by our statistical model with the goal to maximize the over-all response rate. Our experiments, including several in the real world, demonstrate the effectiveness of our work.