Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information

TL;DR

This paper introduces models to identify and engage Twitter users likely to retweet information within a specific timeframe, enhancing information spread by actively targeting the right strangers.

Contribution

It presents a novel recommender system combining social behavior analysis and wait-time prediction to improve targeted information propagation on Twitter.

Findings

The models accurately predict retweet likelihood within desired time frames.

Live studies show increased retweet engagement using the proposed system.

The approach effectively identifies suitable strangers for information dissemination.

Abstract

There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed two models: (i) a feature-based model that leverages peoples' exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.