z-logo
open-access-imgOpen Access
Recommending targeted strangers from whom to solicit information on social media
Author(s) -
Jalal Mahmud,
Michelle X. Zhou,
Nimrod Megiddo,
Jeffrey Nichols,
Clemens Drews
Publication year - 2013
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2449396.2449403
Subject(s) - computer science , set (abstract data type) , social media , work (physics) , data science , world wide web , information retrieval , data mining , artificial intelligence , machine learning , engineering , mechanical engineering , programming language
We present an intelligent, crowd-powered information collection system that automatically identifies and asks targeted strangers on Twitter for desired information (e.g., current wait time at a nightclub). Our work includes three parts. First, we identify a set of features that characterize one's 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 one's 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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom