To follow or not to follow: a feature evaluation

Authors:
Yanan Zhu;Nazli Goharian
Affiliations:
Information Retrieval Lab, Department of Computer Science, Georgetown University, Washington DC, DC, USA;Information Retrieval Lab, Department of Computer Science, Georgetown University, washington DC, DC, USA
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

Citing 3
Cited 1

What is Twitter, a social network or a news media?

Proceedings of the 19th international conference on World wide web
An empirical study on learning to rank of tweets

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Topical semantics of twitter links

Proceedings of the fourth ACM international conference on Web search and data mining

A framework for detecting public health trends with Twitter

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

The features available in Twitter provide meaningful information that can be harvested to provide a ranked list of followees to each user. We hypothesize that retweet and mention features can be further enriched by incorporating both temporal and additional/indirect links from within user's community. Our empirical results provide insights into the effectiveness of each feature, and evaluate our proposed similarity measures in ranking the followees. Utilizing temporal information and indirect links improves the effectiveness of retweet and mention features in terms of nDCG.