Mining Features Associated with Effective Tweets

TL;DR

This paper systematically analyzes 122 million tweet engagements to identify how features like timing, entities, and composition influence tweet effectiveness, revealing non-linear relationships and optimal feature usage.

Contribution

It provides a comprehensive review of tweet features and their complex, non-linear effects on engagement, informing practical applications like advertising and user engagement prediction.

Findings

Tweets with few hashtags are more effective than those with none or many.

The relationship between tweet features and effectiveness is non-linear.

Certain features significantly influence tweet engagement levels.

Abstract

What tweet features are associated with higher effectiveness in tweets? Through the mining of 122 million engagements of 2.5 million original tweets, we present a systematic review of tweet time, entities, composition, and user account features. We show that the relationship between various features and tweeting effectiveness is non-linear; for example, tweets that use a few hashtags have higher effectiveness than using no or too many hashtags. This research closely relates to various industrial applications that are based on tweet features, including the analysis of advertising campaigns, the prediction of user engagement, the extraction of signals for automated trading, etc.

Full text

See pages 1-last of TweetEffectiveness_ASONAM2017.pdf