Deciding what to display: maximizing the information value of social media

TL;DR

This paper proposes an attention economy-based method to select the most informative tweets by considering novelty and popularity, using the Huberman-Wu algorithm, validated on Twitter data over two months.

Contribution

It introduces a novel application of the Huberman-Wu algorithm to optimize tweet selection based on relevance and utility in social media environments.

Findings

The method effectively predicts tweet popularity.

Selected tweets maximize user engagement.

Validation on real Twitter data confirms approach effectiveness.

Abstract

In information-rich environments, the competition for users' attention leads to a flood of content from which people often find hard to sort out the most relevant and useful pieces. Using Twitter as a case study, we applied an attention economy solution to generate the most informative tweets for its users. By considering the novelty and popularity of tweets as objective measures of their relevance and utility, we used the Huberman-Wu algorithm to automatically select the ones that will receive the most attention in the next time interval. Their predicted popularity was confirmed by using Twitter data collected for a period of 2 months.