A dynamical measure of algorithmically infused visibility

TL;DR

This paper introduces a new quantitative measure of visibility in algorithmically influenced social media, capturing how topics trend and rank over time, with applications across multiple disciplines.

Contribution

It proposes a novel, tunable measure of visibility based on rank duration and diversity, validated on large-scale social media data.

Findings

The measure explains a significant portion of view variability.

It effectively captures trending topic dynamics.

The measure is applicable across various social media platforms.

Abstract

This work focuses on the nature of visibility in societies where the behaviours of humans and algorithms influence each other - termed algorithmically infused societies. We propose a quantitative measure of visibility, with implications and applications to an array of disciplines including communication studies, political science, marketing, technology design, and social media analytics. The measure captures the basic characteristics of the visibility of a given topic, in algorithm/AI-mediated communication/social media settings. Topics, when trending, are ranked against each other, and the proposed measure combines the following two attributes of a topic: (i) the amount of time a topic spends at different ranks, and (ii) the different ranks the topic attains. The proposed measure incorporates a tunable parameter, termed the discrimination level, whose value determines the relative weights of the two attributes that contribute to visibility. Analysis of a large-scale, real-time dataset of trending topics, from one of the largest social media platforms, demonstrates that the proposed measure can explain a large share of the variability of the accumulated views of a topic.