A dynamical measure of algorithmically infused visibility

TL;DR

This paper introduces a new way to measure how visible topics become on social media, especially when influenced by algorithms.

Contribution

A novel quantitative visibility measure that accounts for time spent at different ranks and tunable discrimination levels.

Findings

The measure effectively explains variability in accumulated views on Sina Weibo's Hot Search List.

The proposed visibility attributes capture the dynamics of algorithm-mediated communication settings.

The tunable discrimination level allows for flexible analysis of visibility across different contexts.

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 attributes of the visibility of a given topic in algorithm-mediated communication settings associated, for example, with social media. These attributes are: (i) the amount of time a topic spends at different ranks and (ii) the different ranks the topic attains. In addition, 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. The proposed measure is applied to the Hot Search List of the Chinese microblog Sina Weibo. Analysis of a large-scale, real-time dataset of topics of this list demonstrates that the proposed measure can explain a large share of the variability of the accumulated views of a topic.

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