Routledge Handbook of Digital Media and Communication

Abstract

This chapter focuses on data and algorithms and highlights a technical as well as historical, cultural, political and economic understanding of the 'datafied' and algorithmically constructed present. In one of the first critical assessments of the term big data in media and communication studies, boyd and Crawford define big data as “a cultural, technological, and scholarly phenomenon that rests on the interplay of: Technology, Analysis and Mythology”. In data-intensive environments such as social media, machine learning algorithms have become a standard way of learning to recognize patterns in the data, to discover knowledge, and to predict the likelihood of user actions and tastes. An important distinction needs to be made between algorithms that are pre-programmed and behave more or less deterministically and algorithms that have the ability to “learn” or improve in performance over time.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 79 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.