Impact of Recommendation Algorithms on Media Configuration

Abstract

This study examined the impact of recommendation algorithms in media configuration.
The rationale for this study stems from the growing importance of recommendation
algorithms in media configuration. This study was anchored on the filter bubble theory by
Eli Pariser (2011), which explains how personalised recommendation systems influence
the type of information users receive, potentially limiting their exposure to diverse
viewpoints. The researchers used a survey research design and collected data through
questionnaire, with the target population of 1,304,998 for residents of Egor, Oredo and
Ikpoba Okha and the sample size of 400, as determined by the Taro Yamane formula,
with an error margin of 0.05. The findings showed that there is a high level of exposure
and engagement with media content recommended by algorithms. Also, there is
skepticism about the effectiveness of these algorithms, but this is not so with the business
sector as recommendation systems benefit businesses by improving advertising and
marketing strategies. The researchers, therefore, recommend that users should be
educated to help them understand how algorithms shape their media consumption and
empower them to make informed choices. Platforms should implement measures to
prevent filter bubbles and echo chambers.