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The Korean Society for Journalism & Communication Studies (KSJCS)
[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 68, No. 4, pp.31-74
ISSN: 2586-7369 (Online)
Print publication date 31 Aug 2024
Received 07 Jun 2024 Accepted 29 Jul 2024 Revised 02 Aug 2024
DOI: https://doi.org/10.20879/kjjcs.2024.68.4.002

아는 만큼 보인다? : 추천 알고리즘 이용자 태도에 미치는 인지된 지식, 실제 지식, 지식격차 요인의 영향력 탐색

이슬기** ; 김범수***
**부산대학교 미디어커뮤니케이션학과 조교수 sg.lee@pusan.ac.kr
***부산대학교 미디어커뮤니케이션학과 조교수 kbs0035@pusan.ac.kr
The More People Know, the More They See? : The Impact of Perceived and Actual Knowledge, and Knowledge Gap Factors on User Attitudes Toward Recommendation Algorithms
Slgi (Sage) Lee** ; Bumsoo Kim***
**Assistant Professor, Department of Media and Communication, Pusan National University sg.lee@pusan.ac.kr
***Assistant Professor, Department of Media and Communication, Pusan National University kbs0035@pusan.ac.kr

초록

추천 알고리즘은 겉으로 잘 드러나 있지는 않으나, 이용자의 정보환경을 구성하는 데 있어 지속적이고 상당한 영향을 미칠 수 있다. 따라서 미디어 이용자들이 이러한 추천 알고리즘을 올바르게 이해하고 인식하는 것은 매우 중요하다. 특히 추천 알고리즘이 초래할 수 있는 부정적 결과와 편향성을 정확히 인지하고, 이를 비판적으로 바라보는 태도는 추천 알고리즘의 민주적 활용에 있어 필수적이다. 이에 본 연구는 추천 알고리즘 태도에 영향을 미치는 요인에 대한 실증적 탐색을 수행하였다. 구체적으로, 추천 알고리즘에 대한 인지된 지식과 실제 지식이 세 종류의 알고리즘(엔터테인먼트 콘텐츠 추천, 뉴스 추천, 광고 추천)에 대한 태도에 미치는 영향을 살펴보았다. 이에 앞서, ‘지식격차’ 요인으로 제시된 성별, 연령, 소득 및 교육수준, 지역의 (a) 인구사회학적 요인과 (b) 미디어 이용 수준, (c) 디지털 리터러시 수준에 따라 추천 알고리즘에 대한 지식이 어떻게 형성되어 있으며, 이는 알고리즘 태도와 어떠한 관계가 있는지 살펴보았다. 이를 위해 전국 단위의 할당표집을 통해 성인 알고리즘 이용자 1,169명을 대상으로 설문을 시행하였다. 연구 결과, 첫째, 지식격차 요인 중 연령, 소득, 교육수준, 온라인미디어 이용, 디지털 리터러시에 따라 추천 알고리즘 지식에 유의미한 차이가 발견되었다. 즉, 연령이 낮고, 소득이 높으며, 온라인미디어 이용과 디지털 리터러시 수준이 높은 집단일수록 인지된 알고리즘 지식과 실제 알고리즘 지식이 모두 높은 경향을 보였다. 둘째, 알고리즘에 대한 인지된 지식과 실제 지식은 서로 정의 관계를 보였다. 그러나, 이들이 각각 알고리즘 태도에 미치는 영향은 상이했다. 인지된 지식만이 엔터테인먼트 추천 알고리즘 태도와 정적인 관계를 보였으며, 실제 지식은 어떤 종류의 알고리즘 태도와도 유의미한 관계를 보이지 않았다. 셋째, 지식 요인 외에 지식격차 요인 또한 알고리즘 태도에 유의미한 영향을 보였다. 구체적으로, 엔터테인먼트 콘텐츠 추천 알고리즘 태도에는 연령, 온라인미디어 이용, 디지털 리터러시가 각각 유의미한 관계를 보였으며, 뉴스 추천 알고리즘 태도에는 전통미디어와 온라인미디어 이용이 정적인 관계를 보였다. 반면, 광고 추천 알고리즘 태도에 영향을 미치는 지식격차 요인은 발견되지 않았다. 넷째, 세 종류의 추천 알고리즘 태도에 있어 공통적으로 높은 영향력을 보인 요인은 전통미디어 이용, 온라인미디어 이용, 디지털 리터러시였던 반면, 알고리즘 지식(인지된 지식 및 실제 지식)은 낮은 순위의 중요도를 차지했다. 이를 종합하면, 추천 알고리즘 태도 형성에 있어 미디어 이용과 디지털 리터러시가 가장 중요하였으며, 추천 알고리즘을 얼마만큼 이해하고 있는가에 대한 지식의 영향은 상대적으로 미미하다고 볼 수 있다. 또한, 추천 알고리즘 지식과 관련하여 연령, 소득, 교육수준, 디지털 리터러시 등의 요인에 의한 지식격차가 존재하였으나, 동일한 요인에 의한 격차가 알고리즘 태도에도 그대로 작용한다고 보기는 어려웠다. 추천 알고리즘 리터러시 교육과 관련한 함의를 논의하였다.

Abstract

Recommendation algorithms, while not immediately evident, can have a significant and lasting impact on users. Therefore, it is critical that media users recognize and comprehend these algorithms. Specifically, it is essential to accurately identify the potential negative outcomes and biases that recommendation algorithms may introduce, and to apply them with critical awareness. Such critical attitude is fundamental for the democratic use of recommendation algorithms. Hence, this study conducted an empirical analysis of the factors that shape people's attitudes toward recommendation algorithms. Specifically, we examined how perceived and actual knowledge about recommendation algorithms influence attitudes toward three types of recommendation algorithms—that are, entertainment content, news, and advertisements recommendation algorithms. We first investigated how two types of algorithm knowledge are influenced by (a) socio-demographic factors, (b) media use, and (c) digital literacy (collectively referred to as the "knowledge gap" factor), and examined how algorithm knowledge is associated with algorithm attitudes. A nation-wide survey of 1,169 adult algorithm users was conducted using quota sampling. The findings show that there were substantial disparities in levels of algorithm knowledge based on the knowledge gap factors such as age, income, education level, online media use, and digital literacy. Groups with younger individuals and those with higher levels of income, education, online media use, and digital literacy tend to have both higher perceived, and actual knowledge of recommendation algorithms. Second, perceived knowledge about recommendation algorithms had a positive relationship with actual knowledge. However, these two types of knowledge had different relationships with algorithm attitudes; that is, only perceived knowledge showed a positive relationship with entertainment-content algorithm, whereas actual knowledge did not show any significant relationship with any types of algorithm attitude. Third, there were several knowledge gap factors that significantly correlated with algorithm attitude. Age, online media use, and digital literacy were significantly related to attitudes toward entertainment-content recommendation algorithms, whereas traditional and online media use were positively related to news recommendation algorithm attitudes. On the other hand, no factors were found to influence attitudes toward advertisements recommendation algorithms. Fourth, across all three types of algorithms, traditional media use, online media use, and digital literacy were found to be significant factors influencing algorithm attitudes, while algorithm knowledge (both perceived and actual) was of lower importance. Overall, media use and digital literacy were the most critical factors in shaping attitudes toward recommendation algorithms, whereas algorithm knowledge had a minimal impact. Additionally, we found a "knowledge gap" in algorithm knowledge, in which people's level of algorithm knowledge varies significantly depending on their age, income, education level, and digital literacy. However, no identical pattern of gap was found in relation to the formation of algorithm attitudes. Implications for algorithm literacy education were discussed.

Keywords:

Recommendation Algorithm Attitude, Recommendation Algorithm Knowledge, Knowledge Gap Factors, Digital Divide

키워드:

추천 알고리즘 태도, 추천 알고리즘 지식, 지식격차 요인, 디지털(정보)격차

Acknowledgments

This work was supported by Pusan National University Research Grant, 2021(본 연구는 2021학년도 부산대학교 교내학술연구비(신임교수연구정착금)에 의한 연구임).

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