Society Media
Understanding of Algorithms Appears to Strengthen Trust in Mainstream Media – Not in Social Media
People who better understand how algorithms work seem more likely to trust mainstream media, whereas the same connection is not observed with social media. This is reported by a study published in the journal AI & SOCIETY, which examined how people's knowledge and attitudes are linked to media trust.
The study focused on two skills: algorithm literacy, which is the ability to comprehend how platforms' recommendation systems and other algorithms shape the content shown to users, and news literacy, which is the ability to assess the reliability and background of news. The researchers aimed to understand why trust in media arises and what factors could explain it, especially in a situation where news is increasingly consumed through various platforms.
The data was collected in China. The quantitative part was based on a survey of 780 adults, supplemented by follow-up interviews with 21 participants. Additionally, the study utilized the theory of planned behavior, which structures human actions through attitudes, social norms (the perception of what others consider acceptable), and perceived control (the feeling of being able to act in a situation).
The results indicated that both algorithm literacy and news literacy predicted trust in mainstream media, and particularly their combined effect was significant. From the perspective of the theory of planned behavior, algorithm literacy was associated with more positive attitudes, stronger perceived norms, and greater perceived control, which in turn were connected to trust in mainstream media.
In the case of social media, however, algorithm and news literacy were not explanatory factors for trust. The interviews revealed that negative attitudes, norms, and perceived lack of control towards social media could weaken trust – suggesting that different platforms operate under different logic when it comes to trust.
Source: Algorithm literacy as a moderator of media trust: a theory of planned behavior approach, AI & SOCIETY.
The study focused on two skills: algorithm literacy, which is the ability to comprehend how platforms' recommendation systems and other algorithms shape the content shown to users, and news literacy, which is the ability to assess the reliability and background of news. The researchers aimed to understand why trust in media arises and what factors could explain it, especially in a situation where news is increasingly consumed through various platforms.
The data was collected in China. The quantitative part was based on a survey of 780 adults, supplemented by follow-up interviews with 21 participants. Additionally, the study utilized the theory of planned behavior, which structures human actions through attitudes, social norms (the perception of what others consider acceptable), and perceived control (the feeling of being able to act in a situation).
The results indicated that both algorithm literacy and news literacy predicted trust in mainstream media, and particularly their combined effect was significant. From the perspective of the theory of planned behavior, algorithm literacy was associated with more positive attitudes, stronger perceived norms, and greater perceived control, which in turn were connected to trust in mainstream media.
In the case of social media, however, algorithm and news literacy were not explanatory factors for trust. The interviews revealed that negative attitudes, norms, and perceived lack of control towards social media could weaken trust – suggesting that different platforms operate under different logic when it comes to trust.
Source: Algorithm literacy as a moderator of media trust: a theory of planned behavior approach, AI & SOCIETY.
This text was generated with AI assistance and may contain errors. Please verify details from the original source.
Original research: Algorithm literacy as a moderator of media trust: a theory of planned behavior approach
Publisher: AI & SOCIETY
Authors: Chang Sup Park, Murtuza Murtuza
January 14, 2026
Read original →