The algorithmic recommendations of social media platforms have influenced the content that users are exposed to to a certain extent, which may promote divisive content and lead to fierce confrontation between social groups. Here are several main ways: 1. **Emotion-driven content recommendation**: Algorithms often prioritize content that can trigger strong emotional reactions (such as anger, fear, surprise, etc.). This type of content is more likely to attract users' attention and interaction, thereby increasing platform engagement. However, this may also lead to emotional and polarized discussions, further exacerbating confrontation between groups. 2. **Echo chamber effect and filter bubbles**: Based on users' historical behavior, interests and preferences, algorithms will recommend similar views and content. This personalized recommendation makes it easier for users to fall into an "echo chamber" and only be exposed to information that is consistent with their own views. The lack of diverse perspectives increases the gap and misunderstanding between groups. 3. **Priority display of hot and controversial topics**: Algorithms tend to promote topics that have already received widespread attention or are controversial in order to increase the activity and discussion of the platform. This type of content is often confrontational and easily stimulates conflict and division between groups. 4. **Prioritization of high-interaction content**: Social media algorithms usually prioritize content that receives a large number of likes, shares, and comments. Since divisive content often triggers more interactions, the algorithm will further push such content, resulting in a wider spread and stronger influence. 5. **Algorithm optimization goals tend to be biased towards user engagement**: The main goal of the platform's algorithm is to increase user dwell time and interaction frequency, without necessarily focusing on the objectivity and neutrality of the content. This optimization strategy may inadvertently prompt the platform to promote more provocative and divisive content to keep users active. 6. **Personalized recommendations lack diversity**: Overly personalized recommendation systems may limit users' exposure to different perspectives and information, reinforce existing beliefs and prejudices, reduce opportunities for cross-group understanding and communication, and thus exacerbate confrontations between social groups. 7. **Viral transmission mechanism**: The content transmission of social media has viral characteristics, which can easily spread content with high sharing rates. Divisive content is more likely to be widely disseminated due to its characteristics of triggering strong emotions and discussions, further amplifying its influence. 8. **Using recommendation algorithms to manipulate content**: In some cases, malicious actors may use the platform's recommendation algorithm to manipulate public opinion and incite confrontation and conflict between groups by creating and promoting divisive content. **Countermeasures**: In order to reduce the negative impact of algorithmic recommendations, social media platforms can take the following measures: - **Diversified recommendations**: Increase the recommendation of different viewpoints and content to break the information cocoon. - **Emotional regulation**: Reduce the priority of overly emotional content and promote rational and constructive discussions. - **Transparency improvement**: Publicize the algorithmic recommendation mechanism so that users can understand the basis for content recommendations. - **User education**: Improve users' media literacy and enhance their ability to distinguish the authenticity of information and think from multiple perspectives. Through the above measures, social media platforms can reduce the spread of divisive content and promote harmony and understanding among social groups while improving user experience and engagement.