LinkedIn Tweaks Comment Rankings for Relevancy
LinkedIn is adjusting how comments are displayed in its feed as the platform looks to increase meaningful conversations and keep users engaged with professional content.
According to creator economy expert Lindsey Gamble, LinkedIn has introduced an update that ranks comments based on their relevance to individual users. The system considers signals including professional interests, connections and previous engagement activity when determining which responses to surface.
The company is also placing greater emphasis on timely and relevant discussions within the main feed, potentially giving comments a more prominent role in how users discover and interact with content.
The changes follow a reported increase in activity around LinkedIn posts. In its second-quarter performance update, LinkedIn said time spent in post comments rose 18% year over year, while overall content consumption increased 10%. The figures suggest that discussions attached to posts are becoming a more important part of the platform’s user experience.
Improving comment relevance could help turn that increased activity into more substantive interaction. Showing users responses that are more closely connected to their interests may make them more likely to read and contribute to discussions. The challenge is distinguishing genuine engagement from activity generated by automation. LinkedIn has been taking steps to address “engagement pods” – private groups focused on artificially bosting engagement – as well as artificial intelligence-generated spam and other forms of inauthentic activity, particularly in comment sections.
An analysis from AI detection startup Pangram Labs, cited in the report, found that 30% of comments across 57,000 public LinkedIn posts between April and June were entirely AI-generated. LinkedIn has not confirmed that figure, but the growth of automated content presents a potential complication as the platform seeks to increase comment activity. The latest ranking changes serve two purposes: making discussions more relevant to individual members while potentially reducing the visibility of low-value responses.
