Why LinkedIn&#8217;s Feed Algorithm Needs a Revamp
Spam can make or mar our social media experience. A study by Nexgate found that spam on social networking sites was growing at a faster rate than comments. To this end, last week, LinkedIn released a blog titled, ‘Viral spam content detection at LinkedIn’, where the platform claimed that it uses proactive and reactive defences to combat spam and malicious content on its platform. The platform’s proactive defences consist of two types of classifiers that analyse specific spam categories and content types, using deep neural networks trained with TensorFlow. LinkedIn uses these techniques to detect early signals of potential spam content and takes appropriate actions such as filtering or conducting a manual human review. On the other hand, reactive defences employ a combination of predictive machine learning and heuristics, analysing member behaviour, content features, and interaction patterns to predict the probability of spam appearing as viral content. Your spam, not my spam For everyday users, spam content is as good as ‘unwanted’ content. However, Analytics India Magazine has learnt that LinkedIn defines spam content as regular content, which does not comply with LinkedIn’s policy. The platform’s spam detection algorithm does not flat the ‘unwanted’ content that pops up on the feed and any content which is generated originally, can not be considered spam on the site. However, LinkedIn is filled with content that users did not want in the first place. The reasoning behind this is that while the platform transitioned from a pure networking platform to a content consumption one, they missed out on adapting accordingly. When a user ‘connects’ with someone on the platform, it automatically follows that person. This becomes a real issue as this feature makes it difficult for someone to curate the feed after. And unless a user really wants to remain private on the platform and is very strict about only connecting with people it 100% knows, generally there tends to be many people in the network that the user has never met before. Hence, every time a connection likes, comments, or shares content, it ends up on the user’s feed which at times is spam. But, the LinkedIn algorithm considers this as ‘original’ content. Problem of attribution This then brings us to a somewhat personal issue that Analytics India Magazine faced with one of our articles. Linkedin claims that the platform is not designed for virality but on occasion, posts content which results in significant engagement in the form of likes, reactions, comments, and reshares in a short period of time could be considered viral. This is along the lines of what happened with one of our articles. A few weeks ago, an article by us went viral across the country, garnering thousands of reactions and catching the attention of virtually all media outlets. However, we encountered a complication as the article was shared on other social media platforms without being properly attributed to us. On LinkedIn, for example, hundreds of users shared the article, and although some of those posts generated three to four times the engagement of the original post, our authorship was never acknowledged. However, our source at Linkedin responded to this dilemma saying, “Linkedin cannot completely remove the copied content from the site because these will not be detected as spam.” Attributing the ownership of an article to a particular user is more difficult than it seems. Now, with the rise of LLMs, it becomes even harder to identify the first source of the content. (Just to compare, YouTube has had an automatic option preventing users from uploading videos with a copyright, for years now) What LinkedIn can Learn from Reddit Owing to its downvote feature, Reddit has an excellent feed when it comes to written content. The users have a certain amount of control over the type of content they want to read, similar to what can be seen in Quora– another platform that has a rather good recommendation algorithm for its feed. This ingredient of community suggestion is exactly what LinkedIn is missing. Reddit’s secret is that it lets you downvote content and reactions so that users can identify those that are at opposite ends of the community’s interest, those that are seen as great contributions, and of course, those that appear to be more controversial. It often makes the participation more interesting than the content itself. Even Twitter and Facebook have been struggling with their recommendation algorithm for a couple of months now. Users have reported seeing vulgar content on their feeds. However, these platforms were quick to react and correct this, something that LinkedIn is yet to achieve.
Every time a connection likes, comments, or shares content, it ends up on the user’s feed which at times is spam. But, the LinkedIn algorithm considers this as ‘original’ content.
Hence, every time a connection likes, comments, or shares content, it ends up on the user's feed which at times is spam. But, the LinkedIn algorithm considers this as ‘original’ content. 
["AI (Artificial Intelligence)", "Data Scientist", "Machine Learning"]
["Go", "machine learning", "programming_languages:R", "AI", "neural network", "TensorFlow", "Machine Learning", "Aim", "ai_frameworks:TensorFlow", "analytics", "Data Scientist", "R", "AI (Artificial Intelligence)"]
["AI", "machine learning", "neural network", "analytics", "Aim", "TensorFlow", "R", "Go", "ai_frameworks:TensorFlow", "programming_languages:R"]
Tech-savvy storyteller with a knack for uncovering AI's hidden gems and dodging its potential pitfalls. 'Navigating the world of tech', one story at a time.
You can reach me at: lokesh.choudhary@analyticsindiamag.com.