CLASSIFYING ONLINE USERS THROUGH MACHINE LEARNING AND INFORMATION-SEEKING PATTERNS

The internet is overflowing with unfiltered, impromptu, and continuous material from many sources due to technology in today's environment. In order to successfully deliver information depending on user intent, complex algorithms are created. Users' online experiences include a number of information-seeking behaviours, such as sharing, searching, and information verification. However, a thorough investigation of this complex user behaviour is still pending. This study helps to identify different types of users based on their online engagement, propose a user intent-machine learning model for classifying users based on their online search, share, and verification behaviour, and show that dynamic online interactions can be categorised based on their searching, sharing, and verifying behaviour. Participants from a wide range of age, gender, and vocational backgrounds complete a questionnaire designed to collect user input on online behaviour and practices. After a thorough feature engineering process, K-Mean clustering is used to discover user intent classes or profiles and their attributes based on the key features. To identify these classes, data is subsequently used to train a supervised learning Linear Discriminant Analysis Classifier (LDAC). With 80% accuracy, the suggested framework was able to predict the user intent class. A second user study collects data on users' dynamic interactions, which is used to test the model further. The user profiles that emerge from clustering are used by human raters to label the information search, exchange, and verify activity data after it has been converted to suit the model. While the model predicted the user with 67% accuracy, the study obtains an Inter-rater reliability (IRR) of 60%. According to this study, a user's motivation for looking for information, their propensity to share information on social media, and their propensity to believe information to be reliable can all help to understand their intentions, spot behavioural patterns, and identify intent through dynamic interactions that can be utilised for search engine optimisation and targeted marketing.